Update nexus: fix conflicts and sync local changes
This commit is contained in:
Shen Wei
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---
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title: AI一人公司赚钱方法 低成本项目 副业idea — 原始数据
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source:
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author: shenwei
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published:
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created:
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description:
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tags: []
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---
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# AI一人公司赚钱方法 低成本项目 副业idea — 原始数据
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**研究时间:** 2026-04-08
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**Date Range:** 2026-03-09 to 2026-04-08
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**Mode:** all
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||||
---
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||||
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||||
## X Posts
|
||||
|
||||
**X10** (score:91) @NFTCPS (2026-04-06) [299likes, 83rt]
|
||||
一个人，怎么做一家公司？OPC方法论！ 这本《一人企业OPC方法论》在GitHub上拿了14.5k⭐，把solo创业的完整框架写明白了。
|
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https://x.com/NFTCPS/status/2041081459809935841
|
||||
|
||||
**X4** (score:91) @MYohei707 (2026-04-06) [341likes, 52rt]
|
||||
现在这个AI时代，仿佛就是国内改革开放初期的翻版...
|
||||
1. 用豆包批量生成文字 2. 用即梦生成视频、播客、有声书 3. 上传到 YouTube、B站 4. 一旦爆款，几天赚几万，几个月赚几十万 5. 人在睡觉，钱一直进来
|
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https://x.com/MYohei707/status/2041009447175335984
|
||||
|
||||
**X1** (score:88) @NFTCPS (2026-04-06) [179likes, 35rt]
|
||||
用AI搞副业赚钱，这个仓库把能想到的路子都整理了。GitHub 1.4k⭐的AI副业赚钱手册，中英日韩多语言版本
|
||||
https://x.com/NFTCPS/status/2041083114475430202
|
||||
|
||||
**X6** (score:82) @jiroucaigou (2026-04-03) [145likes, 20rt]
|
||||
想用AI做副业搞钱没思路和渠道？来学习别人的成功案例...海外远程工作求职平台与资源导航 让你赚美元花人民币享受7倍快感
|
||||
https://x.com/jiroucaigou/status/2039899773243953393
|
||||
|
||||
**X5** (score:80) @imaxichuhai (2026-03-26) [259likes, 32rt]
|
||||
刷小红书发现个 AI 搞钱项目：金价涨跌提醒服务 有人靠这个赚了 4w+，最高一单卖 175 元
|
||||
https://x.com/imaxichuhai/status/2036995898245603532
|
||||
|
||||
**X14** (score:76) @trentjhughes (2026-03-17) [564likes, 37rt]
|
||||
Local business idea: AI agency for small businesses. Automate scheduling, customer service, social media. Charge $1,500-$3,000/month. 20 clients is $30,000-$60,000
|
||||
https://x.com/trentjhughes/status/2034039429497806929
|
||||
|
||||
**X2** (score:74) @huangyun_122 (2026-03-10) [571likes, 108rt]
|
||||
去年我就在研究 100 个 AI 小生意，零零散散写的帖子...以下开始🧵...
|
||||
https://x.com/huangyun_122/status/2031289301250748669
|
||||
|
||||
**X9** (score:71) @AYi_AInotes (2026-03-22) [94likes, 13rt]
|
||||
我们灵活就业人口突破 2 亿...本文基于6个月真实落地经验，完整呈现普通人从0到1跑通AI一人公司的分阶段路线图
|
||||
https://x.com/AYi_AInotes/status/2035514661999870383
|
||||
|
||||
**X12** (score:69) @cyrilXBT (2026-03-15) [293likes, 33rt]
|
||||
Nobody talks about these AI side hustles. But people are quietly making $5,000–$15,000 a month from them.
|
||||
https://x.com/cyrilXBT/status/2033241936379998470
|
||||
|
||||
**X13** (score:69) @NickDiFabio1 (2026-03-11) [440likes, 42rt]
|
||||
Publishing Ebooks with AI is the simplest side hustle for 2026. If you start now, you can make your first $10k+ by summer.
|
||||
https://x.com/NickDiFabio1/status/2031707286981296490
|
||||
|
||||
**X7** (score:68) @web3annie (2026-03-09) [317likes, 85rt]
|
||||
马斯克手把手教你成立：一人万亿市值公司！...第1周：注册Grok/Claude/OpenAI账号，学用Vapi或Retell建一个简单语音代理
|
||||
https://x.com/web3annie/status/2030829195866370220
|
||||
|
||||
**X15** (score:67) @KengGuangLong (2026-04-04)
|
||||
AI时代做副业，最难的不是技术门槛，而是选对赛道。核心逻辑是：AI放大的是你的『系统能力』，而不是单点技能
|
||||
https://x.com/KengGuangLong/status/2040374452899520926
|
||||
|
||||
**X8** (score:67) @L66778130091 (2026-03-17) [324likes, 12rt]
|
||||
我就让他去搞AI短剧、AI视频...
|
||||
https://x.com/L66778130091/status/2033946392297668757
|
||||
|
||||
**X3** (score:65) @wangharry (2026-03-12) [217likes, 30rt]
|
||||
别人抱怨学了AI没用，你就利用 AI + 地理套利赚钱
|
||||
https://x.com/wangharry/status/2031965912207602094
|
||||
|
||||
**X11** (score:61) @SuisPasDaVinci (2026-03-19) [102likes, 13rt]
|
||||
油管才是在小县城最好的赚钱方法
|
||||
https://x.com/SuisPasDaVinci/status/2034717411035795525
|
||||
|
||||
---
|
||||
|
||||
## YouTube Videos
|
||||
|
||||
**y_ON1Qbb274** (score:48) Chris Koerner on The Koerner Office Podcast (2026-03-29) [111,759 views, 3,400 likes]
|
||||
He Turned $400 Into $2.5M Using AI (No Coding)
|
||||
https://www.youtube.com/watch?v=y_ON1Qbb274
|
||||
|
||||
**w02c8EH2G9Y** (score:34) 澳洲Henry (2025-06-14) [309,951 views, 5,423 likes]
|
||||
1个人+AI，每天4小时，轻松年入百万，AI搞钱新思路
|
||||
https://www.youtube.com/watch?v=w02c8EH2G9Y
|
||||
|
||||
**uL-5dXgTvPU** (score:22) Ainthology (2025-11-02) [7,946 views, 288 likes]
|
||||
2026年用AI賺到100萬美金，10分鐘，教你死守這3個原則
|
||||
https://www.youtube.com/watch?v=uL-5dXgTvPU
|
||||
|
||||
**KiAwGTuMo_Q** (score:28) DAOJIE (2022-11-13) [61,503 views, 1,096 likes]
|
||||
8个轻资产网络创业项目（2023低投入高回报轻资产赚钱）
|
||||
https://www.youtube.com/watch?v=KiAwGTuMo_Q
|
||||
|
||||
**wUeZtNZglhE** (score:25) 李自然说 (2023-08-24) [25,862 views, 405 likes]
|
||||
【李自然说】美国领跑 AI SaaS 创业之路
|
||||
https://www.youtube.com/watch?v=wUeZtNZglhE
|
||||
|
||||
**RJKLuHjcdM0** (score:24) 小白新君Zoe (2022-03-15) [23,635 views, 286 likes]
|
||||
2022 个人创业可以做什么？5个极具发展潜力的低成本创业项目！
|
||||
https://www.youtube.com/watch?v=RJKLuHjcdM0
|
||||
|
||||
**2cc6Ck-yHPA** (score:23) up說創業 Business (2021-08-05) [16,824 views, 354 likes]
|
||||
在校大学生0成本创业月入6万？我学到了什么？
|
||||
https://www.youtube.com/watch?v=2cc6Ck-yHPA
|
||||
|
||||
**qGfV89-x650** (score:22) 然冉创业说 (2023-07-03) [16,651 views, 173 likes]
|
||||
最赚钱的4个风口赛道+10个细分项目，低成本，高利润
|
||||
https://www.youtube.com/watch?v=qGfV89-x650
|
||||
|
||||
**KAeim3bPZAs** (score:21) 哈公老师 - Ai价值创业 (2021-07-13) [6,718 views, 187 likes]
|
||||
网络赚钱2021 | 揭秘5种不用产品都能在家创业的方法
|
||||
https://www.youtube.com/watch?v=KAeim3bPZAs
|
||||
|
||||
**TA3DDg08JRQ** (score:20) 然冉创业说 (2022-06-10) [9,799 views, 111 likes]
|
||||
没钱怎么创业？4个步骤实现低成本创业赚钱
|
||||
https://www.youtube.com/watch?v=TA3DDg08JRQ
|
||||
|
||||
**ycySb--2EgU** (score:19) 然冉创业说 (2023-05-30) [4,676 views, 136 likes]
|
||||
一个知识博主到底有多赚钱？知识付费的风口怎么抓？
|
||||
https://www.youtube.com/watch?v=ycySb--2EgU
|
||||
|
||||
**68PDPNLP7rI** (score:18) 然冉创业说 (2023-06-05) [3,177 views, 91 likes]
|
||||
怎么判断一个赚钱idea是否靠谱？
|
||||
https://www.youtube.com/watch?v=68PDPNLP7rI
|
||||
|
||||
**-sGT-mT3ej8** (score:10) 暴富网赚项目分享 (2023-07-25) [698 views, 1 likes]
|
||||
互联网创业项目，美团圈圈一天收益2000+
|
||||
https://www.youtube.com/watch?v=-sGT-mT3ej8
|
||||
|
||||
**1Co9wc1Sfkg** (score:8) Mind Code Mastery 思維密碼 (2022-05-03) [287 views, 5 likes]
|
||||
REDBUBBLE 如何以低成本創業 | 高收益被動收入大公開
|
||||
https://www.youtube.com/watch?v=1Co9wc1Sfkg
|
||||
|
||||
**WEn29BcmKwA** (score:8) 谋事创业说 (2021-12-06) [147 views, 12 likes]
|
||||
揭秘暴利的剧本杀行业
|
||||
https://www.youtube.com/watch?v=WEn29BcmKwA
|
||||
|
||||
---
|
||||
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## 数据统计
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| 来源 | 数量 | 互动数据 |
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|------|------|---------|
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| X | 16 posts | 4,400+ likes, 500+ reposts |
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| YouTube | 19 videos | 555,000+ views |
|
||||
|
||||
## Top Voices (X)
|
||||
- @NFTCPS (AI OPC方法论)
|
||||
- @MYohei707 (AI内容工厂流水线)
|
||||
- @imaxichuhai (金价提醒服务)
|
||||
- @trentjhughes (AI Agency)
|
||||
- @NickDiFabio1 (AI Ebook出版)
|
||||
- @huangyun_122 (100个AI小生意)
|
||||
- @cyrilXBT ($5k-$15k副业清单)
|
||||
- @web3annie (AI万亿市值公司入门)
|
||||
- @wangharry (地理套利)
|
||||
---
|
||||
title: AI一人公司赚钱方法 低成本项目 副业idea — 原始数据
|
||||
source:
|
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author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# AI一人公司赚钱方法 低成本项目 副业idea — 原始数据
|
||||
**研究时间:** 2026-04-08
|
||||
**Date Range:** 2026-03-09 to 2026-04-08
|
||||
**Mode:** all
|
||||
|
||||
---
|
||||
|
||||
## X Posts
|
||||
|
||||
**X10** (score:91) @NFTCPS (2026-04-06) [299likes, 83rt]
|
||||
一个人，怎么做一家公司？OPC方法论！ 这本《一人企业OPC方法论》在GitHub上拿了14.5k⭐，把solo创业的完整框架写明白了。
|
||||
https://x.com/NFTCPS/status/2041081459809935841
|
||||
|
||||
**X4** (score:91) @MYohei707 (2026-04-06) [341likes, 52rt]
|
||||
现在这个AI时代，仿佛就是国内改革开放初期的翻版...
|
||||
1. 用豆包批量生成文字 2. 用即梦生成视频、播客、有声书 3. 上传到 YouTube、B站 4. 一旦爆款，几天赚几万，几个月赚几十万 5. 人在睡觉，钱一直进来
|
||||
https://x.com/MYohei707/status/2041009447175335984
|
||||
|
||||
**X1** (score:88) @NFTCPS (2026-04-06) [179likes, 35rt]
|
||||
用AI搞副业赚钱，这个仓库把能想到的路子都整理了。GitHub 1.4k⭐的AI副业赚钱手册，中英日韩多语言版本
|
||||
https://x.com/NFTCPS/status/2041083114475430202
|
||||
|
||||
**X6** (score:82) @jiroucaigou (2026-04-03) [145likes, 20rt]
|
||||
想用AI做副业搞钱没思路和渠道？来学习别人的成功案例...海外远程工作求职平台与资源导航 让你赚美元花人民币享受7倍快感
|
||||
https://x.com/jiroucaigou/status/2039899773243953393
|
||||
|
||||
**X5** (score:80) @imaxichuhai (2026-03-26) [259likes, 32rt]
|
||||
刷小红书发现个 AI 搞钱项目：金价涨跌提醒服务 有人靠这个赚了 4w+，最高一单卖 175 元
|
||||
https://x.com/imaxichuhai/status/2036995898245603532
|
||||
|
||||
**X14** (score:76) @trentjhughes (2026-03-17) [564likes, 37rt]
|
||||
Local business idea: AI agency for small businesses. Automate scheduling, customer service, social media. Charge $1,500-$3,000/month. 20 clients is $30,000-$60,000
|
||||
https://x.com/trentjhughes/status/2034039429497806929
|
||||
|
||||
**X2** (score:74) @huangyun_122 (2026-03-10) [571likes, 108rt]
|
||||
去年我就在研究 100 个 AI 小生意，零零散散写的帖子...以下开始🧵...
|
||||
https://x.com/huangyun_122/status/2031289301250748669
|
||||
|
||||
**X9** (score:71) @AYi_AInotes (2026-03-22) [94likes, 13rt]
|
||||
我们灵活就业人口突破 2 亿...本文基于6个月真实落地经验，完整呈现普通人从0到1跑通AI一人公司的分阶段路线图
|
||||
https://x.com/AYi_AInotes/status/2035514661999870383
|
||||
|
||||
**X12** (score:69) @cyrilXBT (2026-03-15) [293likes, 33rt]
|
||||
Nobody talks about these AI side hustles. But people are quietly making $5,000–$15,000 a month from them.
|
||||
https://x.com/cyrilXBT/status/2033241936379998470
|
||||
|
||||
**X13** (score:69) @NickDiFabio1 (2026-03-11) [440likes, 42rt]
|
||||
Publishing Ebooks with AI is the simplest side hustle for 2026. If you start now, you can make your first $10k+ by summer.
|
||||
https://x.com/NickDiFabio1/status/2031707286981296490
|
||||
|
||||
**X7** (score:68) @web3annie (2026-03-09) [317likes, 85rt]
|
||||
马斯克手把手教你成立：一人万亿市值公司！...第1周：注册Grok/Claude/OpenAI账号，学用Vapi或Retell建一个简单语音代理
|
||||
https://x.com/web3annie/status/2030829195866370220
|
||||
|
||||
**X15** (score:67) @KengGuangLong (2026-04-04)
|
||||
AI时代做副业，最难的不是技术门槛，而是选对赛道。核心逻辑是：AI放大的是你的『系统能力』，而不是单点技能
|
||||
https://x.com/KengGuangLong/status/2040374452899520926
|
||||
|
||||
**X8** (score:67) @L66778130091 (2026-03-17) [324likes, 12rt]
|
||||
我就让他去搞AI短剧、AI视频...
|
||||
https://x.com/L66778130091/status/2033946392297668757
|
||||
|
||||
**X3** (score:65) @wangharry (2026-03-12) [217likes, 30rt]
|
||||
别人抱怨学了AI没用，你就利用 AI + 地理套利赚钱
|
||||
https://x.com/wangharry/status/2031965912207602094
|
||||
|
||||
**X11** (score:61) @SuisPasDaVinci (2026-03-19) [102likes, 13rt]
|
||||
油管才是在小县城最好的赚钱方法
|
||||
https://x.com/SuisPasDaVinci/status/2034717411035795525
|
||||
|
||||
---
|
||||
|
||||
## YouTube Videos
|
||||
|
||||
**y_ON1Qbb274** (score:48) Chris Koerner on The Koerner Office Podcast (2026-03-29) [111,759 views, 3,400 likes]
|
||||
He Turned $400 Into $2.5M Using AI (No Coding)
|
||||
https://www.youtube.com/watch?v=y_ON1Qbb274
|
||||
|
||||
**w02c8EH2G9Y** (score:34) 澳洲Henry (2025-06-14) [309,951 views, 5,423 likes]
|
||||
1个人+AI，每天4小时，轻松年入百万，AI搞钱新思路
|
||||
https://www.youtube.com/watch?v=w02c8EH2G9Y
|
||||
|
||||
**uL-5dXgTvPU** (score:22) Ainthology (2025-11-02) [7,946 views, 288 likes]
|
||||
2026年用AI賺到100萬美金，10分鐘，教你死守這3個原則
|
||||
https://www.youtube.com/watch?v=uL-5dXgTvPU
|
||||
|
||||
**KiAwGTuMo_Q** (score:28) DAOJIE (2022-11-13) [61,503 views, 1,096 likes]
|
||||
8个轻资产网络创业项目（2023低投入高回报轻资产赚钱）
|
||||
https://www.youtube.com/watch?v=KiAwGTuMo_Q
|
||||
|
||||
**wUeZtNZglhE** (score:25) 李自然说 (2023-08-24) [25,862 views, 405 likes]
|
||||
【李自然说】美国领跑 AI SaaS 创业之路
|
||||
https://www.youtube.com/watch?v=wUeZtNZglhE
|
||||
|
||||
**RJKLuHjcdM0** (score:24) 小白新君Zoe (2022-03-15) [23,635 views, 286 likes]
|
||||
2022 个人创业可以做什么？5个极具发展潜力的低成本创业项目！
|
||||
https://www.youtube.com/watch?v=RJKLuHjcdM0
|
||||
|
||||
**2cc6Ck-yHPA** (score:23) up說創業 Business (2021-08-05) [16,824 views, 354 likes]
|
||||
在校大学生0成本创业月入6万？我学到了什么？
|
||||
https://www.youtube.com/watch?v=2cc6Ck-yHPA
|
||||
|
||||
**qGfV89-x650** (score:22) 然冉创业说 (2023-07-03) [16,651 views, 173 likes]
|
||||
最赚钱的4个风口赛道+10个细分项目，低成本，高利润
|
||||
https://www.youtube.com/watch?v=qGfV89-x650
|
||||
|
||||
**KAeim3bPZAs** (score:21) 哈公老师 - Ai价值创业 (2021-07-13) [6,718 views, 187 likes]
|
||||
网络赚钱2021 | 揭秘5种不用产品都能在家创业的方法
|
||||
https://www.youtube.com/watch?v=KAeim3bPZAs
|
||||
|
||||
**TA3DDg08JRQ** (score:20) 然冉创业说 (2022-06-10) [9,799 views, 111 likes]
|
||||
没钱怎么创业？4个步骤实现低成本创业赚钱
|
||||
https://www.youtube.com/watch?v=TA3DDg08JRQ
|
||||
|
||||
**ycySb--2EgU** (score:19) 然冉创业说 (2023-05-30) [4,676 views, 136 likes]
|
||||
一个知识博主到底有多赚钱？知识付费的风口怎么抓？
|
||||
https://www.youtube.com/watch?v=ycySb--2EgU
|
||||
|
||||
**68PDPNLP7rI** (score:18) 然冉创业说 (2023-06-05) [3,177 views, 91 likes]
|
||||
怎么判断一个赚钱idea是否靠谱？
|
||||
https://www.youtube.com/watch?v=68PDPNLP7rI
|
||||
|
||||
**-sGT-mT3ej8** (score:10) 暴富网赚项目分享 (2023-07-25) [698 views, 1 likes]
|
||||
互联网创业项目，美团圈圈一天收益2000+
|
||||
https://www.youtube.com/watch?v=-sGT-mT3ej8
|
||||
|
||||
**1Co9wc1Sfkg** (score:8) Mind Code Mastery 思維密碼 (2022-05-03) [287 views, 5 likes]
|
||||
REDBUBBLE 如何以低成本創業 | 高收益被動收入大公開
|
||||
https://www.youtube.com/watch?v=1Co9wc1Sfkg
|
||||
|
||||
**WEn29BcmKwA** (score:8) 谋事创业说 (2021-12-06) [147 views, 12 likes]
|
||||
揭秘暴利的剧本杀行业
|
||||
https://www.youtube.com/watch?v=WEn29BcmKwA
|
||||
|
||||
---
|
||||
|
||||
## 数据统计
|
||||
|
||||
| 来源 | 数量 | 互动数据 |
|
||||
|------|------|---------|
|
||||
| X | 16 posts | 4,400+ likes, 500+ reposts |
|
||||
| YouTube | 19 videos | 555,000+ views |
|
||||
|
||||
## Top Voices (X)
|
||||
- @NFTCPS (AI OPC方法论)
|
||||
- @MYohei707 (AI内容工厂流水线)
|
||||
- @imaxichuhai (金价提醒服务)
|
||||
