新增wiki命令文件

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Build the LLM Wiki knowledge graph.
Usage: /wiki-graph
First try running: python tools/build_graph.py --open
If that fails (missing dependencies), build the graph manually:
1. Use Grep to find all [[wikilinks]] across every file in wiki/
2. Build a nodes list: one node per wiki page, with id=relative-path, label=title, type from frontmatter
3. Build an edges list: one edge per [[wikilink]], tagged EXTRACTED
4. Infer additional implicit relationships between pages not captured by wikilinks — tag these INFERRED with a confidence score (0.01.0); tag low-confidence ones AMBIGUOUS
5. Write graph/graph.json with {nodes, edges, built: today}
6. Write graph/graph.html as a self-contained vis.js page (nodes colored by type, edges colored by type, interactive, searchable)
After building, summarize: node count, edge count, breakdown by type, and the most connected nodes (hubs).
Append to wiki/log.md: ## [today's date] graph | Knowledge graph rebuilt

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Ingest a source document into the LLM Wiki.
Usage: /wiki-ingest $ARGUMENTS
$ARGUMENTS should be the path to a file in raw/, e.g. `raw/articles/my-article.md`
Follow the