- @trentjhughes (AI Agency)
|
||||
- @NickDiFabio1 (AI Ebook出版)
|
||||
- @huangyun_122 (100个AI小生意)
|
||||
- @cyrilXBT ($5k-$15k副业清单)
|
||||
- @web3annie (AI万亿市值公司入门)
|
||||
- @wangharry (地理套利)
|
||||
|
||||
@@ -1,193 +1,193 @@
|
||||
---
|
||||
title: 00后双非电商专业毕业，每周4场AI沙龙，我的能力从何而来？
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# 00后双非电商专业毕业，每周4场AI沙龙，我的能力从何而来？
|
||||
|
||||
> 作者：观自 (@longdechen12) - 认证账号
|
||||
> 来源：X (Twitter)
|
||||
> 发布时间：2026年3月25日 22:43
|
||||
> 链接：https://x.com/longdechen12/status/2036816359343222971
|
||||
> 收藏：11.9万 Views, 572 Likes, 1223 Bookmarks
|
||||
|
||||
---
|
||||
|
||||
说实话，我也是现学现卖。
|
||||
|
||||
但我有一套流水线，能让我三个小时以内摸透一个领域最核心的东西。
|
||||
|
||||
去年靠这套方法，录制了国家工信部 AI 智能体证书的课程，搭建了湘江集团整套的矩阵运营系统，近期六位数的费用刚到账。
|
||||
|
||||
今天把这套方法论完整公开。看完之后，你也能搭建属于你自己的内容变现流水线。
|
||||
|
||||
过去我也是受大家帮助走过来的，所以希望在稍微有经验的时候，能给大家带来一些启发和灵感。
|
||||
|
||||
如果你刚在 AI 领域起步，12 个月前的我就是你最佳的抄袭对象。
|
||||
|
||||
---
|
||||
|
||||
## 省流版工作流
|
||||
|
||||
**多平台信息源 → 全部转成 Markdown → 喂进 NotebookLM 知识库 → Claude Code 批量提问 + 保存答案到本地 → 基于问答创作自己的内容**
|
||||
|
||||
---
|
||||
|
||||
## 先看效果
|
||||
|
||||
我现在研究任何领域，准备每一场线下分享，直接和 Claude Code 对话。
|
||||
|
||||
它会基于两三百篇优质文章，或一个博主的全部视频，或某个关键词下的视频进行回答。所有回答都能完整保持真实性。
|
||||
|
||||
我基于这些回答去创作自己的内容，还能接各种 skill，直接转成文章、口播逐字稿、AI 工具最佳实践手册……可以无限延伸。
|
||||
|
||||
---
|
||||
|
||||
## 为什么大部分人没办法深入研究一个领域？
|
||||
|
||||
我观察下来，卡在三个地方：
|
||||
|
||||
### 第一，不会提问
|
||||
没有好问题，就不会有好答案。你连该问什么都不知道，AI 再强也帮不了你。
|
||||
|
||||
### 第二，答案不可信
|
||||
现在好多 AI 已经被投毒了，没办法基于完整的知识库生成内容，真实性无法保证。你拿着幻觉去讲课，是要翻车的。
|
||||
|
||||
### 第三，知识没有沉淀
|
||||
和 AI 在浏览器窗口对话，问题和答案没办法被留下来。下次需要调用，还得重新问一遍。更别说怎么基于这些问答去进一步扩展内容了。
|
||||
|
||||
**这三个问题，我的工作流全部解决。**
|
||||
|
||||
---
|
||||
|
||||
## 整体框架：IPO 模型（输入 → 处理 → 输出）
|
||||
|
||||
我用最简单的模型来拆——IPO：输入、处理、输出。每个普通人都能理解，每个普通人都能落地。
|
||||
|
||||
---
|
||||
|
||||
## 第一步：喂料——打破信息壁垒（耗时：0.5~1 小时）
|
||||
|
||||
大模型掌握了尽可能多的信息，但好多信息存在壁垒。平台之间的壁垒，付费与免费之间的壁垒，传播媒介之间的壁垒——有的是视频，有的是文章。
|
||||
|
||||
**怎么打破？** 我们以终为始，将所有来源尽可能转化为 AI 最擅长理解的 Markdown 文档。
|
||||
|
||||
### 网页类：三个插件解决一切
|
||||
|
||||
**油管 → YouTube to NotebookLM 插件**
|
||||
|
||||
这个插件支持一键导入一个油管博主的全部视频逐字稿到 NotebookLM 知识库，你可以把它理解为国内的 Get 笔记。导入后就能在知识库里对这个博主的全部视频进行提问，适合研究对标博主的整套内容体系。你也可以基于关键词搜索后依次导入，适合研究某个领域的优质内容。使用方法很简单，直接安装插件配置好即可。
|
||||
|
||||
**推特 / 公众号 / 网页 → Obsidian 剪藏助手**
|
||||
|
||||
推特长文、公众号文章、普通网页，都可以一键剪藏为本地 Markdown。你还可以搭建 RPA 程序进行批量剪藏——比如我要分析飞书的开发案例，搭建好 RPA 后把电脑放在那，它就能自己工作了。
|
||||
|
||||
**飞书文档 → Cloud Document Converter**
|
||||
|
||||
飞书文章容易被设置权限，Obsidian 剪藏助手搞不定。这个插件能帮你破除权限限制，直接以 Markdown 形式保存到本地。
|
||||
|
||||
基本上这三个插件就能解决所有网页文档的剪藏。
|
||||
|
||||
### 视频类：链接提取 or 文件提取，总能搞定
|
||||
|
||||
**链接提取：**
|
||||
B 站长视频、小宇宙播客等，直接把链接发给 Get 笔记，就能提取全部逐字稿。抖音也一样，Get 笔记支持一键提取抖音博主全部逐字稿，但都是在线的。我用 Claude Code 做了一个 app，支持一键将抖音博主全部视频的逐字稿导入本地。
|
||||
|
||||
**文件提取：**
|
||||
有些视频没有链接，或者链接不支持直接提取。最简单的方法是先用下载狗、GreenVideo 等工具把视频下载下来，再上传到通义听悟进行逐字提取。这是底层通用方案——所有视频归根结底都是文件，只要有源文件，一定能提取出来。
|
||||
|
||||
**这样就完成了喂料。**
|
||||
|
||||
---
|
||||
|
||||
## 第二步：消化——NotebookLM × Claude Code 联动（耗时：1~2 小时）
|
||||
|
||||
所有输入的内容都可以放到 NotebookLM 知识库里。
|
||||
|
||||
### 为什么选 NotebookLM？三个原因：
|
||||
|
||||
1. **绝不产生幻觉** - 完全基于你喂进去的真实材料生成内容，每个答案都能在原文中找到溯源，右上角标注好位置，点击就能跳转查看。
|
||||
|
||||
2. **接的是 Gemini 模型，长文本处理效果很好** - 好多 AI 你上传几十个文档它就不知道里边说的什么了，NotebookLM 可以上传 300 个文档。
|
||||
|
||||
3. **输出格式丰富** - 支持音频、视频、幻灯片等多种格式。
|
||||
|
||||
### 核心玩法：让 AI 帮你提问
|
||||
|
||||
处理环节最大的问题，还是前面说的——好多人不知道怎么提问。
|
||||
|
||||
**我的方法是：直接让 AI 帮我提出一系列好的问题。**
|
||||
|
||||
描述一个主题，让 AI 生成一系列问题，然后 NotebookLM 基于知识库给出答案，保存到本地。就像学术研究里的"交叉质询"——AI 提问，知识库举证，我来审判。
|
||||
|
||||
### 怎么实现的？
|
||||
|
||||
首先给 Claude Code 接上 NotebookLM 知识库。直接复制这个链接发给你的 Claude Code，让它帮你安装，按步骤完成认证登录即可。
|
||||
|
||||
> https://github.com/PleasePrompto/notebooklm-skill
|
||||
|
||||
安装好后，和网页端一样提问就行了。我日常的用法是：让它帮我批量生成问题，批量作答。我只需要看对应的问题和答案。它相较我而言能生成更全面更深入的问题。
|
||||
|
||||
我还增加了一个要求：不只是在线提问，而是把问题和答案以本地文件的形式保存下来，方便以后直接做成知识库。
|
||||
|
||||
### 为什么不直接用 Claude Code 读文档？
|
||||
|
||||
很多人问：Claude Code 也可以基于这些文档生成内容，为什么还要接 NotebookLM？
|
||||
|
||||
**核心原因是省钱。**
|
||||
|
||||
直接用 Claude Code 读几百篇文档，极其耗费 token。接上 NotebookLM 知识库后，Claude Code 只负责提问，拿到答案后保存到本地就行了，token 消耗大幅降低。
|
||||
|
||||
**这样处理环节就搞定了。**
|
||||
|
||||
每次我只需要提要求，它就帮我扩展问题、调用知识库、生成答案并保存到本地。我可以直接查阅，下次还能将其作为新的知识库材料。**知识是会复利的——你的库越厚，下次研究的起点就越高。**
|
||||
|
||||
---
|
||||
|
||||
## 第三步：表达——用 AI 做研究，用人做表达（耗时：1~2 小时）
|
||||
|
||||
拿到问题和答案后，实操方面的内容，我会直接去实践一遍。
|
||||
|
||||
如果要讲认知层面的内容，我用自己的理解整理成 Xmind 思维导图，再进一步整理为幻灯片。
|
||||
|
||||
但好多人是直接让 NotebookLM 生成幻灯片、生成逐字稿，然后去现场放在幻灯片注释里直接念。
|
||||
|
||||
**我不喜欢这种方式。** 真正体验过你就知道，这根本行不通。很死板，就是个念稿的，自己都不知道自己讲的是什么，而且当每页都是重点的时候，就没有重点了，根本不关心听众是否理解。
|
||||
|
||||
### 我的做法是：
|
||||
|
||||
1. **先写 Xmind 逐字稿，保证表达框架是我的** - 这样我不需要去顺应 AI 生成 PPT 的垃圾框架。
|
||||
|
||||
2. **用语音输入法（闪电说）或者语音转文字（千问录音）** - 照着思维导图，把我对内容的理解自己输出一遍。这个过程活人味很足，得到的是一篇完全符合我表达风格的逐字稿。稍微修改一下，到时候直接即兴讲就行。
|
||||
|
||||
3. **手搓 Keynote 幻灯片**
|
||||
|
||||
> 我一直有个观点：如果你觉得 NotebookLM 生成的幻灯片好的话，要么你压根不懂表达，要么你只用过 PowerPoint。
|
||||
|
||||
**手搓的好处：** 完全符合我的表达逻辑，手搓过程本身就是梳理内容的过程，同时能控制好观众的注意力，因为它足够简约，使用动画渐进式显现，观众的眼睛会完美契合你的演讲节奏。
|
||||
|
||||
有时间我也可以给大家分享一下我是如何制作幻灯片的。
|
||||
|
||||
---
|
||||
|
||||
## 闭环
|
||||
|
||||
从喂料到消化到表达，全套闭环就是这样。如果有想研究的领域，有想分享的内容，完全可以用这套工作流完成知识变现。
|
||||
|
||||
> **用 AI 做研究，用人做表达。**
|
||||
|
||||
这篇内容就是基于我 Xmind → 口播逐字稿的方法论写出来的。有些环节由于篇幅原因没有最细化，大家可以在评论区反馈：哪里写得好，哪里没写全，方便我以后做更详细的分享。
|
||||
|
||||
---
|
||||
|
||||
## 关于作者
|
||||
|
||||
> 我是观自，00 后，一个农村小伙，AI 领域的创业者，刚来到推特。
|
||||
> 专注于 AI 赋能培训与 AI 自动化运营系统定制。
|
||||
|
||||
---
|
||||
title: 00后双非电商专业毕业，每周4场AI沙龙，我的能力从何而来？
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# 00后双非电商专业毕业，每周4场AI沙龙，我的能力从何而来？
|
||||
|
||||
> 作者：观自 (@longdechen12) - 认证账号
|
||||
> 来源：X (Twitter)
|
||||
> 发布时间：2026年3月25日 22:43
|
||||
> 链接：https://x.com/longdechen12/status/2036816359343222971
|
||||
> 收藏：11.9万 Views, 572 Likes, 1223 Bookmarks
|
||||
|
||||
---
|
||||
|
||||
说实话，我也是现学现卖。
|
||||
|
||||
但我有一套流水线，能让我三个小时以内摸透一个领域最核心的东西。
|
||||
|
||||
去年靠这套方法，录制了国家工信部 AI 智能体证书的课程，搭建了湘江集团整套的矩阵运营系统，近期六位数的费用刚到账。
|
||||
|
||||
今天把这套方法论完整公开。看完之后，你也能搭建属于你自己的内容变现流水线。
|
||||
|
||||
过去我也是受大家帮助走过来的，所以希望在稍微有经验的时候，能给大家带来一些启发和灵感。
|
||||
|
||||
如果你刚在 AI 领域起步，12 个月前的我就是你最佳的抄袭对象。
|
||||
|
||||
---
|
||||
|
||||
## 省流版工作流
|
||||
|
||||
**多平台信息源 → 全部转成 Markdown → 喂进 NotebookLM 知识库 → Claude Code 批量提问 + 保存答案到本地 → 基于问答创作自己的内容**
|
||||
|
||||
---
|
||||
|
||||
## 先看效果
|
||||
|
||||
我现在研究任何领域，准备每一场线下分享，直接和 Claude Code 对话。
|
||||
|
||||
它会基于两三百篇优质文章，或一个博主的全部视频，或某个关键词下的视频进行回答。所有回答都能完整保持真实性。
|
||||
|
||||
我基于这些回答去创作自己的内容，还能接各种 skill，直接转成文章、口播逐字稿、AI 工具最佳实践手册……可以无限延伸。
|
||||
|
||||
---
|
||||
|
||||
## 为什么大部分人没办法深入研究一个领域？
|
||||
|
||||
我观察下来，卡在三个地方：
|
||||
|
||||
### 第一，不会提问
|
||||
没有好问题，就不会有好答案。你连该问什么都不知道，AI 再强也帮不了你。
|
||||
|
||||
### 第二，答案不可信
|
||||
现在好多 AI 已经被投毒了，没办法基于完整的知识库生成内容，真实性无法保证。你拿着幻觉去讲课，是要翻车的。
|
||||
|
||||
### 第三，知识没有沉淀
|
||||
和 AI 在浏览器窗口对话，问题和答案没办法被留下来。下次需要调用，还得重新问一遍。更别说怎么基于这些问答去进一步扩展内容了。
|
||||
|
||||
**这三个问题，我的工作流全部解决。**
|
||||
|
||||
---
|
||||
|
||||
## 整体框架：IPO 模型（输入 → 处理 → 输出）
|
||||
|
||||
我用最简单的模型来拆——IPO：输入、处理、输出。每个普通人都能理解，每个普通人都能落地。
|
||||
|
||||
---
|
||||
|
||||
## 第一步：喂料——打破信息壁垒（耗时：0.5~1 小时）
|
||||
|
||||
大模型掌握了尽可能多的信息，但好多信息存在壁垒。平台之间的壁垒，付费与免费之间的壁垒，传播媒介之间的壁垒——有的是视频，有的是文章。
|
||||
|
||||
**怎么打破？** 我们以终为始，将所有来源尽可能转化为 AI 最擅长理解的 Markdown 文档。
|
||||
|
||||
### 网页类：三个插件解决一切
|
||||
|
||||
**油管 → YouTube to NotebookLM 插件**
|
||||
|
||||
这个插件支持一键导入一个油管博主的全部视频逐字稿到 NotebookLM 知识库，你可以把它理解为国内的 Get 笔记。导入后就能在知识库里对这个博主的全部视频进行提问，适合研究对标博主的整套内容体系。你也可以基于关键词搜索后依次导入，适合研究某个领域的优质内容。使用方法很简单，直接安装插件配置好即可。
|
||||
|
||||
**推特 / 公众号 / 网页 → Obsidian 剪藏助手**
|
||||
|
||||
推特长文、公众号文章、普通网页，都可以一键剪藏为本地 Markdown。你还可以搭建 RPA 程序进行批量剪藏——比如我要分析飞书的开发案例，搭建好 RPA 后把电脑放在那，它就能自己工作了。
|
||||
|
||||
**飞书文档 → Cloud Document Converter**
|
||||
|
||||
飞书文章容易被设置权限，Obsidian 剪藏助手搞不定。这个插件能帮你破除权限限制，直接以 Markdown 形式保存到本地。
|
||||
|
||||
基本上这三个插件就能解决所有网页文档的剪藏。
|
||||
|
||||
### 视频类：链接提取 or 文件提取，总能搞定
|
||||
|
||||
**链接提取：**
|
||||
B 站长视频、小宇宙播客等，直接把链接发给 Get 笔记，就能提取全部逐字稿。抖音也一样，Get 笔记支持一键提取抖音博主全部逐字稿，但都是在线的。我用 Claude Code 做了一个 app，支持一键将抖音博主全部视频的逐字稿导入本地。
|
||||
|
||||
**文件提取：**
|
||||
有些视频没有链接，或者链接不支持直接提取。最简单的方法是先用下载狗、GreenVideo 等工具把视频下载下来，再上传到通义听悟进行逐字提取。这是底层通用方案——所有视频归根结底都是文件，只要有源文件，一定能提取出来。
|
||||
|
||||
**这样就完成了喂料。**
|
||||
|
||||
---
|
||||
|
||||
## 第二步：消化——NotebookLM × Claude Code 联动（耗时：1~2 小时）
|
||||
|
||||
所有输入的内容都可以放到 NotebookLM 知识库里。
|
||||
|
||||
### 为什么选 NotebookLM？三个原因：
|
||||
|
||||
1. **绝不产生幻觉** - 完全基于你喂进去的真实材料生成内容，每个答案都能在原文中找到溯源，右上角标注好位置，点击就能跳转查看。
|
||||
|
||||
2. **接的是 Gemini 模型，长文本处理效果很好** - 好多 AI 你上传几十个文档它就不知道里边说的什么了，NotebookLM 可以上传 300 个文档。
|
||||
|
||||
3. **输出格式丰富** - 支持音频、视频、幻灯片等多种格式。
|
||||
|
||||
### 核心玩法：让 AI 帮你提问
|
||||
|
||||
处理环节最大的问题，还是前面说的——好多人不知道怎么提问。
|
||||
|
||||
**我的方法是：直接让 AI 帮我提出一系列好的问题。**
|
||||
|
||||
描述一个主题，让 AI 生成一系列问题，然后 NotebookLM 基于知识库给出答案，保存到本地。就像学术研究里的"交叉质询"——AI 提问，知识库举证，我来审判。
|
||||
|
||||
### 怎么实现的？
|
||||
|
||||
首先给 Claude Code 接上 NotebookLM 知识库。直接复制这个链接发给你的 Claude Code，让它帮你安装，按步骤完成认证登录即可。
|
||||
|
||||
> https://github.com/PleasePrompto/notebooklm-skill
|
||||
|
||||
安装好后，和网页端一样提问就行了。我日常的用法是：让它帮我批量生成问题，批量作答。我只需要看对应的问题和答案。它相较我而言能生成更全面更深入的问题。
|
||||
|
||||
我还增加了一个要求：不只是在线提问，而是把问题和答案以本地文件的形式保存下来，方便以后直接做成知识库。
|
||||
|
||||
### 为什么不直接用 Claude Code 读文档？
|
||||
|
||||
很多人问：Claude Code 也可以基于这些文档生成内容，为什么还要接 NotebookLM？
|
||||
|
||||
**核心原因是省钱。**
|
||||
|
||||
直接用 Claude Code 读几百篇文档，极其耗费 token。接上 NotebookLM 知识库后，Claude Code 只负责提问，拿到答案后保存到本地就行了，token 消耗大幅降低。
|
||||
|
||||
**这样处理环节就搞定了。**
|
||||
|
||||
每次我只需要提要求，它就帮我扩展问题、调用知识库、生成答案并保存到本地。我可以直接查阅，下次还能将其作为新的知识库材料。**知识是会复利的——你的库越厚，下次研究的起点就越高。**
|
||||
|
||||
---
|
||||
|
||||
## 第三步：表达——用 AI 做研究，用人做表达（耗时：1~2 小时）
|
||||
|
||||
拿到问题和答案后，实操方面的内容，我会直接去实践一遍。
|
||||
|
||||
如果要讲认知层面的内容，我用自己的理解整理成 Xmind 思维导图，再进一步整理为幻灯片。
|
||||
|
||||
但好多人是直接让 NotebookLM 生成幻灯片、生成逐字稿，然后去现场放在幻灯片注释里直接念。
|
||||
|
||||
**我不喜欢这种方式。** 真正体验过你就知道，这根本行不通。很死板，就是个念稿的，自己都不知道自己讲的是什么，而且当每页都是重点的时候，就没有重点了，根本不关心听众是否理解。
|
||||
|
||||
### 我的做法是：
|
||||
|
||||
1. **先写 Xmind 逐字稿，保证表达框架是我的** - 这样我不需要去顺应 AI 生成 PPT 的垃圾框架。
|
||||
|
||||
2. **用语音输入法（闪电说）或者语音转文字（千问录音）** - 照着思维导图，把我对内容的理解自己输出一遍。这个过程活人味很足，得到的是一篇完全符合我表达风格的逐字稿。稍微修改一下，到时候直接即兴讲就行。
|
||||
|
||||
3. **手搓 Keynote 幻灯片**
|
||||
|
||||
> 我一直有个观点：如果你觉得 NotebookLM 生成的幻灯片好的话，要么你压根不懂表达，要么你只用过 PowerPoint。
|
||||
|
||||
**手搓的好处：** 完全符合我的表达逻辑，手搓过程本身就是梳理内容的过程，同时能控制好观众的注意力，因为它足够简约，使用动画渐进式显现，观众的眼睛会完美契合你的演讲节奏。
|
||||
|
||||
有时间我也可以给大家分享一下我是如何制作幻灯片的。
|
||||
|
||||
---
|
||||
|
||||
## 闭环
|
||||
|
||||
从喂料到消化到表达，全套闭环就是这样。如果有想研究的领域，有想分享的内容，完全可以用这套工作流完成知识变现。
|
||||
|
||||
> **用 AI 做研究，用人做表达。**
|
||||
|
||||
这篇内容就是基于我 Xmind → 口播逐字稿的方法论写出来的。有些环节由于篇幅原因没有最细化，大家可以在评论区反馈：哪里写得好，哪里没写全，方便我以后做更详细的分享。
|
||||
|
||||
---
|
||||
|
||||
## 关于作者
|
||||
|
||||
> 我是观自，00 后，一个农村小伙，AI 领域的创业者，刚来到推特。
|
||||
> 专注于 AI 赋能培训与 AI 自动化运营系统定制。
|
||||
|
||||
阅读过程中对于 AI 有任何问题，或者有插件方面的需求，都可以联系我。
|
||||
@@ -1,62 +1,62 @@
|
||||
---
|
||||
title: Bright Data MCP 技能
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# Bright Data MCP 技能
|
||||
|
||||
是的，Bright Data 有相关的技能，主要通过 MCP (Model Context Protocol) 的形式与 OpenClaw 集成。
|
||||
|
||||
这种集成允许 OpenClaw 调用 Bright Data 强大的数据采集能力，特别是其处理反爬、解锁复杂网站（如 Cloudflare 保护页面）的功能。
|
||||
|
||||
## 🤖 Bright Data MCP 技能
|
||||
|
||||
这个技能的核心是 **brightdata-mcp** 服务器。配置后，你可以通过自然语言指令让 OpenClaw 去爬取指定网站的数据，而无需自己编写复杂的爬虫代码或配置代理。
|
||||
|
||||
### 主要功能：
|
||||
|
||||
- **自动化爬虫**：将你的自然语言需求（如"抓取某电商网站的商品名称和价格"）转换为具体的爬取任务。
|
||||
- **反爬处理**：自动处理网站的反爬机制，如验证码和动态渲染，保证数据抓取的稳定性。
|
||||
- **结构化输出**：将抓取到的原始数据清洗并整理成结构化格式（如 JSON、CSV）。
|
||||
|
||||
---
|
||||
|
||||
## ⚙️ 如何配置
|
||||
|
||||
配置 brightdata-mcp 与配置其他 MCP 服务器类似，但需要提供你的 Bright Data API 凭证。
|
||||
|
||||
### 1. 获取 API Key
|
||||
|
||||
首先，你需要登录 Bright Data 账户，在后台获取你的 API Key 和 Account ID。
|
||||
|
||||
### 2. 添加 MCP 服务器
|
||||
|
||||
使用 openclaw 命令行工具添加 brightdata-mcp 服务：
|
||||
|
||||
```bash
|
||||
openclaw mcp add --transport stdio brightdata npx -y brightdata-mcp
|
||||
```
|
||||
|
||||
在某些配置中，可能需要通过环境变量传递凭证：
|
||||
|
||||
```bash
|
||||
openclaw mcp add --transport stdio brightdata npx -y brightdata-mcp
|
||||
```
|
||||
|
||||
然后，你可能需要手动编辑配置文件（~/.openclaw/openclaw.json），在 brightdata 服务的配置项下添加 env 字段来设置 BRIGHT_DATA_API_KEY 等环境变量。
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 安全提示
|
||||
|
||||
请注意，Bright Data 是一个功能强大的商业数据服务。在使用其技能时，务必遵守相关法律法规和网站的使用条款，合法合规地进行数据采集，切勿用于任何非法用途。
|
||||
|
||||
---
|
||||
|
||||
*记录时间：2026-03-25*
|
||||
---
|
||||
title: Bright Data MCP 技能
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# Bright Data MCP 技能
|
||||
|
||||
是的，Bright Data 有相关的技能，主要通过 MCP (Model Context Protocol) 的形式与 OpenClaw 集成。
|
||||
|
||||
这种集成允许 OpenClaw 调用 Bright Data 强大的数据采集能力，特别是其处理反爬、解锁复杂网站（如 Cloudflare 保护页面）的功能。
|
||||
|
||||
## 🤖 Bright Data MCP 技能
|
||||
|
||||
这个技能的核心是 **brightdata-mcp** 服务器。配置后，你可以通过自然语言指令让 OpenClaw 去爬取指定网站的数据，而无需自己编写复杂的爬虫代码或配置代理。
|
||||
|
||||
### 主要功能：
|
||||
|
||||
- **自动化爬虫**：将你的自然语言需求（如"抓取某电商网站的商品名称和价格"）转换为具体的爬取任务。
|
||||
- **反爬处理**：自动处理网站的反爬机制，如验证码和动态渲染，保证数据抓取的稳定性。
|
||||
- **结构化输出**：将抓取到的原始数据清洗并整理成结构化格式（如 JSON、CSV）。
|
||||
|
||||
---
|
||||
|
||||
## ⚙️ 如何配置
|
||||
|
||||
配置 brightdata-mcp 与配置其他 MCP 服务器类似，但需要提供你的 Bright Data API 凭证。
|
||||
|
||||
### 1. 获取 API Key
|
||||
|
||||
首先，你需要登录 Bright Data 账户，在后台获取你的 API Key 和 Account ID。
|
||||
|
||||
### 2. 添加 MCP 服务器
|
||||
|
||||
使用 openclaw 命令行工具添加 brightdata-mcp 服务：
|
||||
|
||||
```bash
|
||||
openclaw mcp add --transport stdio brightdata npx -y brightdata-mcp
|
||||
```
|
||||
|
||||
在某些配置中，可能需要通过环境变量传递凭证：
|
||||
|
||||
```bash
|
||||
openclaw mcp add --transport stdio brightdata npx -y brightdata-mcp
|
||||
```
|
||||
|
||||
然后，你可能需要手动编辑配置文件（~/.openclaw/openclaw.json），在 brightdata 服务的配置项下添加 env 字段来设置 BRIGHT_DATA_API_KEY 等环境变量。
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 安全提示
|
||||
|
||||
请注意，Bright Data 是一个功能强大的商业数据服务。在使用其技能时，务必遵守相关法律法规和网站的使用条款，合法合规地进行数据采集，切勿用于任何非法用途。
|
||||
|
||||
---
|
||||
|
||||
*记录时间：2026-03-25*
|
||||
*来源：星辉与比利的对话*
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,126 +1,126 @@
|
||||
---
|
||||
title:
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
I run 5 AI agents on Claude Code. Here's how I structure the CLAUDE.md and .claude/ directory to keep each one focused.
|
||||
|
||||
I've been building an ecosystem of AI agents using Claude Code and Preemptively Codex, each one is just a directory with a CLAUDE.md file and some config. After a lot of trial and error, I've landed on patterns that actually work. Figured I'd share what I've learned about structuring these files.
|
||||
|
||||
Each agent lives in its own directory under ~/Documents/:
|
||||
|
||||
~/Documents/
|
||||
├── planner/ # Executive function, routing, accountability
|
||||
├── content/ # Content pipeline
|
||||
├── youtube/ # YouTube production (scripting, SEO, metrics)
|
||||
├── life/ # Personal domains (health, finance, energy)
|
||||
└── control-center/ # Dashboard, database, API
|
||||
|
||||
Every agent follows the same template structure:
|
||||
|
||||
agent-name/
|
||||
├── CLAUDE.md # Identity + mission + capabilities
|
||||
├── .claude/
|
||||
│ ├── rules/ # Auto-loaded context (always-on)
|
||||
│ └── skills/ # On-demand workflows
|
||||
├── inbox/ # Input from other agents
|
||||
├── outputs/ # Generated output
|
||||
└── archive/ # Nothing gets deleted without archiving
|
||||
|
||||
The key insight: rules/ vs skills/
|
||||
|
||||
This is the thing that took me the longest to figure out.
|
||||
|
||||
.claude/rules/ files are loaded automatically at the start of every session. Claude reads them as part of its context window. This is where you put things the agent needs to know always — its scope, business context, how it should behave.
|
||||
|
||||
.claude/skills/ files are on-demand. They only load when you invoke them with /skill-name. This is where you put specific workflows like multi-step processes, templates, structured routines.
|
||||
|
||||
Why this matters: Rules files load into your context window at session start and stay there. Claude Code uses prompt caching so repeated content isn't billed at full price each turn, but large rules files still increase context pressure and can cause response degradation. You're carrying that weight every interaction whether you need it or not. With skills, only the name and description live in context by default; the full workflow loads on-demand, either when you call it or when Claude decides it's relevant. And this sent me down a rabbit hole into how token usage and context costs actually work.
|
||||
|
||||
• Rules (always-on): Scope boundaries, business context, routing logic, naming conventions — things that affect every decision
|
||||
Rules (always-on): Scope boundaries, business context, routing logic, naming conventions — things that affect every decision
|
||||
|
||||
• Skills (on-demand): Step-by-step workflows, templates, batch operations. Things you do occasionally
|
||||
(Note: skill descriptions are always in context so Claude knows what's available; only the full content is on-demand)
|
||||
Skills (on-demand): Step-by-step workflows, templates, batch operations. Things you do occasionally (Note: skill descriptions are always in context so Claude knows what's available; only the full content is on-demand)
|
||||
|
||||
I try to keep CLAUDE.md under 120 lines. It covers:
|
||||
|
||||
• Identity (2-3 lines): who this agent is and what it does
|
||||
Identity (2-3 lines): who this agent is and what it does
|
||||
|
||||
• Current phase (2-3 lines): what we're working on right now
|
||||
Current phase (2-3 lines): what we're working on right now
|
||||
|
||||
• Core capabilities (10-15 lines): what skills are available, what it can do
|
||||
Core capabilities (10-15 lines): what skills are available, what it can do
|
||||
|
||||
• Key locations (10-15 lines): file paths it needs to reference
|
||||
Key locations (10-15 lines): file paths it needs to reference
|
||||
|
||||
• What's been built (10-20 lines): history of completed work
|
||||
What's been built (10-20 lines): history of completed work
|
||||
|
||||
• What's next (5-10 lines): immediate priorities
|
||||
What's next (5-10 lines): immediate priorities
|
||||
|
||||
• Principles (5-10 lines): behavioral guardrails
|
||||
Principles (5-10 lines): behavioral guardrails
|
||||
|
||||
The biggest mistake I made early on was cramming everything into CLAUDE.md. It was 300+ lines and Claude's responses got worse because of context dilution. Splitting into rules/ files fixed that.
|
||||
|
||||
Example rules/ structure (my Planning Agent)
|
||||
|
||||
.claude/rules/
|
||||
├── 01-business-context.md # Revenue model, positioning, target customers
|
||||
├── 02-agent-ecosystem.md # All agents, their missions, how they connect
|
||||
├── 03-roadmap.md # Current phase, milestones, exit criteria
|
||||
├── 04-content-architecture.md # Content channels, pillars, workflow
|
||||
├── 05-daily-routine.md # Schedule, idea filtering, anti-distraction rules
|
||||
├── 07-godin-strategy.md # Marketing principles, milestone tracking
|
||||
├── 08-control-center.md # CLI tools reference, DB schema
|
||||
├── 98-end-of-session.md # Ritual: update roadmap, capture knowledge
|
||||
└── 99-content-capture.md # Auto-extract content signals from every session
|
||||
|
||||
The numbering is intentional, it controls load order and makes it easy to find things.
|
||||
|
||||
The agents don't call each other directly. They coordinate through:
|
||||
|
||||
• SQLite database: Source of truth for tasks, content pipeline state, sessions, metrics
|
||||
SQLite database: Source of truth for tasks, content pipeline state, sessions, metrics
|
||||
|
||||
• Inbox files: When one agent needs to hand context to another, it drops a markdown file in the target's inbox/
|
||||
Inbox files: When one agent needs to hand context to another, it drops a markdown file in the target's inbox/
|
||||
|
||||
• API endpoints: Dashboard reads/writes through a FastAPI backend
|
||||
API endpoints: Dashboard reads/writes through a FastAPI backend
|
||||
|
||||
Example: when I finish a build session, the planning agent captures content signals (what was built, what was learned) and drops them in content/inbox/. The content agent picks these up during its weekly batch and drafts social posts from them.
|
||||
|
||||
• Too much in CLAUDE.md: Split into rules/ files. CLAUDE.md is the summary, rules/ are the details.
|
||||
Too much in CLAUDE.md: Split into rules/ files. CLAUDE.md is the summary, rules/ are the details.
|
||||
|
||||
• No scope boundaries: Agents would try to do everything. Now every agent has a 00-scope.md rule that explicitly says what it does and does NOT do.
|
||||
No scope boundaries: Agents would try to do everything. Now every agent has a 00-scope.md rule that explicitly says what it does and does NOT do.
|
||||
|
||||
• No archiving: I deleted old files and lost context. Now everything goes to archive/ first.
|
||||
No archiving: I deleted old files and lost context. Now everything goes to archive/ first.
|
||||
|
||||
• Workflows in rules/: Moved to skills/ and token costs dropped noticeably.
|
||||
Workflows in rules/: Moved to skills/ and token costs dropped noticeably.
|
||||
|
||||
• No standard template: Every agent was structured differently. Created a standard template and refactored all agents to follow it. Consistency makes everything easier.
|
||||
No standard template: Every agent was structured differently. Created a standard template and refactored all agents to follow it. Consistency makes everything easier.
|
||||
|
||||
What I'd tell someone starting out
|
||||
|
||||
Start with one agent and one CLAUDE.md file. Don't build five agents on day one. Get one working well, understand the rules/ vs skills/ split, then create a second agent when you have a genuinely different domain.
|
||||
|
||||
The template structure above is what I'd start with for any new agent.
|
||||
|
||||
Anyone else running multiple Claude Code agents? What patterns have you found for keeping them organized?
|
||||
---
|
||||
title:
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
I run 5 AI agents on Claude Code. Here's how I structure the CLAUDE.md and .claude/ directory to keep each one focused.
|
||||
|
||||
I've been building an ecosystem of AI agents using Claude Code and Preemptively Codex, each one is just a directory with a CLAUDE.md file and some config. After a lot of trial and error, I've landed on patterns that actually work. Figured I'd share what I've learned about structuring these files.
|
||||
|
||||
Each agent lives in its own directory under ~/Documents/:
|
||||
|
||||
~/Documents/
|
||||
├── planner/ # Executive function, routing, accountability
|
||||
├── content/ # Content pipeline
|
||||
├── youtube/ # YouTube production (scripting, SEO, metrics)
|
||||
├── life/ # Personal domains (health, finance, energy)
|
||||
└── control-center/ # Dashboard, database, API
|
||||
|
||||
Every agent follows the same template structure:
|
||||
|
||||
agent-name/
|
||||
├── CLAUDE.md # Identity + mission + capabilities
|
||||
├── .claude/
|
||||
│ ├── rules/ # Auto-loaded context (always-on)
|
||||
│ └── skills/ # On-demand workflows
|
||||
├── inbox/ # Input from other agents
|
||||
├── outputs/ # Generated output
|
||||
└── archive/ # Nothing gets deleted without archiving
|
||||
|
||||
The key insight: rules/ vs skills/
|
||||
|
||||
This is the thing that took me the longest to figure out.
|
||||
|
||||
.claude/rules/ files are loaded automatically at the start of every session. Claude reads them as part of its context window. This is where you put things the agent needs to know always — its scope, business context, how it should behave.
|
||||
|
||||
.claude/skills/ files are on-demand. They only load when you invoke them with /skill-name. This is where you put specific workflows like multi-step processes, templates, structured routines.
|
||||
|
||||
Why this matters: Rules files load into your context window at session start and stay there. Claude Code uses prompt caching so repeated content isn't billed at full price each turn, but large rules files still increase context pressure and can cause response degradation. You're carrying that weight every interaction whether you need it or not. With skills, only the name and description live in context by default; the full workflow loads on-demand, either when you call it or when Claude decides it's relevant. And this sent me down a rabbit hole into how token usage and context costs actually work.
|
||||
|
||||
• Rules (always-on): Scope boundaries, business context, routing logic, naming conventions — things that affect every decision
|
||||
Rules (always-on): Scope boundaries, business context, routing logic, naming conventions — things that affect every decision
|
||||
|
||||
• Skills (on-demand): Step-by-step workflows, templates, batch operations. Things you do occasionally
|
||||
(Note: skill descriptions are always in context so Claude knows what's available; only the full content is on-demand)
|
||||
Skills (on-demand): Step-by-step workflows, templates, batch operations. Things you do occasionally (Note: skill descriptions are always in context so Claude knows what's available; only the full content is on-demand)
|
||||
|
||||
I try to keep CLAUDE.md under 120 lines. It covers:
|
||||
|
||||
• Identity (2-3 lines): who this agent is and what it does
|
||||
Identity (2-3 lines): who this agent is and what it does
|
||||
|
||||
• Current phase (2-3 lines): what we're working on right now
|
||||
Current phase (2-3 lines): what we're working on right now
|
||||
|
||||
• Core capabilities (10-15 lines): what skills are available, what it can do
|
||||
Core capabilities (10-15 lines): what skills are available, what it can do
|
||||
|
||||
• Key locations (10-15 lines): file paths it needs to reference
|
||||
Key locations (10-15 lines): file paths it needs to reference
|
||||
|
||||
• What's been built (10-20 lines): history of completed work
|
||||
What's been built (10-20 lines): history of completed work
|
||||
|
||||
• What's next (5-10 lines): immediate priorities
|
||||
What's next (5-10 lines): immediate priorities
|
||||
|
||||
• Principles (5-10 lines): behavioral guardrails
|
||||
Principles (5-10 lines): behavioral guardrails
|
||||
|
||||
The biggest mistake I made early on was cramming everything into CLAUDE.md. It was 300+ lines and Claude's responses got worse because of context dilution. Splitting into rules/ files fixed that.
|
||||
|
||||
Example rules/ structure (my Planning Agent)
|
||||
|
||||
.claude/rules/
|
||||
├── 01-business-context.md # Revenue model, positioning, target customers
|
||||
├── 02-agent-ecosystem.md # All agents, their missions, how they connect
|
||||
├── 03-roadmap.md # Current phase, milestones, exit criteria
|
||||
├── 04-content-architecture.md # Content channels, pillars, workflow
|
||||
├── 05-daily-routine.md # Schedule, idea filtering, anti-distraction rules
|
||||
├── 07-godin-strategy.md # Marketing principles, milestone tracking
|
||||
├── 08-control-center.md # CLI tools reference, DB schema
|
||||
├── 98-end-of-session.md # Ritual: update roadmap, capture knowledge
|
||||
└── 99-content-capture.md # Auto-extract content signals from every session
|
||||
|
||||
The numbering is intentional, it controls load order and makes it easy to find things.
|
||||
|
||||
The agents don't call each other directly. They coordinate through:
|
||||
|
||||
• SQLite database: Source of truth for tasks, content pipeline state, sessions, metrics
|
||||
SQLite database: Source of truth for tasks, content pipeline state, sessions, metrics
|
||||
|
||||
• Inbox files: When one agent needs to hand context to another, it drops a markdown file in the target's inbox/
|
||||
Inbox files: When one agent needs to hand context to another, it drops a markdown file in the target's inbox/
|
||||
|
||||
• API endpoints: Dashboard reads/writes through a FastAPI backend
|
||||
API endpoints: Dashboard reads/writes through a FastAPI backend
|
||||
|
||||
Example: when I finish a build session, the planning agent captures content signals (what was built, what was learned) and drops them in content/inbox/. The content agent picks these up during its weekly batch and drafts social posts from them.
|
||||
|
||||
• Too much in CLAUDE.md: Split into rules/ files. CLAUDE.md is the summary, rules/ are the details.
|
||||
Too much in CLAUDE.md: Split into rules/ files. CLAUDE.md is the summary, rules/ are the details.
|
||||
|
||||
• No scope boundaries: Agents would try to do everything. Now every agent has a 00-scope.md rule that explicitly says what it does and does NOT do.
|
||||
No scope boundaries: Agents would try to do everything. Now every agent has a 00-scope.md rule that explicitly says what it does and does NOT do.
|
||||
|
||||
• No archiving: I deleted old files and lost context. Now everything goes to archive/ first.
|
||||
No archiving: I deleted old files and lost context. Now everything goes to archive/ first.
|
||||
|
||||
• Workflows in rules/: Moved to skills/ and token costs dropped noticeably.
|
||||
Workflows in rules/: Moved to skills/ and token costs dropped noticeably.
|
||||
|
||||
• No standard template: Every agent was structured differently. Created a standard template and refactored all agents to follow it. Consistency makes everything easier.
|
||||
No standard template: Every agent was structured differently. Created a standard template and refactored all agents to follow it. Consistency makes everything easier.
|
||||
|
||||
What I'd tell someone starting out
|
||||
|
||||
Start with one agent and one CLAUDE.md file. Don't build five agents on day one. Get one working well, understand the rules/ vs skills/ split, then create a second agent when you have a genuinely different domain.
|
||||
|
||||
The template structure above is what I'd start with for any new agent.
|
||||
|
||||
Anyone else running multiple Claude Code agents? What patterns have you found for keeping them organized?
|
||||
|
||||
@@ -1,457 +1,457 @@
|
||||
---
|
||||
title: Product Manager Skills 完整使用指南
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# Product Manager Skills 完整使用指南
|
||||
|
||||
> 作者：沈盟 (程序员猫舍)
|
||||
> 来源：Telegra.ph
|
||||
> 发布时间：2026年3月27日
|
||||
> 链接：https://telegra.ph/Product-Manager-Skills-%E5%AE%8C%E6%95%B4%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8D%97-03-26
|
||||
|
||||
---
|
||||
|
||||
## 1. 总体概述
|
||||
|
||||
### 1.1 什么是 Product Manager Skills？
|
||||
|
||||
这是一套**产品管理框架的数字化封装**，包含 46 个独立但相互关联的技能（Skill），每个技能对应产品管理工作中的一个具体环节。
|
||||
|
||||
**核心理念**：
|
||||
> 用户不是买产品，而是"雇佣"产品来完成某项工作（Jobs-To-Be-Done）。
|
||||
|
||||
**设计目标**：
|
||||
- 📚 将经典产品管理方法论（JTBD、OST、精益 UX 等）转化为可执行的 AI 工作流
|
||||
- 🔗 打通产品发现→定义→交付→验证的完整链条
|
||||
- 🎯 避免"功能工厂"陷阱，确保团队解决正确的问题
|
||||
|
||||
### 1.2 与 JTBD 的关系
|
||||
|
||||
JTBD (Jobs-To-Be-Done) 是这套技能体系的**核心思想基础**：
|
||||
|
||||
```
|
||||
JTBD 理论 → 理解用户"雇佣"产品完成的工作
|
||||
↓
|
||||
产品发现 → 搞清楚用户真正的需求是什么
|
||||
↓
|
||||
产品定义 → 决定做什么来解决这些需求
|
||||
↓
|
||||
产品交付 → 把解决方案做出来
|
||||
↓
|
||||
产品验证 → 检查是否真的解决了问题
|
||||
```
|
||||
|
||||
这 46 个技能就是沿着这个链条展开的**工具箱**。
|
||||
|
||||
### 1.3 技能分类总览
|
||||
|
||||
46 个技能分为 5 大类：
|
||||
- 📊 用户研究类 (5 个)
|
||||
- 📈 市场分析类 (6 个)
|
||||
- 🎯 产品定义类 (8 个)
|
||||
- 📝 文档输出类 (6 个)
|
||||
- 📋 战略规划类 (7 个)
|
||||
- 🎓 高阶能力类 (14 个)
|
||||
|
||||
---
|
||||
|
||||
## 2. 技能全景图
|
||||
|
||||
### 2.1 产品管理工作流地图
|
||||
|
||||
```
|
||||
阶段 1: 产品发现 (Discovery)
|
||||
目标：搞清楚用户是谁？问题是什么？值不值得做？
|
||||
─────────────────────────────────────────────
|
||||
proto-persona → jobs-to-be-done → customer-journey-map
|
||||
初步画像 用户待办 用户旅程
|
||||
|
||||
↓ ↓ ↓
|
||||
|
||||
company-research → discovery-interview → tam-sam-som-calculator
|
||||
竞品调研 访谈准备 市场规模
|
||||
```
|
||||
|
||||
```
|
||||
阶段 2: 产品定义 (Definition)
|
||||
目标：搞清楚做什么？不做什么？优先级？
|
||||
─────────────────────────────────────────────
|
||||
problem-framing → opportunity-solution-tree → prioritization-advisor
|
||||
问题画布 机会方案树 优先级建议
|
||||
|
||||
↓ ↓ ↓
|
||||
|
||||
positioning-statement → epic-hypothesis → user-story-mapping
|
||||
定位陈述 史诗假设 故事地图
|
||||
```
|
||||
|
||||
```
|
||||
阶段 3: 产品交付 (Delivery)
|
||||
目标：怎么写文档？怎么拆任务？怎么验收？
|
||||
─────────────────────────────────────────────
|
||||
prd-development → user-story → storyboard
|
||||
需求文档 用户故事 故事板
|
||||
|
||||
↓ ↓ ↓
|
||||
|
||||
press-release → epic-breakdown → lean-ux-canvas
|
||||
新闻稿 史诗拆分 精益 UX
|
||||
```
|
||||
|
||||
```
|
||||
阶段 4: 产品验证 (Validation)
|
||||
目标：做得怎么样？要不要调整？下一步？
|
||||
─────────────────────────────────────────────
|
||||
business-health-diagnostic → feature-investment-advisor → roadmap-planning
|
||||
业务诊断 投资建议 路线图规划
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. 核心技能详解
|
||||
|
||||
### 3.1 jobs-to-be-done（用户待办任务）
|
||||
|
||||
**核心功能**：系统性地探索用户真正想完成的工作，而非表面需求。
|
||||
|
||||
**解决的问题**：
|
||||
- 用户说的"想要更快"到底是什么意思？
|
||||
- 为什么有些功能上线后数据没变化？
|
||||
- 如何避免做错功能？
|
||||
|
||||
**输出结构**：
|
||||
```markdown
|
||||
## Jobs-to-be-Done 分析
|
||||
|
||||
### 1. Customer Jobs（用户想完成的事）
|
||||
#### Functional Jobs（功能层面）
|
||||
#### Social Jobs（社会层面）
|
||||
#### Emotional Jobs（情感层面）
|
||||
|
||||
### 2. Pains（痛苦）
|
||||
#### Challenges（障碍）
|
||||
#### Costliness（代价）
|
||||
#### Common Mistakes（常见错误）
|
||||
#### Unresolved Problems（未解决问题）
|
||||
|
||||
### 3. Gains（收益）
|
||||
#### Expectations（期待）
|
||||
#### Savings（节省）
|
||||
#### Adoption Factors（采用因素）
|
||||
#### Life Improvement（生活改善）
|
||||
```
|
||||
|
||||
**使用示例**：
|
||||
- 场景：老板说"用户想要更快的开票速度"
|
||||
- 调用 jobs-to-be-done 后发现：
|
||||
- 表面需求："更快开票"
|
||||
- 真实 JTBD："从完工到收款的整个流程别耽误"
|
||||
- 真正机会：自动催款 + 收款跟踪，而不是"一键开票"按钮
|
||||
|
||||
---
|
||||
|
||||
### 3.2 opportunity-solution-tree（机会方案树）
|
||||
|
||||
**核心功能**：从模糊的需求到结构化的探索，避免"功能工厂"陷阱。
|
||||
|
||||
**解决的问题**：
|
||||
- 老板/客户直接说"我要这个功能"怎么办？
|
||||
- 如何确保团队在解决问题，而不是盲目堆功能？
|
||||
- 如何系统性地探索多种解决方案？
|
||||
|
||||
**输出结构**：
|
||||
```
|
||||
期望结果 (Outcome)
|
||||
│
|
||||
├── 机会 1 (问题/需求)
|
||||
│ ├── 方案 1 (实验)
|
||||
│ └── 方案 2 (实验)
|
||||
│
|
||||
├── 机会 2 (问题/需求)
|
||||
│ ├── 方案 1 (A/B测试)
|
||||
│ └── 方案 2 (可用性测试)
|
||||
│
|
||||
└── 机会 3 (问题/需求)
|
||||
└── ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 3.3 prd-development（产品需求文档）
|
||||
|
||||
**核心功能**：将发现阶段的洞察转化为工程可执行的 PRD 文档。
|
||||
|
||||
**输出结构**：
|
||||
```markdown
|
||||
# [功能名称] PRD
|
||||
|
||||
## 1. 执行摘要
|
||||
## 2. 问题陈述（谁？问题？证据？）
|
||||
## 3. 目标用户与画像
|
||||
## 4. 战略背景（业务目标、市场机会、竞争格局）
|
||||
## 5. 方案概述
|
||||
## 6. 成功指标
|
||||
## 7. 用户故事与需求
|
||||
## 8. 不在范围内
|
||||
## 9. 依赖与风险
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 3.4 prioritization-advisor（优先级建议）
|
||||
|
||||
**核心功能**：根据团队阶段和上下文，选择合适的优先级框架。
|
||||
|
||||
**推荐框架**：
|
||||
| 场景 | 推荐框架 |
|
||||
|------|----------|
|
||||
| 早期创业 | ICE |
|
||||
| 成长期 | RICE |
|
||||
| 企业/数据成熟 | WSJF |
|
||||
| 简单快速 | MoSCoW |
|
||||
|
||||
---
|
||||
|
||||
### 3.5 user-story（用户故事）
|
||||
|
||||
**输出格式**：
|
||||
```markdown
|
||||
### 用户故事 [ID]
|
||||
|
||||
#### 用例（Mike Cohn 格式）
|
||||
- **作为** [用户角色]
|
||||
- **我想要** [执行的动作]
|
||||
- **以便于** [期望的结果/价值]
|
||||
|
||||
#### 验收标准（Gherkin 格式）
|
||||
- **场景**: [场景描述]
|
||||
- **给定**: [前提条件]
|
||||
- **当**: [触发事件]
|
||||
- **那么**: [期望结果]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. 技能协作工作流
|
||||
|
||||
### 4.1 标准产品开发生命周期
|
||||
|
||||
```
|
||||
阶段 1: 发现 (Discovery)
|
||||
1. proto-persona → 定义目标用户
|
||||
2. jobs-to-be-done → 发现真实需求
|
||||
3. customer-journey-map → 绘制旅程
|
||||
4. tam-sam-som-calculator → 评估市场
|
||||
5. discovery-interview → 用户访谈
|
||||
|
||||
产出：用户洞察文档 + 机会评估报告
|
||||
|
||||
阶段 2: 定义 (Definition)
|
||||
6. problem-framing-canvas → 定义问题
|
||||
7. opportunity-solution-tree → 探索方案
|
||||
8. prioritization-advisor → 选择框架并排序
|
||||
9. positioning-statement → 产品定位
|
||||
|
||||
产出：优先级排序 + 产品定位文档
|
||||
|
||||
阶段 3: 交付 (Delivery)
|
||||
10. prd-development → 写 PRD
|
||||
11. user-story-mapping → 故事地图
|
||||
12. user-story → 写用户故事
|
||||
13. storyboard → 画故事板
|
||||
14. press-release → 写新闻稿
|
||||
|
||||
产出：PRD + 用户故事 + 故事板
|
||||
|
||||
阶段 4: 验证 (Validation)
|
||||
15. business-health-diagnostic → 上线后评估
|
||||
16. feature-investment-advisor → 决定下一步
|
||||
17. roadmap-planning → 更新路线图
|
||||
|
||||
产出：健康度报告 + 更新后的路线图
|
||||
```
|
||||
|
||||
### 4.2 技能调用依赖关系
|
||||
|
||||
```
|
||||
jobs-to-be-done
|
||||
↓
|
||||
problem-framing-canvas
|
||||
↓
|
||||
opportunity-solution-tree
|
||||
↓
|
||||
prioritization-advisor
|
||||
↓
|
||||
prd-development
|
||||
↓
|
||||
user-story-mapping
|
||||
↓
|
||||
user-story
|
||||
↓
|
||||
storyboard
|
||||
```
|
||||
|
||||
### 4.3 常见协作模式
|
||||
|
||||
| 模式 | 适用场景 | 技能链 |
|
||||
|------|----------|--------|
|
||||
| A: 新功能开发 | 完整流程 | proto-persona → jobs-to-be-done → OST → prioritization → PRD → user-story → storyboard |
|
||||
| B: 体验优化 | 聚焦旅程 | customer-journey-map → problem-framing → user-story-mapping → user-story → PRD |
|
||||
| C: 快速验证 | MVP 路线 | problem-framing → OST → lean-ux-canvas → press-release → user-story |
|
||||
| D: 季度规划 | 战略视角 | business-health-diagnostic → product-strategy-session → altitude-horizon → roadmap |
|
||||
|
||||
---
|
||||
|
||||
## 5. 完整案例演示
|
||||
|
||||
### 案例背景
|
||||
- **公司**：一款面向自由职业者的在线发票 SaaS
|
||||
- **问题**：用户流失率高，老板要求"提升用户体验"
|
||||
- **角色**：产品经理
|
||||
|
||||
### 第 1 步：理解用户（jobs-to-be-done）
|
||||
|
||||
**输入**：
|
||||
- 产品类型：在线发票工具
|
||||
- 目标用户：自由职业者（设计师、开发者、顾问）
|
||||
- 表面问题："用户说开票太麻烦"
|
||||
|
||||
**关键洞察**：
|
||||
> 用户说的"开票麻烦"，真实意思是"从完工到收款的整个流程太折腾"。真正的机会是**自动催款 + 收款跟踪**。
|
||||
|
||||
### 第 2 步：探索方案（opportunity-solution-tree）
|
||||
|
||||
**期望结果**：提升用户留存率 20%
|
||||
|
||||
**机会探索**：
|
||||
- 机会 1：用户忘记催款 → 方案：自动提醒
|
||||
- 机会 2：客户弄丢发票 → 方案：自助查询
|
||||
- 机会 3：对账麻烦 → 方案：自动对账
|
||||
|
||||
### 第 3 步：确定优先级（prioritization-advisor）
|
||||
|
||||
**推荐框架**：RICE
|
||||
|
||||
**评分结果**：
|
||||
1. 自动催款提醒 RICE = 45
|
||||
2. 发票自助查询 RICE = 32
|
||||
3. 自动对账 RICE = 28
|
||||
|
||||
### 第 4 步：写 PRD（prd-development）
|
||||
|
||||
产出完整的 PRD 文档，包含执行摘要、问题陈述、目标用户、成功指标、用户故事等。
|
||||
|
||||
### 第 5-7 步：拆故事 → 画故事板 → 验证结果
|
||||
|
||||
最终成果：
|
||||
- 收款周期缩短 36%
|
||||
- 续费率提升 18%
|
||||
- 用户满意度显著提升
|
||||
|
||||
---
|
||||
|
||||
## 6. 使用手册
|
||||
|
||||
### 6.1 安装与配置
|
||||
|
||||
- **位置**：~/.openclaw/workspace/skills/
|
||||
- **技能数量**：46 个
|
||||
- **占用空间**：约 2MB
|
||||
|
||||
### 6.2 如何调用技能
|
||||
|
||||
在 OpenClaw 会话中：
|
||||
- **方式 1**：直接提及技能名
|
||||
- "帮我用 jobs-to-be-done 分析一下发票工具的用户需求"
|
||||
- **方式 2**：描述任务，AI 自动匹配
|
||||
- **方式 3**：使用技能前缀
|
||||
- "/skills prd-development 我要写一个 PRD"
|
||||
|
||||
### 6.3 新手入门路径
|
||||
|
||||
**第 1 周：掌握核心 5 技能**
|
||||
- Day 1-2: jobs-to-be-done（理解用户）
|
||||
- Day 3-4: user-story（拆分需求）
|
||||
- Day 5: prd-development（写文档）
|
||||
- Day 6: prioritization-advisor（排优先级）
|
||||
- Day 7: 复习 + 实战练习
|
||||
|
||||
**第 2 周：扩展技能栈**
|
||||
- Day 8-9: opportunity-solution-tree
|
||||
- Day 10-11: customer-journey-map
|
||||
- Day 12: problem-framing-canvas
|
||||
- Day 13: user-story-mapping
|
||||
- Day 14: 完整项目实战
|
||||
|
||||
### 6.4 最佳实践
|
||||
|
||||
**✅ 应该做的**：
|
||||
1. **先理解问题，再跳方案** - 先用 jobs-to-be-done，再用 opportunity-solution-tree，避免直接写 PRD
|
||||
2. **文档是活的，不是交付物** - PRD、用户故事都是"活文档"，随着认知更新而更新
|
||||
3. **技能是框架，不是答案** - 技能帮你结构化思考，真正的答案来自用户调研和数据
|
||||
4. **组合使用，效果最佳** - 单个技能有用，组合使用威力更大
|
||||
|
||||
**❌ 应该避免的**：
|
||||
1. 跳过发现阶段 - 错误：直接写 PRD；正确：先做 JTBD 分析
|
||||
2. 把技能当 checklist - 错误：机械地填充模板；正确：理解背后的思考框架
|
||||
3. 一次性用所有技能 - 错误：每个项目都用 46 个技能；正确：根据项目阶段选择
|
||||
4. 忽视验证环节 - 错误：功能上线就结束；正确：用 business-health-diagnostic 验证
|
||||
|
||||
---
|
||||
|
||||
## 7. 常见问题
|
||||
|
||||
**Q1: 这些技能适合什么类型的产品？**
|
||||
- ✅ SaaS 产品（B2B/B2C）
|
||||
- ✅ 互联网产品（App、Web）
|
||||
- ✅ 数字化转型项目
|
||||
- ⚠️ 硬件产品（部分技能适用，需调整）
|
||||
- ❌ 纯内容产品（如博客、视频）
|
||||
|
||||
**Q2: 小团队/个人开发者需要用这么多技能吗？**
|
||||
不需要全部。建议：
|
||||
- **个人开发者**：jobs-to-be-done + user-story + prd-development（3 个核心）
|
||||
- **小团队 (<10 人)**：+ opportunity-solution-tree + prioritization-advisor（5 个）
|
||||
- **中型团队**：根据项目需要灵活组合
|
||||
|
||||
**Q3: 技能和敏捷开发是什么关系？**
|
||||
互补关系：
|
||||
```
|
||||
产品技能 敏捷开发
|
||||
─────────────────────────────
|
||||
发现需求 → 产品 Backlog
|
||||
写 PRD → Sprint 规划
|
||||
用户故事 → 迭代开发
|
||||
验证结果 → Sprint 回顾
|
||||
```
|
||||
|
||||
**Q4: 技能输出的文档可以直接用吗？**
|
||||
可以，但建议：技能输出的是**框架和初稿**，需要结合具体业务填充细节，和团队 review 后定稿。
|
||||
|
||||
**Q5: 如何评估这些技能的效果？**
|
||||
看三个指标：
|
||||
- **需求准确性**：上线后是否解决了真实问题？
|
||||
- **团队效率**：沟通成本是否降低？
|
||||
- **业务结果**：核心指标是否提升？
|
||||
|
||||
**Q6: 技能会更新吗？**
|
||||
是的。技能来源于：
|
||||
- 经典产品管理理论（JTBD、OST 等）
|
||||
- 最新实践（AI 产品、上下文工程等）
|
||||
- 社区反馈
|
||||
|
||||
**Q7: 可以和 OpenClaw 的其他功能结合吗？**
|
||||
可以！例如：
|
||||
- 用 `searxng` 做竞品调研 → 输入 `company-research`
|
||||
- 用 `browser` 抓取用户反馈 → 输入 `jobs-to-be-done`
|
||||
- 用 `message` 推送 PRD 给团队 → `prd-development` 输出后
|
||||
|
||||
---
|
||||
|
||||
---
|
||||
title: Product Manager Skills 完整使用指南
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# Product Manager Skills 完整使用指南
|
||||
|
||||
> 作者：沈盟 (程序员猫舍)
|
||||
> 来源：Telegra.ph
|
||||
> 发布时间：2026年3月27日
|
||||
> 链接：https://telegra.ph/Product-Manager-Skills-%E5%AE%8C%E6%95%B4%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8D%97-03-26
|
||||
|
||||
---
|
||||
|
||||
## 1. 总体概述
|
||||
|
||||
### 1.1 什么是 Product Manager Skills？
|
||||
|
||||
这是一套**产品管理框架的数字化封装**，包含 46 个独立但相互关联的技能（Skill），每个技能对应产品管理工作中的一个具体环节。
|
||||
|
||||
**核心理念**：
|
||||
> 用户不是买产品，而是"雇佣"产品来完成某项工作（Jobs-To-Be-Done）。
|
||||
|
||||
**设计目标**：
|
||||
- 📚 将经典产品管理方法论（JTBD、OST、精益 UX 等）转化为可执行的 AI 工作流
|
||||
- 🔗 打通产品发现→定义→交付→验证的完整链条
|
||||
- 🎯 避免"功能工厂"陷阱，确保团队解决正确的问题
|
||||
|
||||
### 1.2 与 JTBD 的关系
|
||||
|
||||
JTBD (Jobs-To-Be-Done) 是这套技能体系的**核心思想基础**：
|
||||
|
||||
```
|
||||
JTBD 理论 → 理解用户"雇佣"产品完成的工作
|
||||
↓
|
||||
产品发现 → 搞清楚用户真正的需求是什么
|
||||
↓
|
||||
产品定义 → 决定做什么来解决这些需求
|
||||
↓
|
||||
产品交付 → 把解决方案做出来
|
||||
↓
|
||||
产品验证 → 检查是否真的解决了问题
|
||||
```
|
||||
|
||||
这 46 个技能就是沿着这个链条展开的**工具箱**。
|
||||
|
||||
### 1.3 技能分类总览
|
||||
|
||||
46 个技能分为 5 大类：
|
||||
- 📊 用户研究类 (5 个)
|
||||
- 📈 市场分析类 (6 个)
|
||||
- 🎯 产品定义类 (8 个)
|
||||
- 📝 文档输出类 (6 个)
|
||||
- 📋 战略规划类 (7 个)
|
||||
- 🎓 高阶能力类 (14 个)
|
||||
|
||||
---
|
||||
|
||||
## 2. 技能全景图
|
||||
|
||||
### 2.1 产品管理工作流地图
|
||||
|
||||
```
|
||||
阶段 1: 产品发现 (Discovery)
|
||||
目标：搞清楚用户是谁？问题是什么？值不值得做？
|
||||
─────────────────────────────────────────────
|
||||
proto-persona → jobs-to-be-done → customer-journey-map
|
||||
初步画像 用户待办 用户旅程
|
||||
|
||||
↓ ↓ ↓
|
||||
|
||||
company-research → discovery-interview → tam-sam-som-calculator
|
||||
竞品调研 访谈准备 市场规模
|
||||
```
|
||||
|
||||
```
|
||||
阶段 2: 产品定义 (Definition)
|
||||
目标：搞清楚做什么？不做什么？优先级？
|
||||
─────────────────────────────────────────────
|
||||
problem-framing → opportunity-solution-tree → prioritization-advisor
|
||||
问题画布 机会方案树 优先级建议
|
||||
|
||||
↓ ↓ ↓
|
||||
|
||||
positioning-statement → epic-hypothesis → user-story-mapping
|
||||
定位陈述 史诗假设 故事地图
|
||||
```
|
||||
|
||||
```
|
||||
阶段 3: 产品交付 (Delivery)
|
||||
目标：怎么写文档？怎么拆任务？怎么验收？
|
||||
─────────────────────────────────────────────
|
||||
prd-development → user-story → storyboard
|
||||
需求文档 用户故事 故事板
|
||||
|
||||
↓ ↓ ↓
|
||||
|
||||
press-release → epic-breakdown → lean-ux-canvas
|
||||
新闻稿 史诗拆分 精益 UX
|
||||
```
|
||||
|
||||
```
|
||||
阶段 4: 产品验证 (Validation)
|
||||
目标：做得怎么样？要不要调整？下一步？
|
||||
─────────────────────────────────────────────
|
||||
business-health-diagnostic → feature-investment-advisor → roadmap-planning
|
||||
业务诊断 投资建议 路线图规划
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. 核心技能详解
|
||||
|
||||
### 3.1 jobs-to-be-done（用户待办任务）
|
||||
|
||||
**核心功能**：系统性地探索用户真正想完成的工作，而非表面需求。
|
||||
|
||||
**解决的问题**：
|
||||
- 用户说的"想要更快"到底是什么意思？
|
||||
- 为什么有些功能上线后数据没变化？
|
||||
- 如何避免做错功能？
|
||||
|
||||
**输出结构**：
|
||||
```markdown
|
||||
## Jobs-to-be-Done 分析
|
||||
|
||||
### 1. Customer Jobs（用户想完成的事）
|
||||
#### Functional Jobs（功能层面）
|
||||
#### Social Jobs（社会层面）
|
||||
#### Emotional Jobs（情感层面）
|
||||
|
||||
### 2. Pains（痛苦）
|
||||
#### Challenges（障碍）
|
||||
#### Costliness（代价）
|
||||
#### Common Mistakes（常见错误）
|
||||
#### Unresolved Problems（未解决问题）
|
||||
|
||||
### 3. Gains（收益）
|
||||
#### Expectations（期待）
|
||||
#### Savings（节省）
|
||||
#### Adoption Factors（采用因素）
|
||||
#### Life Improvement（生活改善）
|
||||
```
|
||||
|
||||
**使用示例**：
|
||||
- 场景：老板说"用户想要更快的开票速度"
|
||||
- 调用 jobs-to-be-done 后发现：
|
||||
- 表面需求："更快开票"
|
||||
- 真实 JTBD："从完工到收款的整个流程别耽误"
|
||||
- 真正机会：自动催款 + 收款跟踪，而不是"一键开票"按钮
|
||||
|
||||
---
|
||||
|
||||
### 3.2 opportunity-solution-tree（机会方案树）
|
||||
|
||||
**核心功能**：从模糊的需求到结构化的探索，避免"功能工厂"陷阱。
|
||||
|
||||
**解决的问题**：
|
||||
- 老板/客户直接说"我要这个功能"怎么办？
|
||||
- 如何确保团队在解决问题，而不是盲目堆功能？
|
||||
- 如何系统性地探索多种解决方案？
|
||||
|
||||
**输出结构**：
|
||||
```
|
||||
期望结果 (Outcome)
|
||||
│
|
||||
├── 机会 1 (问题/需求)
|
||||
│ ├── 方案 1 (实验)
|
||||
│ └── 方案 2 (实验)
|
||||
│
|
||||
├── 机会 2 (问题/需求)
|
||||
│ ├── 方案 1 (A/B测试)
|
||||
│ └── 方案 2 (可用性测试)
|
||||
│
|
||||
└── 机会 3 (问题/需求)
|
||||
└── ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 3.3 prd-development（产品需求文档）
|
||||
|
||||
**核心功能**：将发现阶段的洞察转化为工程可执行的 PRD 文档。
|
||||
|
||||
**输出结构**：
|
||||
```markdown
|
||||
# [功能名称] PRD
|
||||
|
||||
## 1. 执行摘要
|
||||
## 2. 问题陈述（谁？问题？证据？）
|
||||
## 3. 目标用户与画像
|
||||
## 4. 战略背景（业务目标、市场机会、竞争格局）
|
||||
## 5. 方案概述
|
||||
## 6. 成功指标
|
||||
## 7. 用户故事与需求
|
||||
## 8. 不在范围内
|
||||
## 9. 依赖与风险
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 3.4 prioritization-advisor（优先级建议）
|
||||
|
||||
**核心功能**：根据团队阶段和上下文，选择合适的优先级框架。
|
||||
|
||||
**推荐框架**：
|
||||
| 场景 | 推荐框架 |
|
||||
|------|----------|
|
||||
| 早期创业 | ICE |
|
||||
| 成长期 | RICE |
|
||||
| 企业/数据成熟 | WSJF |
|
||||
| 简单快速 | MoSCoW |
|
||||
|
||||
---
|
||||
|
||||
### 3.5 user-story（用户故事）
|
||||
|
||||
**输出格式**：
|
||||
```markdown
|
||||
### 用户故事 [ID]
|
||||
|
||||
#### 用例（Mike Cohn 格式）
|
||||
- **作为** [用户角色]
|
||||
- **我想要** [执行的动作]
|
||||
- **以便于** [期望的结果/价值]
|
||||
|
||||
#### 验收标准（Gherkin 格式）
|
||||
- **场景**: [场景描述]
|
||||
- **给定**: [前提条件]
|
||||
- **当**: [触发事件]
|
||||
- **那么**: [期望结果]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. 技能协作工作流
|
||||
|
||||
### 4.1 标准产品开发生命周期
|
||||
|
||||
```
|
||||
阶段 1: 发现 (Discovery)
|
||||
1. proto-persona → 定义目标用户
|
||||
2. jobs-to-be-done → 发现真实需求
|
||||
3. customer-journey-map → 绘制旅程
|
||||
4. tam-sam-som-calculator → 评估市场
|
||||
5. discovery-interview → 用户访谈
|
||||
|
||||
产出：用户洞察文档 + 机会评估报告
|
||||
|
||||
阶段 2: 定义 (Definition)
|
||||
6. problem-framing-canvas → 定义问题
|
||||
7. opportunity-solution-tree → 探索方案
|
||||
8. prioritization-advisor → 选择框架并排序
|
||||
9. positioning-statement → 产品定位
|
||||
|
||||
产出：优先级排序 + 产品定位文档
|
||||
|
||||
阶段 3: 交付 (Delivery)
|
||||
10. prd-development → 写 PRD
|
||||
11. user-story-mapping → 故事地图
|
||||
12. user-story → 写用户故事
|
||||
13. storyboard → 画故事板
|
||||
14. press-release → 写新闻稿
|
||||
|
||||
产出：PRD + 用户故事 + 故事板
|
||||
|
||||
阶段 4: 验证 (Validation)
|
||||
15. business-health-diagnostic → 上线后评估
|
||||
16. feature-investment-advisor → 决定下一步
|
||||
17. roadmap-planning → 更新路线图
|
||||
|
||||
产出：健康度报告 + 更新后的路线图
|
||||
```
|
||||
|
||||
### 4.2 技能调用依赖关系
|
||||
|
||||
```
|
||||
jobs-to-be-done
|
||||
↓
|
||||
problem-framing-canvas
|
||||
↓
|
||||
opportunity-solution-tree
|
||||
↓
|
||||
prioritization-advisor
|
||||
↓
|
||||
prd-development
|
||||
↓
|
||||
user-story-mapping
|
||||
↓
|
||||
user-story
|
||||
↓
|
||||
storyboard
|
||||
```
|
||||
|
||||
### 4.3 常见协作模式
|
||||
|
||||
| 模式 | 适用场景 | 技能链 |
|
||||
|------|----------|--------|
|
||||
| A: 新功能开发 | 完整流程 | proto-persona → jobs-to-be-done → OST → prioritization → PRD → user-story → storyboard |
|
||||
| B: 体验优化 | 聚焦旅程 | customer-journey-map → problem-framing → user-story-mapping → user-story → PRD |
|
||||
| C: 快速验证 | MVP 路线 | problem-framing → OST → lean-ux-canvas → press-release → user-story |
|
||||
| D: 季度规划 | 战略视角 | business-health-diagnostic → product-strategy-session → altitude-horizon → roadmap |
|
||||
|
||||
---
|
||||
|
||||
## 5. 完整案例演示
|
||||
|
||||
### 案例背景
|
||||
- **公司**：一款面向自由职业者的在线发票 SaaS
|
||||
- **问题**：用户流失率高，老板要求"提升用户体验"
|
||||
- **角色**：产品经理
|
||||
|
||||
### 第 1 步：理解用户（jobs-to-be-done）
|
||||
|
||||
**输入**：
|
||||
- 产品类型：在线发票工具
|
||||
- 目标用户：自由职业者（设计师、开发者、顾问）
|
||||
- 表面问题："用户说开票太麻烦"
|
||||
|
||||
**关键洞察**：
|
||||
> 用户说的"开票麻烦"，真实意思是"从完工到收款的整个流程太折腾"。真正的机会是**自动催款 + 收款跟踪**。
|
||||
|
||||
### 第 2 步：探索方案（opportunity-solution-tree）
|
||||
|
||||
**期望结果**：提升用户留存率 20%
|
||||
|
||||
**机会探索**：
|
||||
- 机会 1：用户忘记催款 → 方案：自动提醒
|
||||
- 机会 2：客户弄丢发票 → 方案：自助查询
|
||||
- 机会 3：对账麻烦 → 方案：自动对账
|
||||
|
||||
### 第 3 步：确定优先级（prioritization-advisor）
|
||||
|
||||
**推荐框架**：RICE
|
||||
|
||||
**评分结果**：
|
||||
1. 自动催款提醒 RICE = 45
|
||||
2. 发票自助查询 RICE = 32
|
||||
3. 自动对账 RICE = 28
|
||||
|
||||
### 第 4 步：写 PRD（prd-development）
|
||||
|
||||
产出完整的 PRD 文档，包含执行摘要、问题陈述、目标用户、成功指标、用户故事等。
|
||||
|
||||
### 第 5-7 步：拆故事 → 画故事板 → 验证结果
|
||||
|
||||
最终成果：
|
||||
- 收款周期缩短 36%
|
||||
- 续费率提升 18%
|
||||
- 用户满意度显著提升
|
||||
|
||||
---
|
||||
|
||||
## 6. 使用手册
|
||||
|
||||
### 6.1 安装与配置
|
||||
|
||||
- **位置**：~/.openclaw/workspace/skills/
|
||||
- **技能数量**：46 个
|
||||
- **占用空间**：约 2MB
|
||||
|
||||
### 6.2 如何调用技能
|
||||
|
||||
在 OpenClaw 会话中：
|
||||
- **方式 1**：直接提及技能名
|
||||
- "帮我用 jobs-to-be-done 分析一下发票工具的用户需求"
|
||||
- **方式 2**：描述任务，AI 自动匹配
|
||||
- **方式 3**：使用技能前缀
|
||||
- "/skills prd-development 我要写一个 PRD"
|
||||
|
||||
### 6.3 新手入门路径
|
||||
|
||||
**第 1 周：掌握核心 5 技能**
|
||||
- Day 1-2: jobs-to-be-done（理解用户）
|
||||
- Day 3-4: user-story（拆分需求）
|
||||
- Day 5: prd-development（写文档）
|
||||
- Day 6: prioritization-advisor（排优先级）
|
||||
- Day 7: 复习 + 实战练习
|
||||
|
||||
**第 2 周：扩展技能栈**
|
||||
- Day 8-9: opportunity-solution-tree
|
||||
- Day 10-11: customer-journey-map
|
||||
- Day 12: problem-framing-canvas
|
||||
- Day 13: user-story-mapping
|
||||
- Day 14: 完整项目实战
|
||||
|
||||
### 6.4 最佳实践
|
||||
|
||||
**✅ 应该做的**：
|
||||
1. **先理解问题，再跳方案** - 先用 jobs-to-be-done，再用 opportunity-solution-tree，避免直接写 PRD
|
||||
2. **文档是活的，不是交付物** - PRD、用户故事都是"活文档"，随着认知更新而更新
|
||||
3. **技能是框架，不是答案** - 技能帮你结构化思考，真正的答案来自用户调研和数据
|
||||
4. **组合使用，效果最佳** - 单个技能有用，组合使用威力更大
|
||||
|
||||
**❌ 应该避免的**：
|
||||
1. 跳过发现阶段 - 错误：直接写 PRD；正确：先做 JTBD 分析
|
||||
2. 把技能当 checklist - 错误：机械地填充模板；正确：理解背后的思考框架
|
||||
3. 一次性用所有技能 - 错误：每个项目都用 46 个技能；正确：根据项目阶段选择
|
||||
4. 忽视验证环节 - 错误：功能上线就结束；正确：用 business-health-diagnostic 验证
|
||||
|
||||
---
|
||||
|
||||
## 7. 常见问题
|
||||
|
||||
**Q1: 这些技能适合什么类型的产品？**
|
||||
- ✅ SaaS 产品（B2B/B2C）
|
||||
- ✅ 互联网产品（App、Web）
|
||||
- ✅ 数字化转型项目
|
||||
- ⚠️ 硬件产品（部分技能适用，需调整）
|
||||
- ❌ 纯内容产品（如博客、视频）
|
||||
|
||||
**Q2: 小团队/个人开发者需要用这么多技能吗？**
|
||||
不需要全部。建议：
|
||||
- **个人开发者**：jobs-to-be-done + user-story + prd-development（3 个核心）
|
||||
- **小团队 (<10 人)**：+ opportunity-solution-tree + prioritization-advisor（5 个）
|
||||
- **中型团队**：根据项目需要灵活组合
|
||||
|
||||
**Q3: 技能和敏捷开发是什么关系？**
|
||||
互补关系：
|
||||
```
|
||||
产品技能 敏捷开发
|
||||
─────────────────────────────
|
||||
发现需求 → 产品 Backlog
|
||||
写 PRD → Sprint 规划
|
||||
用户故事 → 迭代开发
|
||||
验证结果 → Sprint 回顾
|
||||
```
|
||||
|
||||
**Q4: 技能输出的文档可以直接用吗？**
|
||||
可以，但建议：技能输出的是**框架和初稿**，需要结合具体业务填充细节，和团队 review 后定稿。
|
||||
|
||||
**Q5: 如何评估这些技能的效果？**
|
||||
看三个指标：
|
||||
- **需求准确性**：上线后是否解决了真实问题？
|
||||
- **团队效率**：沟通成本是否降低？
|
||||
- **业务结果**：核心指标是否提升？
|
||||
|
||||
**Q6: 技能会更新吗？**
|
||||
是的。技能来源于：
|
||||
- 经典产品管理理论（JTBD、OST 等）
|
||||
- 最新实践（AI 产品、上下文工程等）
|
||||
- 社区反馈
|
||||
|
||||
**Q7: 可以和 OpenClaw 的其他功能结合吗？**
|
||||
可以！例如：
|
||||
- 用 `searxng` 做竞品调研 → 输入 `company-research`
|
||||
- 用 `browser` 抓取用户反馈 → 输入 `jobs-to-be-done`
|
||||
- 用 `message` 推送 PRD 给团队 → `prd-development` 输出后
|
||||
|
||||
---
|
||||
|
||||
> 来源：由"微信搬运工"搬运（点击进入：https://t.me/wxbyg）
|
||||
@@ -1,223 +1,223 @@
|
||||
---
|
||||
title: How to Fix Your OpenClaw's Memory
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# How to Fix Your OpenClaw's Memory
|
||||
|
||||
**Source**: https://x.com/_sean_matthew/status/2031800232569102610
|
||||
**Author**: Sean Matthew (Verified)
|
||||
**Published**: March 12, 2026 at 2:31 AM
|
||||
**Views**: 1,670 | **Reposts**: 5 | **Likes**: 41 | **Bookmarks**: 50
|
||||
|
||||
---
|
||||
|
||||
A few weeks ago, my OpenClaw stopped working. At first, I thought it was a model problem. It took me a while to figure out I was wrong.
|
||||
|
||||
Here's what happened: my agent kept forgetting important context. Skills weren't being triggered correctly. Cron jobs stopped running reliably. I kept getting context-related errors in Telegram. And the longer a session ran, the more tokens it seemed to burn.
|
||||
|
||||
This happened when I was running Kimi K2.5 as my main model. I switched over to Sonnet 4.6, and things got a little better, especially with triggering skills and calling tools. But the real problem wasn't the model. It was how OpenClaw manages memory and context behind the scenes.
|
||||
|
||||
Right around the time I was struggling with this, I saw a [post](https://x.com/i/article/2025615759771123712) from a developer named Ramya, who documented spending a week debugging her agent's memory. She was running into some of the exact same issues I was hitting. Her article helped me a ton and inspired me to completely rethink the way my OpenClaw runs. The fixes discussed here are based on what I changed in my own setup to make my OpenClaw agent, Jarvis, more stable and much cheaper to run.
|
||||
|
||||
If you'd rather watch how to do this than read about it, I have a full video walkthrough on YouTube, which you can access here: https://youtu.be/UTztjR4o7Y8
|
||||
|
||||
---
|
||||
|
||||
## Before You Start
|
||||
|
||||
If you followed my last article on the [3 Essential Tools for OpenClaw](https://x.com/_sean_matthew/status/2028902126005653889), this setup is the same. On whatever machine OpenClaw is running, open a Terminal and run:
|
||||
|
||||
```bash
|
||||
cd ~/.openclaw
|
||||
```
|
||||
|
||||
This puts you in the OpenClaw workspace folder. Launch Claude Code, Codex, or your preferred coding agent from this directory. All of the prompts below assume the agent can see your OpenClaw workspace.
|
||||
|
||||
If you're wondering why I use Claude Code instead of OpenClaw itself for this kind of work, here's the short answer: I want OpenClaw executing systems, not burning tokens to build them. Claude Code handles the engineering. OpenClaw runs the result. I covered this in more detail in the last article, so I won't repeat myself here.
|
||||
|
||||
Now, let's move on to the fixes.
|
||||
|
||||
---
|
||||
|
||||
## Fix 1: Stop Losing Important Context During Compaction
|
||||
|
||||
Here's the first thing to understand. OpenClaw has a finite context window, just like any AI agent. This is basically the agent's short-term memory. As your conversation gets longer, OpenClaw does something called compaction: it compresses older messages into a summary to make room for new ones.
|
||||
|
||||
Sounds reasonable, right? The problem is that compaction treats everything equally. That important instruction you gave 20 messages ago gets the same compression treatment as everything else, no matter how relevant. Names, numbers, exact decisions, and other important details can get lost as the compaction process distills everything into a generic summary.
|
||||
|
||||
**The rule is simple:** if it's only in the context window, it's temporary. If it's on disk, it survives.
|
||||
|
||||
The first fix is memory flush. This gives the agent a chance to write important information out to disk before compaction runs.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw memory docs here: https://docs.openclaw.ai/concepts/memory Then inspect my current openclaw.json and enable memory flush under compaction. Requirements:
|
||||
1. Turn memory flush on
|
||||
2. Use a soft threshold amount that is recommended in the documentation (if none is recommended, use 4000 tokens)
|
||||
3. If the compaction block does not exist yet, create it
|
||||
4. Do not duplicate existing settings
|
||||
5. After making the change, explain in plain English what will happen before compaction runs
|
||||
```
|
||||
|
||||
That handles the first failure mode. But there's a second one: very long sessions.
|
||||
|
||||
Memory flush triggers once per compaction cycle. If you're in a really long session, like a four-hour deep work session, compaction might run multiple times, and only the first one gets the flush treatment. So you also want session pruning to aggressively clean up old context:
|
||||
|
||||
**Paste this prompt into Claude Code next:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw session pruning docs here: https://docs.openclaw.ai/concepts/session-pruning Then inspect my openclaw.json and configure session pruning (also known as context pruning). Requirements:
|
||||
1. Use cache-ttl mode
|
||||
2. Set the TTL to whatever is recommended in the documentation (if nothing is recommended, use 4 hours)
|
||||
3. Keep the last 3 assistant messages
|
||||
4. If session/context pruning already exists, update it safely instead of creating duplicate blocks
|
||||
5. After the change, summarize the final behavior in plain English
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Fix 2: Make Retrieval Actually Work
|
||||
|
||||
Saving important information via memory flush is only half the job. Your agent also has to find this information again. And more importantly, it has to remember to look.
|
||||
|
||||
This is where a lot of setups quietly fail. The data exists somewhere on disk, but the agent never searches for it, or the default retrieval isn't good enough to surface the right result.
|
||||
|
||||
If you followed my last article, you already know about QMD: the hybrid search engine by Tobi Lutke that combines keyword matching, vector semantic search, and an LLM re-ranker. If you haven't set that up yet, go read that article first. It's the single biggest upgrade you can make to OpenClaw's memory.
|
||||
|
||||
But here's the more subtle problem I didn't cover last time: even with great search, your agent has to actually decide to search. And if the conversation doesn't trigger the right cues, it just won't look things up. The information exists. The agent doesn't use it.
|
||||
|
||||
So you need to add explicit retrieval instructions to the top of your agent's file (AGENTS.md). Think of this as a checklist: before doing anything, the agent searches for relevant context.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw memory docs for QMD here: https://docs.openclaw.ai/concepts/memory#qmd-backend-experimental Then inspect my current OpenClaw setup and improve retrieval. Requirements:
|
||||
1. If I am not already using QMD as the memory backend, install and configure it
|
||||
2. Verify the memory backend is working with a test query
|
||||
3. Update the top of my AGENTS.md instructions so that before starting any task the agent:
|
||||
- searches daily logs for relevant context
|
||||
- checks a central learnings file (LEARNINGS.md) for rules related to the task
|
||||
4. Keep the boot instructions concise and operational
|
||||
5. After the changes, explain the new retrieval flow in plain English
|
||||
```
|
||||
|
||||
What this does is it grounds your OpenClaw's actions in any relevant memories that can be searched in QMD, as well as a central list of rules that guard against mistakes and reinforce other lessons learned: a LEARNINGS.md file. This was another revelation from Ramya's article: that every time the OpenClaw agent makes a mistake, you make the agent write a one-line or two-line rule in a LEARNINGS.md file to prevent the agent from making the same mistake again. This, coupled with the agent's smarter search and retrieval of memories via QMD, will make your agent much smarter and more efficient.
|
||||
|
||||
---
|
||||
|
||||
## Fix 3: The Heartbeat Cost Trap
|
||||
|
||||
This was probably the most expensive mistake in my setup, and nobody really warns you about it.
|
||||
|
||||
If you have heartbeat enabled, which you should by default, your agent wakes up every 30 minutes to check on things. You arguably need this to make your OpenClaw truly a realtime agent. But, you should also know that every single heartbeat is a full agent turn, not a lightweight ping. A full API call that carries the entire session context.
|
||||
|
||||
That means every 30 minutes, your agent re-sends your entire system prompt — AGENTS.md, SOUL.md, TOOLS.md, IDENTITY.md, HEARTBEAT.md, MEMORY.md — plus all your skill metadata, plus whatever conversation history is in the session. That's potentially 10,000 to 15,000 tokens, forty-eight times a day. Even on a cheap model, that adds up fast.
|
||||
|
||||
Here's what the optimized heartbeat config looks like:
|
||||
|
||||
```json
|
||||
{
|
||||
"agents": {
|
||||
"defaults": {
|
||||
"heartbeat": {
|
||||
"every": "30m",
|
||||
"lightContext": true,
|
||||
"model": "google/gemini-3.1-flash-lite-preview",
|
||||
"activeHours": {
|
||||
"start": "08:00",
|
||||
"end": "23:00"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The key changes, and why each one matters:
|
||||
|
||||
**lightContext: true** — This is a big one. By default, every heartbeat loads your entire system prompt. With light context enabled, the heartbeat only loads HEARTBEAT.md. That's it. All other files get skipped. Instead of sending 10,000 to 15,000 tokens on every heartbeat, you're sending maybe a few hundred tokens.
|
||||
|
||||
**Cheap model** — There's no reason to burn Opus or Sonnet tokens on a heartbeat. It's just checking whether anything needs attention. Use the cheapest model that can read a checklist. I like Gemini 3.1 Flash-Lite, but you could even use a local model, such as Qwen 3.5:9B, potentially eliminating heartbeat costs entirely.
|
||||
|
||||
**Active hours** — If your agent doesn't need to check in at 3 AM, don't let it. That cuts your heartbeat calls almost in half. If you must run your heartbeat all day, alternatively, you can have your heartbeat run every 1 or 2 hours, instead of every 30 minutes.
|
||||
|
||||
**And one more thing:** keep HEARTBEAT.md tiny. If that file is bloated with instructions, you're paying for all of it on every heartbeat run. Trim it to just the most essential checklist of things to check.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw heartbeat docs here: https://docs.openclaw.ai/gateway/heartbeat Then inspect my current OpenClaw heartbeat configuration and optimize it for low token usage. Requirements:
|
||||
1. Enable lightContext for heartbeat
|
||||
2. Set the heartbeat model to google/gemini-3.1-flash-lite-preview
|
||||
3. Limit active hours to 08:00 through 23:00
|
||||
4. Review HEARTBEAT.md and trim it so it only contains the minimum instructions needed for heartbeat runs
|
||||
5. Do not remove any heartbeat behavior that is actually necessary
|
||||
6. After the change, explain what context heartbeat will still load on each run
|
||||
```
|
||||
|
||||
You do not need to enable **all** of the heartbeat cost-saving recommendations, though. At a minimum, you really only need to use a cheap model and keep HEARTBEAT.md tiny.
|
||||
|
||||
---
|
||||
|
||||
## Fix 4: Do a Full System Prompt Audit
|
||||
|
||||
This was the lesson that tied everything together for me.
|
||||
|
||||
When I was running Kimi K2.5, I kept thinking it was the model's fault. When I started getting endless "context full" error messages when I messaged Jarvis in Telegram, I switched to Sonnet 4.6. The increased context window helped a bit. But I was still burning more context than I should have.
|
||||
|
||||
That's when I realized: I had **no idea** what was actually in my agent's context. All these files — AGENTS.md, SOUL.md, TOOLS.md, IDENTITY.md, USER.md, HEARTBEAT.md, MEMORY.md — they all auto-load on every turn. And I'd been adding stuff to them without ever auditing how big they'd gotten or checking for redundancy.
|
||||
|
||||
You can inspect some of this inside OpenClaw with the command "/context detail." But the best move is to let Claude Code audit the whole prompt surface for duplication and bloat. Claude Code is perfect for this because it can read all those files, understand the relationships between them, and give you a concrete list of what to trim.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read all of my OpenClaw system prompt files:
|
||||
- AGENTS.md
|
||||
- SOUL.md
|
||||
- TOOLS.md
|
||||
- IDENTITY.md
|
||||
- USER.md
|
||||
- HEARTBEAT.md
|
||||
- MEMORY.md Then do a full system prompt audit. Requirements:
|
||||
1. Identify anything redundant, duplicated across files, or unnecessarily long
|
||||
2. Give me a concrete cut list with reasons
|
||||
3. Apply the trims carefully without removing important behavior
|
||||
4. Keep responsibilities clearly separated between files
|
||||
5. After editing, summarize the biggest reductions and any risks or follow-up checks I should make
|
||||
```
|
||||
|
||||
After I did this, my system prompt shrank significantly. And that's really why Kimi K2.5 was choking: it has a smaller context window, and I was wasting a huge chunk of it on bloated system files. The real fix wasn't switching models. It was trimming the unnecessary stuff.
|
||||
|
||||
This is why I now think that context engineering and memory management are super important when you're customizing your own personal AI agent like OpenClaw.
|
||||
|
||||
---
|
||||
|
||||
## Recap
|
||||
|
||||
Here's the practical sequence I'd recommend:
|
||||
|
||||
1. **Memory flush** — Write important context to disk before compaction wipes it.
|
||||
2. **Session pruning** — Stop long-running conversations from dragging dead context forever.
|
||||
3. **Retrieval + boot instructions** — Upgrade search with QMD, and make sure the agent is explicitly told to search for prior context before starting any task. Save important rules in a central .md file to prevent the agent from repeating mistakes.
|
||||
4. **Heartbeat optimization** — Enable light context, use a cheap model, limit active hours, and/or keep HEARTBEAT.md small.
|
||||
5. **System prompt audit** — Let Claude Code do a full audit on your system prompt to cut the bloat.
|
||||
|
||||
The main takeaway here is that if you're building with OpenClaw, the models you pick to run your agent are only half the battle (or less). The real value is in making sure your agent has a good memory and context management system.
|
||||
|
||||
---
|
||||
|
||||
## Resources
|
||||
|
||||
- [Ramya's (code_rams) article on agent memory debugging](https://x.com/i/article/2025615759771123712)
|
||||
- [OpenClaw memory docs](https://docs.openclaw.ai/concepts/memory)
|
||||
- [OpenClaw session pruning docs](https://docs.openclaw.ai/concepts/session-pruning)
|
||||
- [OpenClaw heartbeat docs](https://docs.openclaw.ai/gateway/heartbeat)
|
||||
- [QMD by Tobi Lutke](https://github.com/tobi/qmd)
|
||||
---
|
||||
title: How to Fix Your OpenClaw's Memory
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# How to Fix Your OpenClaw's Memory
|
||||
|
||||
**Source**: https://x.com/_sean_matthew/status/2031800232569102610
|
||||
**Author**: Sean Matthew (Verified)
|
||||
**Published**: March 12, 2026 at 2:31 AM
|
||||
**Views**: 1,670 | **Reposts**: 5 | **Likes**: 41 | **Bookmarks**: 50
|
||||
|
||||
---
|
||||
|
||||
A few weeks ago, my OpenClaw stopped working. At first, I thought it was a model problem. It took me a while to figure out I was wrong.
|
||||
|
||||
Here's what happened: my agent kept forgetting important context. Skills weren't being triggered correctly. Cron jobs stopped running reliably. I kept getting context-related errors in Telegram. And the longer a session ran, the more tokens it seemed to burn.
|
||||
|
||||
This happened when I was running Kimi K2.5 as my main model. I switched over to Sonnet 4.6, and things got a little better, especially with triggering skills and calling tools. But the real problem wasn't the model. It was how OpenClaw manages memory and context behind the scenes.
|
||||
|
||||
Right around the time I was struggling with this, I saw a [post](https://x.com/i/article/2025615759771123712) from a developer named Ramya, who documented spending a week debugging her agent's memory. She was running into some of the exact same issues I was hitting. Her article helped me a ton and inspired me to completely rethink the way my OpenClaw runs. The fixes discussed here are based on what I changed in my own setup to make my OpenClaw agent, Jarvis, more stable and much cheaper to run.
|
||||
|
||||
If you'd rather watch how to do this than read about it, I have a full video walkthrough on YouTube, which you can access here: https://youtu.be/UTztjR4o7Y8
|
||||
|
||||
---
|
||||
|
||||
## Before You Start
|
||||
|
||||
If you followed my last article on the [3 Essential Tools for OpenClaw](https://x.com/_sean_matthew/status/2028902126005653889), this setup is the same. On whatever machine OpenClaw is running, open a Terminal and run:
|
||||
|
||||
```bash
|
||||
cd ~/.openclaw
|
||||
```
|
||||
|
||||
This puts you in the OpenClaw workspace folder. Launch Claude Code, Codex, or your preferred coding agent from this directory. All of the prompts below assume the agent can see your OpenClaw workspace.
|
||||
|
||||
If you're wondering why I use Claude Code instead of OpenClaw itself for this kind of work, here's the short answer: I want OpenClaw executing systems, not burning tokens to build them. Claude Code handles the engineering. OpenClaw runs the result. I covered this in more detail in the last article, so I won't repeat myself here.
|
||||
|
||||
Now, let's move on to the fixes.
|
||||
|
||||
---
|
||||
|
||||
## Fix 1: Stop Losing Important Context During Compaction
|
||||
|
||||
Here's the first thing to understand. OpenClaw has a finite context window, just like any AI agent. This is basically the agent's short-term memory. As your conversation gets longer, OpenClaw does something called compaction: it compresses older messages into a summary to make room for new ones.
|
||||
|
||||
Sounds reasonable, right? The problem is that compaction treats everything equally. That important instruction you gave 20 messages ago gets the same compression treatment as everything else, no matter how relevant. Names, numbers, exact decisions, and other important details can get lost as the compaction process distills everything into a generic summary.
|
||||
|
||||
**The rule is simple:** if it's only in the context window, it's temporary. If it's on disk, it survives.
|
||||
|
||||
The first fix is memory flush. This gives the agent a chance to write important information out to disk before compaction runs.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw memory docs here: https://docs.openclaw.ai/concepts/memory Then inspect my current openclaw.json and enable memory flush under compaction. Requirements:
|
||||
1. Turn memory flush on
|
||||
2. Use a soft threshold amount that is recommended in the documentation (if none is recommended, use 4000 tokens)
|
||||
3. If the compaction block does not exist yet, create it
|
||||
4. Do not duplicate existing settings
|
||||
5. After making the change, explain in plain English what will happen before compaction runs
|
||||
```
|
||||
|
||||
That handles the first failure mode. But there's a second one: very long sessions.
|
||||
|
||||
Memory flush triggers once per compaction cycle. If you're in a really long session, like a four-hour deep work session, compaction might run multiple times, and only the first one gets the flush treatment. So you also want session pruning to aggressively clean up old context:
|
||||
|
||||
**Paste this prompt into Claude Code next:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw session pruning docs here: https://docs.openclaw.ai/concepts/session-pruning Then inspect my openclaw.json and configure session pruning (also known as context pruning). Requirements:
|
||||
1. Use cache-ttl mode
|
||||
2. Set the TTL to whatever is recommended in the documentation (if nothing is recommended, use 4 hours)
|
||||
3. Keep the last 3 assistant messages
|
||||
4. If session/context pruning already exists, update it safely instead of creating duplicate blocks
|
||||
5. After the change, summarize the final behavior in plain English
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Fix 2: Make Retrieval Actually Work
|
||||
|
||||
Saving important information via memory flush is only half the job. Your agent also has to find this information again. And more importantly, it has to remember to look.
|
||||
|
||||
This is where a lot of setups quietly fail. The data exists somewhere on disk, but the agent never searches for it, or the default retrieval isn't good enough to surface the right result.
|
||||
|
||||
If you followed my last article, you already know about QMD: the hybrid search engine by Tobi Lutke that combines keyword matching, vector semantic search, and an LLM re-ranker. If you haven't set that up yet, go read that article first. It's the single biggest upgrade you can make to OpenClaw's memory.
|
||||
|
||||
But here's the more subtle problem I didn't cover last time: even with great search, your agent has to actually decide to search. And if the conversation doesn't trigger the right cues, it just won't look things up. The information exists. The agent doesn't use it.
|
||||
|
||||
So you need to add explicit retrieval instructions to the top of your agent's file (AGENTS.md). Think of this as a checklist: before doing anything, the agent searches for relevant context.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw memory docs for QMD here: https://docs.openclaw.ai/concepts/memory#qmd-backend-experimental Then inspect my current OpenClaw setup and improve retrieval. Requirements:
|
||||
1. If I am not already using QMD as the memory backend, install and configure it
|
||||
2. Verify the memory backend is working with a test query
|
||||
3. Update the top of my AGENTS.md instructions so that before starting any task the agent:
|
||||
- searches daily logs for relevant context
|
||||
- checks a central learnings file (LEARNINGS.md) for rules related to the task
|
||||
4. Keep the boot instructions concise and operational
|
||||
5. After the changes, explain the new retrieval flow in plain English
|
||||
```
|
||||
|
||||
What this does is it grounds your OpenClaw's actions in any relevant memories that can be searched in QMD, as well as a central list of rules that guard against mistakes and reinforce other lessons learned: a LEARNINGS.md file. This was another revelation from Ramya's article: that every time the OpenClaw agent makes a mistake, you make the agent write a one-line or two-line rule in a LEARNINGS.md file to prevent the agent from making the same mistake again. This, coupled with the agent's smarter search and retrieval of memories via QMD, will make your agent much smarter and more efficient.
|
||||
|
||||
---
|
||||
|
||||
## Fix 3: The Heartbeat Cost Trap
|
||||
|
||||
This was probably the most expensive mistake in my setup, and nobody really warns you about it.
|
||||
|
||||
If you have heartbeat enabled, which you should by default, your agent wakes up every 30 minutes to check on things. You arguably need this to make your OpenClaw truly a realtime agent. But, you should also know that every single heartbeat is a full agent turn, not a lightweight ping. A full API call that carries the entire session context.
|
||||
|
||||
That means every 30 minutes, your agent re-sends your entire system prompt — AGENTS.md, SOUL.md, TOOLS.md, IDENTITY.md, HEARTBEAT.md, MEMORY.md — plus all your skill metadata, plus whatever conversation history is in the session. That's potentially 10,000 to 15,000 tokens, forty-eight times a day. Even on a cheap model, that adds up fast.
|
||||
|
||||
Here's what the optimized heartbeat config looks like:
|
||||
|
||||
```json
|
||||
{
|
||||
"agents": {
|
||||
"defaults": {
|
||||
"heartbeat": {
|
||||
"every": "30m",
|
||||
"lightContext": true,
|
||||
"model": "google/gemini-3.1-flash-lite-preview",
|
||||
"activeHours": {
|
||||
"start": "08:00",
|
||||
"end": "23:00"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The key changes, and why each one matters:
|
||||
|
||||
**lightContext: true** — This is a big one. By default, every heartbeat loads your entire system prompt. With light context enabled, the heartbeat only loads HEARTBEAT.md. That's it. All other files get skipped. Instead of sending 10,000 to 15,000 tokens on every heartbeat, you're sending maybe a few hundred tokens.
|
||||
|
||||
**Cheap model** — There's no reason to burn Opus or Sonnet tokens on a heartbeat. It's just checking whether anything needs attention. Use the cheapest model that can read a checklist. I like Gemini 3.1 Flash-Lite, but you could even use a local model, such as Qwen 3.5:9B, potentially eliminating heartbeat costs entirely.
|
||||
|
||||
**Active hours** — If your agent doesn't need to check in at 3 AM, don't let it. That cuts your heartbeat calls almost in half. If you must run your heartbeat all day, alternatively, you can have your heartbeat run every 1 or 2 hours, instead of every 30 minutes.
|
||||
|
||||
**And one more thing:** keep HEARTBEAT.md tiny. If that file is bloated with instructions, you're paying for all of it on every heartbeat run. Trim it to just the most essential checklist of things to check.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read the OpenClaw heartbeat docs here: https://docs.openclaw.ai/gateway/heartbeat Then inspect my current OpenClaw heartbeat configuration and optimize it for low token usage. Requirements:
|
||||
1. Enable lightContext for heartbeat
|
||||
2. Set the heartbeat model to google/gemini-3.1-flash-lite-preview
|
||||
3. Limit active hours to 08:00 through 23:00
|
||||
4. Review HEARTBEAT.md and trim it so it only contains the minimum instructions needed for heartbeat runs
|
||||
5. Do not remove any heartbeat behavior that is actually necessary
|
||||
6. After the change, explain what context heartbeat will still load on each run
|
||||
```
|
||||
|
||||
You do not need to enable **all** of the heartbeat cost-saving recommendations, though. At a minimum, you really only need to use a cheap model and keep HEARTBEAT.md tiny.
|
||||
|
||||
---
|
||||
|
||||
## Fix 4: Do a Full System Prompt Audit
|
||||
|
||||
This was the lesson that tied everything together for me.
|
||||
|
||||
When I was running Kimi K2.5, I kept thinking it was the model's fault. When I started getting endless "context full" error messages when I messaged Jarvis in Telegram, I switched to Sonnet 4.6. The increased context window helped a bit. But I was still burning more context than I should have.
|
||||
|
||||
That's when I realized: I had **no idea** what was actually in my agent's context. All these files — AGENTS.md, SOUL.md, TOOLS.md, IDENTITY.md, USER.md, HEARTBEAT.md, MEMORY.md — they all auto-load on every turn. And I'd been adding stuff to them without ever auditing how big they'd gotten or checking for redundancy.
|
||||
|
||||
You can inspect some of this inside OpenClaw with the command "/context detail." But the best move is to let Claude Code audit the whole prompt surface for duplication and bloat. Claude Code is perfect for this because it can read all those files, understand the relationships between them, and give you a concrete list of what to trim.
|
||||
|
||||
**Paste this prompt into Claude Code:**
|
||||
|
||||
```markdown
|
||||
Read all of my OpenClaw system prompt files:
|
||||
- AGENTS.md
|
||||
- SOUL.md
|
||||
- TOOLS.md
|
||||
- IDENTITY.md
|
||||
- USER.md
|
||||
- HEARTBEAT.md
|
||||
- MEMORY.md Then do a full system prompt audit. Requirements:
|
||||
1. Identify anything redundant, duplicated across files, or unnecessarily long
|
||||
2. Give me a concrete cut list with reasons
|
||||
3. Apply the trims carefully without removing important behavior
|
||||
4. Keep responsibilities clearly separated between files
|
||||
5. After editing, summarize the biggest reductions and any risks or follow-up checks I should make
|
||||
```
|
||||
|
||||
After I did this, my system prompt shrank significantly. And that's really why Kimi K2.5 was choking: it has a smaller context window, and I was wasting a huge chunk of it on bloated system files. The real fix wasn't switching models. It was trimming the unnecessary stuff.
|
||||
|
||||
This is why I now think that context engineering and memory management are super important when you're customizing your own personal AI agent like OpenClaw.
|
||||
|
||||
---
|
||||
|
||||
## Recap
|
||||
|
||||
Here's the practical sequence I'd recommend:
|
||||
|
||||
1. **Memory flush** — Write important context to disk before compaction wipes it.
|
||||
2. **Session pruning** — Stop long-running conversations from dragging dead context forever.
|
||||
3. **Retrieval + boot instructions** — Upgrade search with QMD, and make sure the agent is explicitly told to search for prior context before starting any task. Save important rules in a central .md file to prevent the agent from repeating mistakes.
|
||||
4. **Heartbeat optimization** — Enable light context, use a cheap model, limit active hours, and/or keep HEARTBEAT.md small.
|
||||
5. **System prompt audit** — Let Claude Code do a full audit on your system prompt to cut the bloat.
|
||||
|
||||
The main takeaway here is that if you're building with OpenClaw, the models you pick to run your agent are only half the battle (or less). The real value is in making sure your agent has a good memory and context management system.
|
||||
|
||||
---
|
||||
|
||||
## Resources
|
||||
|
||||
- [Ramya's (code_rams) article on agent memory debugging](https://x.com/i/article/2025615759771123712)
|
||||
- [OpenClaw memory docs](https://docs.openclaw.ai/concepts/memory)
|
||||
- [OpenClaw session pruning docs](https://docs.openclaw.ai/concepts/session-pruning)
|
||||
- [OpenClaw heartbeat docs](https://docs.openclaw.ai/gateway/heartbeat)
|
||||
- [QMD by Tobi Lutke](https://github.com/tobi/qmd)
|
||||
|
||||
@@ -1,358 +1,358 @@
|
||||
---
|
||||
title: last30days AI Research Tool — 原始研究数据
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: [AI, AITips, AITools, AIgenerated, BuildWithAI, FutureCodex, Humanizer, LearnOnTikTok, Productivity, Roadmap, Scientist, TikTokTech, ai, aiagent, aiagents, aitools, aiworkflow, automation, chatgpt, claude, claudeai, claudecode, contentcalendar, contentcreator, contentplan, creatortips, juliangoldie, news, openclaw, productivity, reseaai, smallbusiness, socialmediatips]
|
||||
---
|
||||
|
||||
# last30days AI Research Tool — 原始研究数据
|
||||
|
||||
> 生成时间：2026-03-30
|
||||
> 数据范围：2026-02-28 至 2026-03-30（过去30天）
|
||||
> 主题：last30days AI research tool
|
||||
> 模型：OpenAI GPT-5.2 / xAI Grok-4-1-fast
|
||||
|
||||
---
|
||||
|
||||
## Reddit 热门讨论（90条帖子）
|
||||
|
||||
### R1 (score:78) — r/popculturechat
|
||||
**This is a celebrity scandal from Germany that deserves international attention**
|
||||
- 评分：18311👍 | 916💬
|
||||
- 链接：https://www.reddit.com/r/popculturechat/comments/1ryqtw2/this_is_a_celebrity_scandal_from_germany_that/
|
||||
- 热评："sincerely what the fuck" (8823 upvotes)
|
||||
|
||||
### R7 (score:69) — r/ClaudeAI
|
||||
**Anthropic's research proves AI coding tools are secretly making developers worse**
|
||||
- 评分：1583👍 | 245💬
|
||||
- 链接：https://www.reddit.com/r/ClaudeAI/comments/1rzmfyd/anthropics_research_proves_ai_coding_tools_are/
|
||||
|
||||
### R8 (score:69) — r/singularity
|
||||
**OpenAI research team reveals its models go insane when given repetitive tasks**
|
||||
- 评分：1405👍 | 252💬
|
||||
- 链接：https://www.reddit.com/r/singularity/comments/1rzg4lk/openai_research_team_reveals_its_models_go_insane/
|
||||
|
||||
### R15 (score:68) — r/ArtificialInteligence
|
||||
**I tested what happens when you give an AI coding agent access to 2 million research papers**
|
||||
- 评分：130👍 | 43💬
|
||||
- 链接：https://www.reddit.com/r/ArtificialInteligence/comments/1s6bavl/i_tested_what_happens_when_you_give_an_ai_coding/
|
||||
|
||||
### R10 (score:67) — r/ArtificialInteligence
|
||||
**Wharton researchers just proved why "just review the AI output" doesn't work**
|
||||
- 评分：477👍 | 135💬
|
||||
- 链接：https://www.reddit.com/r/ArtificialInteligence/comments/1s154ig/wharton_researchers_just_proved_why_just_review/
|
||||
|
||||
### R14 (score:65) — r/PromptEngineering
|
||||
**i tested 47 AI tools in 90 days. here's the honest tier list nobody writes**
|
||||
- 评分：158👍 | 70💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1s2r59p/i_tested_47_ai_tools_in_90_days_heres_the_honest/
|
||||
|
||||
### R21 (score:64) — r/techforlife
|
||||
**What AI tools are you actually using daily right now?**
|
||||
- 评分：69👍 | 46💬
|
||||
- 链接：https://www.reddit.com/r/techforlife/comments/1s2hpb1/what_ai_tools_are_you_actually_using_daily_right/
|
||||
|
||||
### R23 (score:62) — r/AIToolsAndTips
|
||||
**What are the most actually useful Ai tools you use daily?**
|
||||
- 评分：26👍 | 57💬
|
||||
- 链接：https://www.reddit.com/r/AIToolsAndTips/comments/1s207ta/what_are_the_most_actually_useful_ai_tools_you/
|
||||
|
||||
### R4 (score:62) — r/PromptEngineering
|
||||
**Google has been releasing a bunch of free AI tools outside of the main Gemini app. Most are buried in Google Labs**
|
||||
- 评分：2649👍 | 159💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1rpueo8/google_has_been_releasing_a_bunch_of_free_ai/
|
||||
- 热评含链接：
|
||||
- Learn your way: https://learnyourway.withgoogle.com/
|
||||
- Lumiere: https://lumiere-video.github.io
|
||||
- Whisk: https://labs.go...
|
||||
|
||||
### R11 (score:61) — r/PromptEngineering
|
||||
**Google's NotebookLM is still the most slept-on free AI tool in 2026 and i don't get why**
|
||||
- 评分：458👍 | 81💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1rvhlf3/googles_notebooklm_is_still_the_most_slepton_free/
|
||||
|
||||
### R5 (score:61) — r/tifu
|
||||
**TIFU by nailing my first professional interview but instantly ruining it when the hiring manager asked about salary**
|
||||
- 评分：2471👍 | 268💬
|
||||
- 链接：https://www.reddit.com/r/tifu/comments/1rmwldm/tifu_by_nailing_my_first_professional_interview/
|
||||
- 热评："What did your mom say?" (3356 upvotes)
|
||||
|
||||
### R13 (score:60) — r/PromptEngineering
|
||||
**The free AI stack i use to run my entire workflow in 2026 (no paid tools, no subscriptions)**
|
||||
- 评分：173👍 | 30💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1rxbq27/the_free_ai_stack_i_use_to_run_my_entire_workflow/
|
||||
|
||||
### R16 (score:59) — r/ArtificialInteligence
|
||||
**I tested 40+ AI tools this month. Here are 5 that are actually worth your time**
|
||||
- 评分：121👍 | 67💬
|
||||
- 链接：https://www.reddit.com/r/ArtificialInteligence/comments/1rw7hth/i_tested_40_ai_tools_this_month_here_are_5_that/
|
||||
|
||||
### R2 (score:59) — r/ChatGPT
|
||||
**And so…**
|
||||
- 评分：5691👍 | 151💬
|
||||
- 链接：https://www.reddit.com/r/ChatGPT/comments/1rl99ca/and_so/
|
||||
- 热评："Also ChatGPT: 'You're not crazy. You're solution-oriented. And that's rare.'" (396 upvotes)
|
||||
|
||||
### R20 (score:58) — r/BlackboxAI_
|
||||
**Anthropic just dropped 'Code Review' tool to check the flood of AI-generated code**
|
||||
- 评分：72👍 | 57💬
|
||||
- 链接：https://www.reddit.com/r/BlackboxAI_/comments/1rvvir1/anthropic_just_dropped_code_review_tool_to_check/
|
||||
|
||||
---
|
||||
|
||||
## X (Twitter) 热门推文（20条）
|
||||
|
||||
### X6 (score:82) @socialwithaayan — 2026-03-29
|
||||
**This open-source AI skill just replaced hours of manual research for thousands of developers. It is called last30days-skill**
|
||||
- 👍33 | 🔁11
|
||||
- 链接：https://x.com/socialwithaayan/status/2038247173654213105
|
||||
|
||||
### X13 (score:76) @HuaxiuYaoML — 2026-03-15
|
||||
**Everyone's excited about Karpathy's autoresearch... Meet AutoResearchClaw: one message in, full conference paper out**
|
||||
- 👍1475 | 🔁203
|
||||
- 链接：https://x.com/HuaxiuYaoML/status/2033038170653405308
|
||||
|
||||
### X4 (score:73) @RoyAmal — 2026-03-25
|
||||
**most AI answers are outdated. this fixes that. it's a skill that researches what's happening right now... /last30days [topic]**
|
||||
- 链接：https://x.com/RoyAmal/status/2036827050691211636
|
||||
|
||||
### X20 (score:72) @newlinedotco — 2026-03-27
|
||||
**If you're diving into AI content creation, /last30days helps with prompt research**
|
||||
- 链接：https://x.com/newlinedotco/status/2037379808313610469
|
||||
|
||||
### X14 (score:71) @godofprompt — 2026-03-11
|
||||
**BREAKING: Perplexity Computer just became the most dangerous tool on a public market desk**
|
||||
- 👍922 | 🔁113
|
||||
- 链接：https://x.com/godofprompt/status/2031785580678516850
|
||||
|
||||
### X11 (score:70) @milesdeutscher — 2026-03-15
|
||||
**Perplexity Computer is fast becoming my most-used AI agent for research**
|
||||
- 👍383 | 🔁22
|
||||
- 链接：https://x.com/milesdeutscher/status/2033241180624867574
|
||||
|
||||
### X19 (score:69) @MindBranches — 2026-03-22
|
||||
**How to Master AI in 30 Days (The Exact Roadmap)**
|
||||
- 👍823 | 🔁117
|
||||
- 链接：https://x.com/MindBranches/status/2035530753598017680
|
||||
|
||||
### X3 (score:68) @Nikokow — 2026-03-27
|
||||
**last30days is an AI agent skill that researches any topic using only the past 30 days of data**
|
||||
- 链接：https://x.com/Nikokow/status/2037461628527009840
|
||||
|
||||
### X12 (score:68) @ellen_in_sf — 2026-03-13
|
||||
**I only started doing AI research 9 months ago... I built autoresearch-gen**
|
||||
- 👍691 | 🔁65
|
||||
- 链接：https://x.com/ellen_in_sf/status/2032538112971444436
|
||||
|
||||
### X7 (score:67) @AI_Aravind — 2026-03-28
|
||||
**The last30days skill turns 30 days of web chatter into a grounded briefing**
|
||||
- 链接：https://x.com/AI_Aravind/status/2037749979276890450
|
||||
|
||||
### X1 (score:67) @newlinedotco — 2026-03-27
|
||||
**I just stumbled upon an AI research tool that keeps you updated with the latest discussions across multiple platforms. It's called /last30days**
|
||||
- 🔁1
|
||||
- 链接：https://x.com/newlinedotco/status/2037379661747921352
|
||||
|
||||
### X9 (score:67) @sunsetsyntax — 2026-03-26
|
||||
**Tired of AI research that's full of hallucinations and outdated info? Meet last30days**
|
||||
- 链接：https://x.com/sunsetsyntax/status/2037101457166508369
|
||||
|
||||
### X2 (score:67) @f_aswadi — 2026-03-05
|
||||
**جوجل لم يعد كافياً للمطورين... /last30days by Matt Van Horn**
|
||||
- 👍236 | 🔁19
|
||||
- 链接：https://x.com/f_aswadi/status/2029667349343686999
|
||||
|
||||
### X10 (score:65) @davemorin — 2026-03-05
|
||||
**This AI research tool @mvanhorn built is really good. I use it every day**
|
||||
- 👍193 | 🔁15
|
||||
- 链接：https://x.com/davemorin/status/2029438897479176381
|
||||
|
||||
### X8 (score:64) @AI_Tools01 — 2026-03-28
|
||||
**GitHub Trending: last30days-skill - AI Research Tool (11.9k⭐)**
|
||||
- 链接：https://x.com/AI_Tools01/status/2037722016758050823
|
||||
|
||||
---
|
||||
|
||||
## YouTube 热门视频（8条，5条有字幕）
|
||||
|
||||
### qb90PPbAWz4 — Greg Isenberg（2026-03-11）
|
||||
**Karpathy's "autoresearch" broke the internet**
|
||||
- 👁️81,063 views | 👍2,100
|
||||
- 链接：https://www.youtube.com/watch?v=qb90PPbAWz4
|
||||
- 字幕摘要：Andre Karpathy, one of the godfathers of AI, launched "auto research" and it's going viral on Twitter
|
||||
|
||||
### fKjOKFpJYwI — biomedical analysis（2026-03-20）
|
||||
**I Tried 100 AI Tools… These 5 Are All You Need**
|
||||
- 👁️56 views | 👍0
|
||||
- 链接：https://www.youtube.com/watch?v=fKjOKFpJYwI
|
||||
|
||||
### HmwYja4ob8w — AutoContent API（2026-03-06）
|
||||
**Codex Security Research Preview: AI Agent Slashes Alert Noise 84% and Finds 792 Critical Flaws**
|
||||
- 👁️185 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=HmwYja4ob8w
|
||||
- 字幕摘要：Major development in application security - AI agent that can fundamentally change how developers and security teams work
|
||||
|
||||
### -7Bnab8I0g8 — Naveed Ganatra（2026-03-09）
|
||||
**Google Search Console AI Tool Explained (2026) | New AI Feature for SEO Analysis & GSC Reports**
|
||||
- 👁️37 views | 👍0
|
||||
- 链接：https://www.youtube.com/watch?v=-7Bnab8I0g8
|
||||
|
||||
### 4czkIAU1HVk — Artificial Intelligence Tools for Academics（2026-03-19）
|
||||
**Artificial Intelligence Is Replacing Jobs Right Now. Here's What Nobody Tells You**
|
||||
- 👁️126 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=4czkIAU1HVk
|
||||
|
||||
### GdjH6sPT1LQ — Dr. Alvaro Cintas（2026-03-06）
|
||||
**7 Secret Google AI Tools That Are 100% FREE (Destroys All Paid Alternatives)**
|
||||
- 👁️259 views | 👍16
|
||||
- 链接：https://www.youtube.com/watch?v=GdjH6sPT1LQ
|
||||
- 字幕摘要：Spent the last month testing every free AI tool Google has released - genuinely shocked these are free and better than apps costing $20-50/month
|
||||
|
||||
### yxFK-S_WZvY — Build With AI（2026-03-26）
|
||||
**Trending Github: last30days, fast whisper, geosentinel, feynman, tscircuit, geoai, modly (Ep. #10)**
|
||||
- 👁️93 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=yxFK-S_WZvY
|
||||
|
||||
### 66-OQIukmR8 — Build With AI（2026-03-24）
|
||||
**Trending Github: DeepCamera, MoneyPrinterTurbo, deer-flow, supermemory, editor, & more**
|
||||
- 👁️135 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=66-OQIukmR8
|
||||
- 字幕摘要：12 rising AI and tech projects on GitHub - Deep Camera turns home/office security cameras into local AI guard
|
||||
|
||||
---
|
||||
|
||||
## TikTok 热门视频（10条）
|
||||
|
||||
### TK3 (score:66) @future.with.ai98 — 2026-03-26
|
||||
**Most people use OpenClaw wrong 👀 With the Last 30 Days skill**
|
||||
- 👁️2,632 views | 👍77
|
||||
- 链接：https://www.tiktok.com/@future.with.ai98/video/7621441910820932871
|
||||
- 标签：#ai #openclaw #claude #aiagents #juliangoldie
|
||||
|
||||
### TK2 (score:65) @rtctutorials — 2026-03-12
|
||||
**This AI Tool Just Made Research Papers 10x Easier**
|
||||
- 👁️4,172 views | 👍128
|
||||
- 链接：https://www.tiktok.com/@rtctutorials/video/7616358708800490772
|
||||
- 标签：#reseaai #AI #aitools #Humanizer #BuildWithAI
|
||||
|
||||
### TK1 (score:64) @taki.gpt — 2026-03-14
|
||||
**Every day AI changes - here's how I keep up without newsletters, Twitter, or YouTube recaps**
|
||||
- 👁️22,895 views | 👍885
|
||||
- 链接：https://www.tiktok.com/@taki.gpt/video/7616923581003386128
|
||||
- 标签：#claude #ai #news #productivity #aiagent
|
||||
|
||||
### TK4 (score:64) @mikemeansbusiness.ai — 2026-03-14
|
||||
**Staying ahead in AI doesn't mean drowning in newsletters or burning hours scrolling**
|
||||
- 👁️1,436 views | 👍61
|
||||
- 链接：https://www.tiktok.com/@mikemeansbusiness.ai/video/7616916372055985430
|
||||
- 标签：#ai #aitools #claudecode #automation #productivity
|
||||
|
||||
### TK8 (score:63) @jgoldieseo — 2026-03-28
|
||||
**Your AI agent can now monitor Reddit for breaking trends automatically**
|
||||
- 👁️115 views | 👍6
|
||||
- 链接：https://www.tiktok.com/@jgoldieseo/video/7622139337064336654
|
||||
- 标签：#claudeai #openclaw #aiworkflow #automation #aitools
|
||||
|
||||
### TK9 (score:55) @products.that.wor7 — 2026-03-30
|
||||
**Stop spending Sunday nights planning content. AI prompt built a full 30-day content calendar in 10 seconds**
|
||||
- 👁️115 views | 👍2
|
||||
- 链接：https://www.tiktok.com/@products.that.wor7/video/7622857878260518174
|
||||
- 标签：#contentcalendar #socialmediatips #aitools #smallbusiness #chatgpt
|
||||
|
||||
### TK7 (score:54) @futurecodex_ — 2026-03-28
|
||||
**I tested 15 free AI tools for 30 days Pro: It cites sources, so I stopped fact-checking everything twice**
|
||||
- 👁️130 views | 👍3
|
||||
- 链接：https://www.tiktok.com/@futurecodex_/video/7622272570317999373
|
||||
- 标签：#AITools #TikTokTech #FutureCodex #LearnOnTikTok #AITips #Productivity
|
||||
|
||||
### TK6 (score:53) @ananya.chadha1 — 2026-03-17
|
||||
**Claude AI made my 30-day content plan in under 5 mins 🤯**
|
||||
- 👁️229 views | 👍30
|
||||
- 链接：https://www.tiktok.com/@ananya.chadha1/video/7618110742818000145
|
||||
- 标签：#claudeai #contentcreator #contentplan #aitools #creatortips
|
||||
|
||||
### TK5 (score:42) @futurecodex_ — 2026-03-28
|
||||
**ChatGPT vs Claude for writing — the winner shocked me**
|
||||
- 👁️236 views | 👍5
|
||||
- 链接：https://www.tiktok.com/@futurecodex_/video/7622324775360269581
|
||||
- 标签：#AITools #TikTokTech #FutureCodex #LearnOnTikTok #AITips
|
||||
|
||||
### TK10 (score:31) @herbertowens4 — 2026-03-11
|
||||
**THE SCIENTIST BUNDLE - 30-Day AI Roadmap + Master Prompt Library**
|
||||
- 👁️94 views | 👍1
|
||||
- 链接：https://www.tiktok.com/@herbertowens4/video/7615856929117605134
|
||||
- 标签：#AI #Roadmap #Scientist #AIgenerated
|
||||
|
||||
---
|
||||
|
||||
## Polymarket 预测市场（8个相关市场）
|
||||
|
||||
### PM5 — Which company has the best AI model end of March 2026?
|
||||
- 交易量：$10.5M | 流动性：$2.3M
|
||||
- 结果：Anthropic 100%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-best-ai-model-end-of-march-751
|
||||
|
||||
### PM3 — Will xAI have the best AI model at the end of June 2026?
|
||||
- 交易量：$1.8M | 流动性：$452K
|
||||
- 结果：Anthropic 66% | Google 24% | OpenAI 8%
|
||||
- 较上周：↓4.0%
|
||||
- 链接：https://polymarket.com/event/which-company-has-best-ai-model-end-of-june
|
||||
|
||||
### PM6 — Will DeepSeek have the best AI model at the end of April 2026?
|
||||
- 交易量：$2.7M | 流动性：$451K
|
||||
- 结果：Anthropic 90% | Google 4% | OpenAI 2%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-best-ai-model-end-of-april
|
||||
|
||||
### PM2 — Will xAI have a #1 AI model by June 30?
|
||||
- 交易量：$728K | 流动性：$74K
|
||||
- 结果：OpenAI 30% | xAI 15% | Alibaba 10%
|
||||
- 较上月：↓15.0%
|
||||
- 链接：https://polymarket.com/event/which-companies-will-have-a-1-ai-model-by-june-30
|
||||
|
||||
### PM7 — Will DeepSeek have the best AI model for coding on March 31?
|
||||
- 交易量：$607K | 流动性：$170K
|
||||
- 结果：OpenAI 99% | Anthropic 1%
|
||||
- 较上周：↑1.2%
|
||||
- 链接：https://polymarket.com/event/which-company-will-have-the-best-ai-model-for-coding-on-march-31
|
||||
|
||||
### PM4 — Will Google have the best AI model for math on March 31?
|
||||
- 交易量：$343K | 流动性：$62K
|
||||
- 结果：OpenAI 100%
|
||||
- 较上周：↓8.5%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-best-ai-model-for-math-on-march-31
|
||||
|
||||
### PM9 — Will Mistral have the top AI model at the end of March 2026?
|
||||
- 交易量：$929K | 流动性：$112K
|
||||
- 结果：Anthropic 98% | xAI 1%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-top-ai-model-end-of-march-style-control-on
|
||||
|
||||
### PM15 — Will Big AI be out as #1 Free App in US Apple Store by April 15?
|
||||
- 交易量：$887 | 流动性：$6K
|
||||
- 链接：https://polymarket.com/event/big-ai-out-as-1-free-app-in-the-us-apple-app-store-by-426
|
||||
|
||||
---
|
||||
|
||||
## 数据统计
|
||||
|
||||
- ✅ Reddit：90 条帖子
|
||||
- ✅ X：20 条推文
|
||||
- ✅ YouTube：8 条视频（5条有字幕）
|
||||
- ✅ TikTok：10 条视频（5条有字幕）
|
||||
- ❌ Instagram：获取失败
|
||||
- ✅ Polymarket：8 个相关市场
|
||||
- 🌐 Web：见上方搜索结果
|
||||
|
||||
---
|
||||
|
||||
## 相关资源链接
|
||||
|
||||
- GitHub：https://github.com/mvanhorn/last30days-skill
|
||||
- Smithery：https://smithery.ai/skills/openclaw/last30days
|
||||
- VoltAgent Awesome List：https://github.com/VoltAgent/awesome-openclaw-skills
|
||||
|
||||
---
|
||||
|
||||
*本文件由星辉自动生成并保存（原始数据未经摘要处理）*
|
||||
---
|
||||
title: last30days AI Research Tool — 原始研究数据
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: [AI, AITips, AITools, AIgenerated, BuildWithAI, FutureCodex, Humanizer, LearnOnTikTok, Productivity, Roadmap, Scientist, TikTokTech, ai, aiagent, aiagents, aitools, aiworkflow, automation, chatgpt, claude, claudeai, claudecode, contentcalendar, contentcreator, contentplan, creatortips, juliangoldie, news, openclaw, productivity, reseaai, smallbusiness, socialmediatips]
|
||||
---
|
||||
|
||||
# last30days AI Research Tool — 原始研究数据
|
||||
|
||||
> 生成时间：2026-03-30
|
||||
> 数据范围：2026-02-28 至 2026-03-30（过去30天）
|
||||
> 主题：last30days AI research tool
|
||||
> 模型：OpenAI GPT-5.2 / xAI Grok-4-1-fast
|
||||
|
||||
---
|
||||
|
||||
## Reddit 热门讨论（90条帖子）
|
||||
|
||||
### R1 (score:78) — r/popculturechat
|
||||
**This is a celebrity scandal from Germany that deserves international attention**
|
||||
- 评分：18311👍 | 916💬
|
||||
- 链接：https://www.reddit.com/r/popculturechat/comments/1ryqtw2/this_is_a_celebrity_scandal_from_germany_that/
|
||||
- 热评："sincerely what the fuck" (8823 upvotes)
|
||||
|
||||
### R7 (score:69) — r/ClaudeAI
|
||||
**Anthropic's research proves AI coding tools are secretly making developers worse**
|
||||
- 评分：1583👍 | 245💬
|
||||
- 链接：https://www.reddit.com/r/ClaudeAI/comments/1rzmfyd/anthropics_research_proves_ai_coding_tools_are/
|
||||
|
||||
### R8 (score:69) — r/singularity
|
||||
**OpenAI research team reveals its models go insane when given repetitive tasks**
|
||||
- 评分：1405👍 | 252💬
|
||||
- 链接：https://www.reddit.com/r/singularity/comments/1rzg4lk/openai_research_team_reveals_its_models_go_insane/
|
||||
|
||||
### R15 (score:68) — r/ArtificialInteligence
|
||||
**I tested what happens when you give an AI coding agent access to 2 million research papers**
|
||||
- 评分：130👍 | 43💬
|
||||
- 链接：https://www.reddit.com/r/ArtificialInteligence/comments/1s6bavl/i_tested_what_happens_when_you_give_an_ai_coding/
|
||||
|
||||
### R10 (score:67) — r/ArtificialInteligence
|
||||
**Wharton researchers just proved why "just review the AI output" doesn't work**
|
||||
- 评分：477👍 | 135💬
|
||||
- 链接：https://www.reddit.com/r/ArtificialInteligence/comments/1s154ig/wharton_researchers_just_proved_why_just_review/
|
||||
|
||||
### R14 (score:65) — r/PromptEngineering
|
||||
**i tested 47 AI tools in 90 days. here's the honest tier list nobody writes**
|
||||
- 评分：158👍 | 70💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1s2r59p/i_tested_47_ai_tools_in_90_days_heres_the_honest/
|
||||
|
||||
### R21 (score:64) — r/techforlife
|
||||
**What AI tools are you actually using daily right now?**
|
||||
- 评分：69👍 | 46💬
|
||||
- 链接：https://www.reddit.com/r/techforlife/comments/1s2hpb1/what_ai_tools_are_you_actually_using_daily_right/
|
||||
|
||||
### R23 (score:62) — r/AIToolsAndTips
|
||||
**What are the most actually useful Ai tools you use daily?**
|
||||
- 评分：26👍 | 57💬
|
||||
- 链接：https://www.reddit.com/r/AIToolsAndTips/comments/1s207ta/what_are_the_most_actually_useful_ai_tools_you/
|
||||
|
||||
### R4 (score:62) — r/PromptEngineering
|
||||
**Google has been releasing a bunch of free AI tools outside of the main Gemini app. Most are buried in Google Labs**
|
||||
- 评分：2649👍 | 159💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1rpueo8/google_has_been_releasing_a_bunch_of_free_ai/
|
||||
- 热评含链接：
|
||||
- Learn your way: https://learnyourway.withgoogle.com/
|
||||
- Lumiere: https://lumiere-video.github.io
|
||||
- Whisk: https://labs.go...
|
||||
|
||||
### R11 (score:61) — r/PromptEngineering
|
||||
**Google's NotebookLM is still the most slept-on free AI tool in 2026 and i don't get why**
|
||||
- 评分：458👍 | 81💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1rvhlf3/googles_notebooklm_is_still_the_most_slepton_free/
|
||||
|
||||
### R5 (score:61) — r/tifu
|
||||
**TIFU by nailing my first professional interview but instantly ruining it when the hiring manager asked about salary**
|
||||
- 评分：2471👍 | 268💬
|
||||
- 链接：https://www.reddit.com/r/tifu/comments/1rmwldm/tifu_by_nailing_my_first_professional_interview/
|
||||
- 热评："What did your mom say?" (3356 upvotes)
|
||||
|
||||
### R13 (score:60) — r/PromptEngineering
|
||||
**The free AI stack i use to run my entire workflow in 2026 (no paid tools, no subscriptions)**
|
||||
- 评分：173👍 | 30💬
|
||||
- 链接：https://www.reddit.com/r/PromptEngineering/comments/1rxbq27/the_free_ai_stack_i_use_to_run_my_entire_workflow/
|
||||
|
||||
### R16 (score:59) — r/ArtificialInteligence
|
||||
**I tested 40+ AI tools this month. Here are 5 that are actually worth your time**
|
||||
- 评分：121👍 | 67💬
|
||||
- 链接：https://www.reddit.com/r/ArtificialInteligence/comments/1rw7hth/i_tested_40_ai_tools_this_month_here_are_5_that/
|
||||
|
||||
### R2 (score:59) — r/ChatGPT
|
||||
**And so…**
|
||||
- 评分：5691👍 | 151💬
|
||||
- 链接：https://www.reddit.com/r/ChatGPT/comments/1rl99ca/and_so/
|
||||
- 热评："Also ChatGPT: 'You're not crazy. You're solution-oriented. And that's rare.'" (396 upvotes)
|
||||
|
||||
### R20 (score:58) — r/BlackboxAI_
|
||||
**Anthropic just dropped 'Code Review' tool to check the flood of AI-generated code**
|
||||
- 评分：72👍 | 57💬
|
||||
- 链接：https://www.reddit.com/r/BlackboxAI_/comments/1rvvir1/anthropic_just_dropped_code_review_tool_to_check/
|
||||
|
||||
---
|
||||
|
||||
## X (Twitter) 热门推文（20条）
|
||||
|
||||
### X6 (score:82) @socialwithaayan — 2026-03-29
|
||||
**This open-source AI skill just replaced hours of manual research for thousands of developers. It is called last30days-skill**
|
||||
- 👍33 | 🔁11
|
||||
- 链接：https://x.com/socialwithaayan/status/2038247173654213105
|
||||
|
||||
### X13 (score:76) @HuaxiuYaoML — 2026-03-15
|
||||
**Everyone's excited about Karpathy's autoresearch... Meet AutoResearchClaw: one message in, full conference paper out**
|
||||
- 👍1475 | 🔁203
|
||||
- 链接：https://x.com/HuaxiuYaoML/status/2033038170653405308
|
||||
|
||||
### X4 (score:73) @RoyAmal — 2026-03-25
|
||||
**most AI answers are outdated. this fixes that. it's a skill that researches what's happening right now... /last30days [topic]**
|
||||
- 链接：https://x.com/RoyAmal/status/2036827050691211636
|
||||
|
||||
### X20 (score:72) @newlinedotco — 2026-03-27
|
||||
**If you're diving into AI content creation, /last30days helps with prompt research**
|
||||
- 链接：https://x.com/newlinedotco/status/2037379808313610469
|
||||
|
||||
### X14 (score:71) @godofprompt — 2026-03-11
|
||||
**BREAKING: Perplexity Computer just became the most dangerous tool on a public market desk**
|
||||
- 👍922 | 🔁113
|
||||
- 链接：https://x.com/godofprompt/status/2031785580678516850
|
||||
|
||||
### X11 (score:70) @milesdeutscher — 2026-03-15
|
||||
**Perplexity Computer is fast becoming my most-used AI agent for research**
|
||||
- 👍383 | 🔁22
|
||||
- 链接：https://x.com/milesdeutscher/status/2033241180624867574
|
||||
|
||||
### X19 (score:69) @MindBranches — 2026-03-22
|
||||
**How to Master AI in 30 Days (The Exact Roadmap)**
|
||||
- 👍823 | 🔁117
|
||||
- 链接：https://x.com/MindBranches/status/2035530753598017680
|
||||
|
||||
### X3 (score:68) @Nikokow — 2026-03-27
|
||||
**last30days is an AI agent skill that researches any topic using only the past 30 days of data**
|
||||
- 链接：https://x.com/Nikokow/status/2037461628527009840
|
||||
|
||||
### X12 (score:68) @ellen_in_sf — 2026-03-13
|
||||
**I only started doing AI research 9 months ago... I built autoresearch-gen**
|
||||
- 👍691 | 🔁65
|
||||
- 链接：https://x.com/ellen_in_sf/status/2032538112971444436
|
||||
|
||||
### X7 (score:67) @AI_Aravind — 2026-03-28
|
||||
**The last30days skill turns 30 days of web chatter into a grounded briefing**
|
||||
- 链接：https://x.com/AI_Aravind/status/2037749979276890450
|
||||
|
||||
### X1 (score:67) @newlinedotco — 2026-03-27
|
||||
**I just stumbled upon an AI research tool that keeps you updated with the latest discussions across multiple platforms. It's called /last30days**
|
||||
- 🔁1
|
||||
- 链接：https://x.com/newlinedotco/status/2037379661747921352
|
||||
|
||||
### X9 (score:67) @sunsetsyntax — 2026-03-26
|
||||
**Tired of AI research that's full of hallucinations and outdated info? Meet last30days**
|
||||
- 链接：https://x.com/sunsetsyntax/status/2037101457166508369
|
||||
|
||||
### X2 (score:67) @f_aswadi — 2026-03-05
|
||||
**جوجل لم يعد كافياً للمطورين... /last30days by Matt Van Horn**
|
||||
- 👍236 | 🔁19
|
||||
- 链接：https://x.com/f_aswadi/status/2029667349343686999
|
||||
|
||||
### X10 (score:65) @davemorin — 2026-03-05
|
||||
**This AI research tool @mvanhorn built is really good. I use it every day**
|
||||
- 👍193 | 🔁15
|
||||
- 链接：https://x.com/davemorin/status/2029438897479176381
|
||||
|
||||
### X8 (score:64) @AI_Tools01 — 2026-03-28
|
||||
**GitHub Trending: last30days-skill - AI Research Tool (11.9k⭐)**
|
||||
- 链接：https://x.com/AI_Tools01/status/2037722016758050823
|
||||
|
||||
---
|
||||
|
||||
## YouTube 热门视频（8条，5条有字幕）
|
||||
|
||||
### qb90PPbAWz4 — Greg Isenberg（2026-03-11）
|
||||
**Karpathy's "autoresearch" broke the internet**
|
||||
- 👁️81,063 views | 👍2,100
|
||||
- 链接：https://www.youtube.com/watch?v=qb90PPbAWz4
|
||||
- 字幕摘要：Andre Karpathy, one of the godfathers of AI, launched "auto research" and it's going viral on Twitter
|
||||
|
||||
### fKjOKFpJYwI — biomedical analysis（2026-03-20）
|
||||
**I Tried 100 AI Tools… These 5 Are All You Need**
|
||||
- 👁️56 views | 👍0
|
||||
- 链接：https://www.youtube.com/watch?v=fKjOKFpJYwI
|
||||
|
||||
### HmwYja4ob8w — AutoContent API（2026-03-06）
|
||||
**Codex Security Research Preview: AI Agent Slashes Alert Noise 84% and Finds 792 Critical Flaws**
|
||||
- 👁️185 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=HmwYja4ob8w
|
||||
- 字幕摘要：Major development in application security - AI agent that can fundamentally change how developers and security teams work
|
||||
|
||||
### -7Bnab8I0g8 — Naveed Ganatra（2026-03-09）
|
||||
**Google Search Console AI Tool Explained (2026) | New AI Feature for SEO Analysis & GSC Reports**
|
||||
- 👁️37 views | 👍0
|
||||
- 链接：https://www.youtube.com/watch?v=-7Bnab8I0g8
|
||||
|
||||
### 4czkIAU1HVk — Artificial Intelligence Tools for Academics（2026-03-19）
|
||||
**Artificial Intelligence Is Replacing Jobs Right Now. Here's What Nobody Tells You**
|
||||
- 👁️126 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=4czkIAU1HVk
|
||||
|
||||
### GdjH6sPT1LQ — Dr. Alvaro Cintas（2026-03-06）
|
||||
**7 Secret Google AI Tools That Are 100% FREE (Destroys All Paid Alternatives)**
|
||||
- 👁️259 views | 👍16
|
||||
- 链接：https://www.youtube.com/watch?v=GdjH6sPT1LQ
|
||||
- 字幕摘要：Spent the last month testing every free AI tool Google has released - genuinely shocked these are free and better than apps costing $20-50/month
|
||||
|
||||
### yxFK-S_WZvY — Build With AI（2026-03-26）
|
||||
**Trending Github: last30days, fast whisper, geosentinel, feynman, tscircuit, geoai, modly (Ep. #10)**
|
||||
- 👁️93 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=yxFK-S_WZvY
|
||||
|
||||
### 66-OQIukmR8 — Build With AI（2026-03-24）
|
||||
**Trending Github: DeepCamera, MoneyPrinterTurbo, deer-flow, supermemory, editor, & more**
|
||||
- 👁️135 views | 👍3
|
||||
- 链接：https://www.youtube.com/watch?v=66-OQIukmR8
|
||||
- 字幕摘要：12 rising AI and tech projects on GitHub - Deep Camera turns home/office security cameras into local AI guard
|
||||
|
||||
---
|
||||
|
||||
## TikTok 热门视频（10条）
|
||||
|
||||
### TK3 (score:66) @future.with.ai98 — 2026-03-26
|
||||
**Most people use OpenClaw wrong 👀 With the Last 30 Days skill**
|
||||
- 👁️2,632 views | 👍77
|
||||
- 链接：https://www.tiktok.com/@future.with.ai98/video/7621441910820932871
|
||||
- 标签：#ai #openclaw #claude #aiagents #juliangoldie
|
||||
|
||||
### TK2 (score:65) @rtctutorials — 2026-03-12
|
||||
**This AI Tool Just Made Research Papers 10x Easier**
|
||||
- 👁️4,172 views | 👍128
|
||||
- 链接：https://www.tiktok.com/@rtctutorials/video/7616358708800490772
|
||||
- 标签：#reseaai #AI #aitools #Humanizer #BuildWithAI
|
||||
|
||||
### TK1 (score:64) @taki.gpt — 2026-03-14
|
||||
**Every day AI changes - here's how I keep up without newsletters, Twitter, or YouTube recaps**
|
||||
- 👁️22,895 views | 👍885
|
||||
- 链接：https://www.tiktok.com/@taki.gpt/video/7616923581003386128
|
||||
- 标签：#claude #ai #news #productivity #aiagent
|
||||
|
||||
### TK4 (score:64) @mikemeansbusiness.ai — 2026-03-14
|
||||
**Staying ahead in AI doesn't mean drowning in newsletters or burning hours scrolling**
|
||||
- 👁️1,436 views | 👍61
|
||||
- 链接：https://www.tiktok.com/@mikemeansbusiness.ai/video/7616916372055985430
|
||||
- 标签：#ai #aitools #claudecode #automation #productivity
|
||||
|
||||
### TK8 (score:63) @jgoldieseo — 2026-03-28
|
||||
**Your AI agent can now monitor Reddit for breaking trends automatically**
|
||||
- 👁️115 views | 👍6
|
||||
- 链接：https://www.tiktok.com/@jgoldieseo/video/7622139337064336654
|
||||
- 标签：#claudeai #openclaw #aiworkflow #automation #aitools
|
||||
|
||||
### TK9 (score:55) @products.that.wor7 — 2026-03-30
|
||||
**Stop spending Sunday nights planning content. AI prompt built a full 30-day content calendar in 10 seconds**
|
||||
- 👁️115 views | 👍2
|
||||
- 链接：https://www.tiktok.com/@products.that.wor7/video/7622857878260518174
|
||||
- 标签：#contentcalendar #socialmediatips #aitools #smallbusiness #chatgpt
|
||||
|
||||
### TK7 (score:54) @futurecodex_ — 2026-03-28
|
||||
**I tested 15 free AI tools for 30 days Pro: It cites sources, so I stopped fact-checking everything twice**
|
||||
- 👁️130 views | 👍3
|
||||
- 链接：https://www.tiktok.com/@futurecodex_/video/7622272570317999373
|
||||
- 标签：#AITools #TikTokTech #FutureCodex #LearnOnTikTok #AITips #Productivity
|
||||
|
||||
### TK6 (score:53) @ananya.chadha1 — 2026-03-17
|
||||
**Claude AI made my 30-day content plan in under 5 mins 🤯**
|
||||
- 👁️229 views | 👍30
|
||||
- 链接：https://www.tiktok.com/@ananya.chadha1/video/7618110742818000145
|
||||
- 标签：#claudeai #contentcreator #contentplan #aitools #creatortips
|
||||
|
||||
### TK5 (score:42) @futurecodex_ — 2026-03-28
|
||||
**ChatGPT vs Claude for writing — the winner shocked me**
|
||||
- 👁️236 views | 👍5
|
||||
- 链接：https://www.tiktok.com/@futurecodex_/video/7622324775360269581
|
||||
- 标签：#AITools #TikTokTech #FutureCodex #LearnOnTikTok #AITips
|
||||
|
||||
### TK10 (score:31) @herbertowens4 — 2026-03-11
|
||||
**THE SCIENTIST BUNDLE - 30-Day AI Roadmap + Master Prompt Library**
|
||||
- 👁️94 views | 👍1
|
||||
- 链接：https://www.tiktok.com/@herbertowens4/video/7615856929117605134
|
||||
- 标签：#AI #Roadmap #Scientist #AIgenerated
|
||||
|
||||
---
|
||||
|
||||
## Polymarket 预测市场（8个相关市场）
|
||||
|
||||
### PM5 — Which company has the best AI model end of March 2026?
|
||||
- 交易量：$10.5M | 流动性：$2.3M
|
||||
- 结果：Anthropic 100%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-best-ai-model-end-of-march-751
|
||||
|
||||
### PM3 — Will xAI have the best AI model at the end of June 2026?
|
||||
- 交易量：$1.8M | 流动性：$452K
|
||||
- 结果：Anthropic 66% | Google 24% | OpenAI 8%
|
||||
- 较上周：↓4.0%
|
||||
- 链接：https://polymarket.com/event/which-company-has-best-ai-model-end-of-june
|
||||
|
||||
### PM6 — Will DeepSeek have the best AI model at the end of April 2026?
|
||||
- 交易量：$2.7M | 流动性：$451K
|
||||
- 结果：Anthropic 90% | Google 4% | OpenAI 2%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-best-ai-model-end-of-april
|
||||
|
||||
### PM2 — Will xAI have a #1 AI model by June 30?
|
||||
- 交易量：$728K | 流动性：$74K
|
||||
- 结果：OpenAI 30% | xAI 15% | Alibaba 10%
|
||||
- 较上月：↓15.0%
|
||||
- 链接：https://polymarket.com/event/which-companies-will-have-a-1-ai-model-by-june-30
|
||||
|
||||
### PM7 — Will DeepSeek have the best AI model for coding on March 31?
|
||||
- 交易量：$607K | 流动性：$170K
|
||||
- 结果：OpenAI 99% | Anthropic 1%
|
||||
- 较上周：↑1.2%
|
||||
- 链接：https://polymarket.com/event/which-company-will-have-the-best-ai-model-for-coding-on-march-31
|
||||
|
||||
### PM4 — Will Google have the best AI model for math on March 31?
|
||||
- 交易量：$343K | 流动性：$62K
|
||||
- 结果：OpenAI 100%
|
||||
- 较上周：↓8.5%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-best-ai-model-for-math-on-march-31
|
||||
|
||||
### PM9 — Will Mistral have the top AI model at the end of March 2026?
|
||||
- 交易量：$929K | 流动性：$112K
|
||||
- 结果：Anthropic 98% | xAI 1%
|
||||
- 链接：https://polymarket.com/event/which-company-has-the-top-ai-model-end-of-march-style-control-on
|
||||
|
||||
### PM15 — Will Big AI be out as #1 Free App in US Apple Store by April 15?
|
||||
- 交易量：$887 | 流动性：$6K
|
||||
- 链接：https://polymarket.com/event/big-ai-out-as-1-free-app-in-the-us-apple-app-store-by-426
|
||||
|
||||
---
|
||||
|
||||
## 数据统计
|
||||
|
||||
- ✅ Reddit：90 条帖子
|
||||
- ✅ X：20 条推文
|
||||
- ✅ YouTube：8 条视频（5条有字幕）
|
||||
- ✅ TikTok：10 条视频（5条有字幕）
|
||||
- ❌ Instagram：获取失败
|
||||
- ✅ Polymarket：8 个相关市场
|
||||
- 🌐 Web：见上方搜索结果
|
||||
|
||||
---
|
||||
|
||||
## 相关资源链接
|
||||
|
||||
- GitHub：https://github.com/mvanhorn/last30days-skill
|
||||
- Smithery：https://smithery.ai/skills/openclaw/last30days
|
||||
- VoltAgent Awesome List：https://github.com/VoltAgent/awesome-openclaw-skills
|
||||
|
||||
---
|
||||
|
||||
*本文件由星辉自动生成并保存（原始数据未经摘要处理）*
|
||||
|
||||
@@ -1,186 +1,186 @@
|
||||
---
|
||||
title: last30days skill 应用场景 — X (Twitter) 原始数据
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# last30days skill 应用场景 — X (Twitter) 原始数据
|
||||
|
||||
> 生成时间：2026-03-30
|
||||
> 数据范围：2026-02-28 至 2026-03-30
|
||||
> 主题：last30days skill applications use cases
|
||||
> 平台：X (Twitter) 为主
|
||||
|
||||
---
|
||||
|
||||
## X (Twitter) 热门推文（21条）
|
||||
|
||||
### X16 (score:95) @Jason23818126 — 2026-03-28
|
||||
**信息差，依然是这个时代非常赚钱的方式之一... 本周狂涨 7,349 Stars... last30days-skill，恰好把这件事做透了**
|
||||
- 👍1812 | 🔁455
|
||||
- 链接：https://x.com/Jason23818126/status/2037830783969484898
|
||||
- 要点：分析 last30days-skill 用于信息差套利和趋势检测用例
|
||||
|
||||
### X11 (score:94) @GitTrend0x — 2026-03-27
|
||||
**今天 GitHub 被 Agent 军团屠榜了！5 个星标暴增最狠的项目... 1. mvanhorn/last30days-skill 你的私人全网情报官**
|
||||
- 👍2119 | 🔁541
|
||||
- 链接：https://x.com/GitTrend0x/status/2037352914227011868
|
||||
- 要点：将 last30days-skill 定位为顶级 Trending GitHub 项目，用于情报收集
|
||||
|
||||
### X14 (score:89) @aiedge_ — 2026-03-25
|
||||
**This is literal cheating. Someone built a Claude Code skill that scans Reddit and X from the last 30 days on any topic you give it, then writes you copy-paste-ready prompts**
|
||||
- 👍932 | 🔁73
|
||||
- 链接：https://x.com/aiedge_/status/2036639228101337210
|
||||
- 要点：强调 last30days-skill 用于社区来源的提示词技巧研究
|
||||
|
||||
### X17 (score:86) @NFTCPS — 2026-03-28
|
||||
**想靠信息差赚钱但不知道去哪挖？这个工具一次扫10个平台... last30days-skill，目前GitHub Trending第一**
|
||||
- 👍357 | 🔁95
|
||||
- 链接：https://x.com/NFTCPS/status/2037855232856850486
|
||||
- 要点：推广 last30days-skill 用于跨平台趋势分析和变现机会
|
||||
|
||||
### X15 (score:78) @socialwithaayan — 2026-03-29
|
||||
**This open-source AI skill just replaced hours of manual research... It is called last30days-skill. Give it any topic and it instantly researches the last 30 days**
|
||||
- 👍34 | 🔁11
|
||||
- 链接：https://x.com/socialwithaayan/status/2038247173654213105
|
||||
- 要点：详细介绍 last30days-skill 在多平台研究综合方面的应用
|
||||
|
||||
### X1 (score:74) @naldorp — 2026-03-29
|
||||
**o last30days-skill é um agente de AI que pesquisa qualquer assunto nos últimos 30 dias: Reddit, X, YouTube, Hacker News, Polymarket... e sintetiza em relatório. 1.680 stars só hoje no github**
|
||||
- 链接：https://x.com/naldorp/status/2038229808308429146
|
||||
- 要点：描述 last30days-skill 跨多平台30天研究并综合报告的功能
|
||||
|
||||
### X9 (score:73) @RecruitmentPq — 2026-03-18
|
||||
**Here's what actually works at 30 with no degree: Tech Sales... Digital Marketing... Cybersecurity... Every single one of these starts from zero. 30 is not late**
|
||||
- 👍1383 | 🔁163
|
||||
- 链接：https://x.com/RecruitmentPq/status/2034131410311643314
|
||||
- 要点：提供职业技趌应用和无学历入口路径
|
||||
|
||||
### X10 (score:73) @segoslavia — 2026-03-16
|
||||
**Another day to remind you to build the right skills. - Data Center Operations - AI deployment and optimization - Prompt engineering - IAM - API Security**
|
||||
- 👍2108 | 🔁290
|
||||
- 链接：https://x.com/segoslavia/status/2033669240914419823
|
||||
- 要点：推荐关键网络安全和 AI 相关技能
|
||||
|
||||
### X3 (score:72) @dimzhuk — 2026-03-25
|
||||
**last30days-skill just gained 1,342 stars today. An AI agent skill that researches any topic. Scans Reddit, X, YouTube, HN, and Polymarket. Then synthesizes a grounded summary**
|
||||
- 链接：https://x.com/dimzhuk/status/2036821040119119942
|
||||
- 要点：强调 last30days-skill 近期研究的快速增长和核心功能
|
||||
|
||||
### X18 (score:71) @trending_repos — 2026-03-27
|
||||
**Trending repository of the day: last30days-skill AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web**
|
||||
- 👍34 | 🔁4
|
||||
- 链接：https://x.com/trending_repos/status/2037469131323384204
|
||||
- 要点：GitHub Trending 帖子关于 last30days-skill 的研究能力
|
||||
|
||||
### X4 (score:71) @claudeai — 2026-03-11
|
||||
**Skills are also now available inside the Excel and PowerPoint add-ins. When your team has a standard workflow (like for running a variance analysis or building a client deck), save it as a skill**
|
||||
- 👍1441 | 🔁75
|
||||
- 链接：https://x.com/claudeai/status/2031790756156096881
|
||||
- 要点：官方发布关于 Office 工具中的技能集成和团队工作流用例
|
||||
|
||||
### X6 (score:69) @omarsar0 — 2026-03-14
|
||||
**Skills are so good when you combine them properly with MCP & CLIs. I have found that Skills can significantly improve tool usage**
|
||||
- 👍255 | 🔁55
|
||||
- 链接：https://x.com/omarsar0/status/2032928526022881399
|
||||
- 要点：探索将技能结合以获得更好的 AI agent 性能
|
||||
|
||||
### X22 (score:67) @shubhraaanshu — 2026-03-29
|
||||
**Real use cases: Extract product specs from manufacturer PDFs, Pull competitor pricing from web pages, Parse legal clauses from contracts**
|
||||
- 链接：https://x.com/shubhraaanshu/status/2038296658111574329
|
||||
- 要点：列出适用于 AI 技能的具体数据提取用例
|
||||
|
||||
### X19 (score:67) @aigclink — 2026-03-27
|
||||
**让AI帮你刷Reddit/YouTube/X的skill：last30days-skill 给一个话题，它会自动去各大社交平台搜最近30天的热门讨论**
|
||||
- 👍6 | 🔁1
|
||||
- 链接：https://x.com/aigclink/status/2037373609635946945
|
||||
- 要点：描述 last30days-skill 在市场研究和工具对比中的用例
|
||||
|
||||
### X7 (score:67) @GoSailGlobal — 2026-03-08
|
||||
**三天不到，开发好了agent skills hub（祝女神们节日快乐） 为什么开发这个产品？如何在众多的skills中找到合适的skills**
|
||||
- 👍306 | 🔁61
|
||||
- 链接：https://x.com/GoSailGlobal/status/2030490279221051487
|
||||
- 要点：介绍 agent skills hub 的分类和推荐用例
|
||||
|
||||
### X2 (score:67) @RoyAmal — 2026-03-25
|
||||
**most AI answers are outdated. this fixes that. it's a skill that researches what's happening right now... /last30days [topic] and it scans: reddit, X, youtube, hacker news, the web**
|
||||
- 链接：https://x.com/RoyAmal/status/2036827050691211636
|
||||
- 要点：将 /last30days 作为实时 AI 研究的工具推广
|
||||
|
||||
### X8 (score:67) @Nikokow — 2026-03-27
|
||||
**last30days is an AI agent skill that researches any topic using only the past 30 days of data. It pulls from Reddit, X, YouTube and more, then synthesizes real discussions**
|
||||
- 链接：https://x.com/Nikokow/status/2037461628527009840
|
||||
- 要点：描述 last30days 作为跨平台近期研究 AI agent 技能
|
||||
|
||||
### X12 (score:65) @davemorin — 2026-03-05
|
||||
**This AI research tool @mvanhorn built is really good. I use it every day**
|
||||
- 👍193 | 🔁15
|
||||
- 链接：https://x.com/davemorin/status/2029438897479176381
|
||||
- 要点：背书 last30days AI 研究工具
|
||||
|
||||
### X13 (score:64) @AI_Tools01 — 2026-03-28
|
||||
**GitHub Trending: last30days-skill - AI Research Tool (11.9k⭐)**
|
||||
- 链接：https://x.com/AI_Tools01/status/2037722016758050823
|
||||
- 要点：列出 last30days-skill 为顶级 GitHub Trending AI 研究工具
|
||||
|
||||
### X20 (score:63) @newlinedotco — 2026-03-27
|
||||
**I just stumbled upon an AI research tool that keeps you updated with the latest discussions across multiple platforms. It's called /last30days**
|
||||
- 🔁1
|
||||
- 链接：https://x.com/newlinedotco/status/2037379661747921352
|
||||
- 要点：介绍 /last30days 作为多平台最新讨论的 AI 研究工具
|
||||
|
||||
### X21 (score:61) @sunsetsyntax — 2026-03-26
|
||||
**Tired of AI research that's full of hallucinations and outdated info? Meet last30days: the open-source agent skill that pulls fresh data from Reddit, X, YouTube, HN, Polymarket**
|
||||
- 链接：https://x.com/sunsetsyntax/status/2037101457166508369
|
||||
- 要点：推广 last30days OSS 以获得最新、可靠的研究
|
||||
|
||||
---
|
||||
|
||||
## 相关 Reddit 讨论（93条，节选相关）
|
||||
|
||||
### R20 (score:69) r/claude — 2026-03-28
|
||||
**Best Claude Skills I use in 2026**
|
||||
- 评分：322👍 | 32💬
|
||||
- 链接：https://www.reddit.com/r/claude/comments/1s5qyef/best_claude_skills_i_use_in_2026/
|
||||
|
||||
### R16 (score:68) r/ClaudeCowork — 2026-03-25
|
||||
**6 skills i actually use every day**
|
||||
- 评分：791👍 | 28💬
|
||||
- 链接：https://www.reddit.com/r/ClaudeCowork/comments/1s3ljzh/6_skills_i_actually_use_every_day/
|
||||
|
||||
---
|
||||
|
||||
## YouTube 相关视频（20条，节选）
|
||||
|
||||
### 71ES9jzqa0Q — Greg Isenberg（2026-02-04）
|
||||
**The Claude Code Skill My Smartest Friends Use**
|
||||
- 👁️35,779 views | 👍764
|
||||
- 链接：https://www.youtube.com/watch?v=71ES9jzqa0Q
|
||||
|
||||
### qb90PPbAWz4 — Greg Isenberg（2026-03-11）
|
||||
**Karpathy's "autoresearch" broke the internet**
|
||||
- 👁️81,095 views | 👍2,101
|
||||
- 链接：https://www.youtube.com/watch?v=qb90PPbAWz4
|
||||
|
||||
### zKBPwDpBfhs — Nate Herk | AI Automation（2026-02-27）
|
||||
**Master 95% of Claude Code Skills in 28 Minutes**
|
||||
- 👁️118,348 views | 👍2,959
|
||||
- 链接：https://www.youtube.com/watch?v=zKBPwDpBfhs
|
||||
|
||||
---
|
||||
|
||||
## 数据统计
|
||||
|
||||
- ✅ X：21 条推文
|
||||
- ✅ Reddit：93 条帖子
|
||||
- ✅ YouTube：20 条视频
|
||||
- ✅ TikTok：2 条视频
|
||||
- 📊 Polymarket：7 个市场（非相关主题）
|
||||
|
||||
---
|
||||
|
||||
*本文件由星辉自动生成（原始数据未经处理）*
|
||||
---
|
||||
title: last30days skill 应用场景 — X (Twitter) 原始数据
|
||||
source:
|
||||
author: shenwei
|
||||
published:
|
||||
created:
|
||||
description:
|
||||
tags: []
|
||||
---
|
||||
|
||||
# last30days skill 应用场景 — X (Twitter) 原始数据
|
||||
|
||||
> 生成时间：2026-03-30
|
||||
> 数据范围：2026-02-28 至 2026-03-30
|
||||
> 主题：last30days skill applications use cases
|
||||
> 平台：X (Twitter) 为主
|
||||
|
||||
---
|
||||
|
||||
## X (Twitter) 热门推文（21条）
|
||||
|
||||
### X16 (score:95) @Jason23818126 — 2026-03-28
|
||||
**信息差，依然是这个时代非常赚钱的方式之一... 本周狂涨 7,349 Stars... last30days-skill，恰好把这件事做透了**
|
||||
- 👍1812 | 🔁455
|
||||
- 链接：https://x.com/Jason23818126/status/2037830783969484898
|
||||
- 要点：分析 last30days-skill 用于信息差套利和趋势检测用例
|
||||
|
||||
### X11 (score:94) @GitTrend0x — 2026-03-27
|
||||
**今天 GitHub 被 Agent 军团屠榜了！5 个星标暴增最狠的项目... 1. mvanhorn/last30days-skill 你的私人全网情报官**
|
||||
- 👍2119 | 🔁541
|
||||
- 链接：https://x.com/GitTrend0x/status/2037352914227011868
|
||||
- 要点：将 last30days-skill 定位为顶级 Trending GitHub 项目，用于情报收集
|
||||
|
||||
### X14 (score:89) @aiedge_ — 2026-03-25
|
||||
**This is literal cheating. Someone built a Claude Code skill that scans Reddit and X from the last 30 days on any topic you give it, then writes you copy-paste-ready prompts**
|
||||
- 👍932 | 🔁73
|
||||
- 链接：https://x.com/aiedge_/status/2036639228101337210
|
||||
- 要点：强调 last30days-skill 用于社区来源的提示词技巧研究
|
||||
|
||||
### X17 (score:86) @NFTCPS — 2026-03-28
|
||||
**想靠信息差赚钱但不知道去哪挖？这个工具一次扫10个平台... last30days-skill，目前GitHub Trending第一**
|
||||
- 👍357 | 🔁95
|
||||
- 链接：https://x.com/NFTCPS/status/2037855232856850486
|
||||
- 要点：推广 last30days-skill 用于跨平台趋势分析和变现机会
|
||||
|
||||
### X15 (score:78) @socialwithaayan — 2026-03-29
|
||||
**This open-source AI skill just replaced hours of manual research... It is called last30days-skill. Give it any topic and it instantly researches the last 30 days**
|
||||
- 👍34 | 🔁11
|
||||
- 链接：https://x.com/socialwithaayan/status/2038247173654213105
|
||||
- 要点：详细介绍 last30days-skill 在多平台研究综合方面的应用
|
||||
|
||||
### X1 (score:74) @naldorp — 2026-03-29
|
||||
**o last30days-skill é um agente de AI que pesquisa qualquer assunto nos últimos 30 dias: Reddit, X, YouTube, Hacker News, Polymarket... e sintetiza em relatório. 1.680 stars só hoje no github**
|
||||
- 链接：https://x.com/naldorp/status/2038229808308429146
|
||||
- 要点：描述 last30days-skill 跨多平台30天研究并综合报告的功能
|
||||
|
||||
### X9 (score:73) @RecruitmentPq — 2026-03-18
|
||||
**Here's what actually works at 30 with no degree: Tech Sales... Digital Marketing... Cybersecurity... Every single one of these starts from zero. 30 is not late**
|
||||
- 👍1383 | 🔁163
|
||||
- 链接：https://x.com/RecruitmentPq/status/2034131410311643314
|
||||
- 要点：提供职业技趌应用和无学历入口路径
|
||||
|
||||
### X10 (score:73) @segoslavia — 2026-03-16
|
||||
**Another day to remind you to build the right skills. - Data Center Operations - AI deployment and optimization - Prompt engineering - IAM - API Security**
|
||||
- 👍2108 | 🔁290
|
||||
- 链接：https://x.com/segoslavia/status/2033669240914419823
|
||||
- 要点：推荐关键网络安全和 AI 相关技能
|
||||
|
||||
### X3 (score:72) @dimzhuk — 2026-03-25
|
||||
**last30days-skill just gained 1,342 stars today. An AI agent skill that researches any topic. Scans Reddit, X, YouTube, HN, and Polymarket. Then synthesizes a grounded summary**
|
||||
- 链接：https://x.com/dimzhuk/status/2036821040119119942
|
||||
- 要点：强调 last30days-skill 近期研究的快速增长和核心功能
|
||||
|
||||
### X18 (score:71) @trending_repos — 2026-03-27
|
||||
**Trending repository of the day: last30days-skill AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web**
|
||||
- 👍34 | 🔁4
|
||||
- 链接：https://x.com/trending_repos/status/2037469131323384204
|
||||
- 要点：GitHub Trending 帖子关于 last30days-skill 的研究能力
|
||||
|
||||
### X4 (score:71) @claudeai — 2026-03-11
|
||||
**Skills are also now available inside the Excel and PowerPoint add-ins. When your team has a standard workflow (like for running a variance analysis or building a client deck), save it as a skill**
|
||||
- 👍1441 | 🔁75
|
||||
- 链接：https://x.com/claudeai/status/2031790756156096881
|
||||
- 要点：官方发布关于 Office 工具中的技能集成和团队工作流用例
|
||||
|
||||
### X6 (score:69) @omarsar0 — 2026-03-14
|
||||
**Skills are so good when you combine them properly with MCP & CLIs. I have found that Skills can significantly improve tool usage**
|
||||
- 👍255 | 🔁55
|
||||
- 链接：https://x.com/omarsar0/status/2032928526022881399
|
||||
- 要点：探索将技能结合以获得更好的 AI agent 性能
|
||||
|
||||
### X22 (score:67) @shubhraaanshu — 2026-03-29
|
||||
**Real use cases: Extract product specs from manufacturer PDFs, Pull competitor pricing from web pages, Parse legal clauses from contracts**
|
||||
- 链接：https://x.com/shubhraaanshu/status/2038296658111574329
|
||||
- 要点：列出适用于 AI 技能的具体数据提取用例
|
||||
|
||||
### X19 (score:67) @aigclink — 2026-03-27
|
||||
**让AI帮你刷Reddit/YouTube/X的skill：last30days-skill 给一个话题，它会自动去各大社交平台搜最近30天的热门讨论**
|
||||
- 👍6 | 🔁1
|
||||
- 链接：https://x.com/aigclink/status/2037373609635946945
|
||||
- 要点：描述 last30days-skill 在市场研究和工具对比中的用例
|
||||
|
||||
### X7 (score:67) @GoSailGlobal — 2026-03-08
|
||||
**三天不到，开发好了agent skills hub（祝女神们节日快乐） 为什么开发这个产品？如何在众多的skills中找到合适的skills**
|
||||
- 👍306 | 🔁61
|
||||
- 链接：https://x.com/GoSailGlobal/status/2030490279221051487
|
||||
- 要点：介绍 agent skills hub 的分类和推荐用例
|
||||
|
||||
### X2 (score:67) @RoyAmal — 2026-03-25
|
||||
**most AI answers are outdated. this fixes that. it's a skill that researches what's happening right now... /last30days [topic] and it scans: reddit, X, youtube, hacker news, the web**
|
||||
- 链接：https://x.com/RoyAmal/status/2036827050691211636
|
||||
- 要点：将 /last30days 作为实时 AI 研究的工具推广
|
||||
|
||||
### X8 (score:67) @Nikokow — 2026-03-27
|
||||
**last30days is an AI agent skill that researches any topic using only the past 30 days of data. It pulls from Reddit, X, YouTube and more, then synthesizes real discussions**
|
||||
- 链接：https://x.com/Nikokow/status/2037461628527009840
|
||||
- 要点：描述 last30days 作为跨平台近期研究 AI agent 技能
|
||||
|
||||
### X12 (score:65) @davemorin — 2026-03-05
|
||||
**This AI research tool @mvanhorn built is really good. I use it every day**
|
||||
- 👍193 | 🔁15
|
||||
- 链接：https://x.com/davemorin/status/2029438897479176381
|
||||
- 要点：背书 last30days AI 研究工具
|
||||
|
||||
### X13 (score:64) @AI_Tools01 — 2026-03-28
|
||||
**GitHub Trending: last30days-skill - AI Research Tool (11.9k⭐)**
|
||||
- 链接：https://x.com/AI_Tools01/status/2037722016758050823
|
||||
- 要点：列出 last30days-skill 为顶级 GitHub Trending AI 研究工具
|
||||
|
||||
### X20 (score:63) @newlinedotco — 2026-03-27
|
||||
**I just stumbled upon an AI research tool that keeps you updated with the latest discussions across multiple platforms. It's called /last30days**
|
||||
- 🔁1
|
||||
- 链接：https://x.com/newlinedotco/status/2037379661747921352
|
||||
- 要点：介绍 /last30days 作为多平台最新讨论的 AI 研究工具
|
||||
|
||||
### X21 (score:61) @sunsetsyntax — 2026-03-26
|
||||
**Tired of AI research that's full of hallucinations and outdated info? Meet last30days: the open-source agent skill that pulls fresh data from Reddit, X, YouTube, HN, Polymarket**
|
||||
- 链接：https://x.com/sunsetsyntax/status/2037101457166508369
|
||||
- 要点：推广 last30days OSS 以获得最新、可靠的研究
|
||||
|
||||
---
|
||||
|
||||
## 相关 Reddit 讨论（93条，节选相关）
|
||||
|
||||
### R20 (score:69) r/claude — 2026-03-28
|
||||
**Best Claude Skills I use in 2026**
|
||||
- 评分：322👍 | 32💬
|
||||
- 链接：https://www.reddit.com/r/claude/comments/1s5qyef/best_claude_skills_i_use_in_2026/
|
||||
|
||||
### R16 (score:68) r/ClaudeCowork — 2026-03-25
|
||||
**6 skills i actually use every day**
|
||||
- 评分：791👍 | 28💬
|
||||
- 链接：https://www.reddit.com/r/ClaudeCowork/comments/1s3ljzh/6_skills_i_actually_use_every_day/
|
||||
|
||||
---
|
||||
|
||||
## YouTube 相关视频（20条，节选）
|
||||
|
||||
### 71ES9jzqa0Q — Greg Isenberg（2026-02-04）
|
||||
**The Claude Code Skill My Smartest Friends Use**
|
||||
- 👁️35,779 views | 👍764
|
||||
- 链接：https://www.youtube.com/watch?v=71ES9jzqa0Q
|
||||
|
||||
### qb90PPbAWz4 — Greg Isenberg（2026-03-11）
|
||||
**Karpathy's "autoresearch" broke the internet**
|
||||
- 👁️81,095 views | 👍2,101
|
||||
- 链接：https://www.youtube.com/watch?v=qb90PPbAWz4
|
||||
|
||||
### zKBPwDpBfhs — Nate Herk | AI Automation（2026-02-27）
|
||||
**Master 95% of Claude Code Skills in 28 Minutes**
|
||||
- 👁️118,348 views | 👍2,959
|
||||
- 链接：https://www.youtube.com/watch?v=zKBPwDpBfhs
|
||||
|
||||
---
|
||||
|
||||
## 数据统计
|
||||
|
||||
- ✅ X：21 条推文
|
||||
- ✅ Reddit：93 条帖子
|
||||
- ✅ YouTube：20 条视频
|
||||
- ✅ TikTok：2 条视频
|
||||
- 📊 Polymarket：7 个市场（非相关主题）
|
||||
|
||||
---
|
||||
|
||||
*本文件由星辉自动生成（原始数据未经处理）*
|
||||
|
||||
@@ -1,37 +1,37 @@
|
||||
# meigen.ai：一整条 AI 图片 & 视频创作链路
|
||||
|
||||
> 来源：Telegram频道「AI探索指南」
|
||||
> 作者：AI探索指南 @aigc1024
|
||||
> 日期：2026-04-18
|
||||
|
||||
## 核心定位
|
||||
|
||||
很多提示词网站只是在堆内容，而 MeiGen 做的是一整条创作链路：
|
||||
**找提示词 → 生成图片 → 修改优化 → 发布分享**
|
||||
|
||||
整个流程几乎没有卡点。这次更新之后，它甚至把 AI 图片创作 → AI 视频创作也打通了。
|
||||
|
||||
## 创作链路（5步闭环）
|
||||
|
||||
| 步骤 | 功能 | 说明 |
|
||||
|------|------|------|
|
||||
| 1 | 找提示词 | 内置多个标签体系，搜索历史和社区帖子，快速找到想要的提示词 |
|
||||
| 2 | 生成图片 | 找到提示词后可以直接生成图片，无需跳转其它工具 |
|
||||
| 3 | 图生视频 | 支持 veo3.1 模型，生成图片后可以直接图生视频 |
|
||||
| 4 | 修改优化 | 内置修图 / 抠图 / 反推提示词等基础修改能力 |
|
||||
| 5 | 发布分享 | 一键发布到 𝕏，减少内容创作中的重复操作 |
|
||||
|
||||
## 亮点更新
|
||||
|
||||
- **veo3.1 模型**：新增图生视频功能，创作效率直接拉满
|
||||
- **开源 MCP 工具**：让你的 OpenClaw / Claude Code 具备类似 Lovart 的创意规划能力
|
||||
- **被 Awesome Prompt Engineering 收录**（5.5k⭐）
|
||||
|
||||
## 资源链接
|
||||
|
||||
- 网站：https://meigen.ai
|
||||
- GitHub：https://github.com/jau123
|
||||
|
||||
## 标签
|
||||
|
||||
#AI图片 #提示词 #创作工具 #veo3 #MCP #效率工具
|
||||
# meigen.ai：一整条 AI 图片 & 视频创作链路
|
||||
|
||||
> 来源：Telegram频道「AI探索指南」
|
||||
> 作者：AI探索指南 @aigc1024
|
||||
> 日期：2026-04-18
|
||||
|
||||
## 核心定位
|
||||
|
||||
很多提示词网站只是在堆内容，而 MeiGen 做的是一整条创作链路：
|
||||
**找提示词 → 生成图片 → 修改优化 → 发布分享**
|
||||
|
||||
整个流程几乎没有卡点。这次更新之后，它甚至把 AI 图片创作 → AI 视频创作也打通了。
|
||||
|
||||
## 创作链路（5步闭环）
|
||||
|
||||
| 步骤 | 功能 | 说明 |
|
||||
|------|------|------|
|
||||
| 1 | 找提示词 | 内置多个标签体系，搜索历史和社区帖子，快速找到想要的提示词 |
|
||||
| 2 | 生成图片 | 找到提示词后可以直接生成图片，无需跳转其它工具 |
|
||||
| 3 | 图生视频 | 支持 veo3.1 模型，生成图片后可以直接图生视频 |
|
||||
| 4 | 修改优化 | 内置修图 / 抠图 / 反推提示词等基础修改能力 |
|
||||
| 5 | 发布分享 | 一键发布到 𝕏，减少内容创作中的重复操作 |
|
||||
|
||||
## 亮点更新
|
||||
|
||||
- **veo3.1 模型**：新增图生视频功能，创作效率直接拉满
|
||||
- **开源 MCP 工具**：让你的 OpenClaw / Claude Code 具备类似 Lovart 的创意规划能力
|
||||
- **被 Awesome Prompt Engineering 收录**（5.5k⭐）
|
||||
|
||||
## 资源链接
|
||||
|
||||
- 网站：https://meigen.ai
|
||||
- GitHub：https://github.com/jau123
|
||||
|
||||
## 标签
|
||||
|
||||
#AI图片 #提示词 #创作工具 #veo3 #MCP #效率工具
|
||||
|
||||
Reference in New Issue
Block a user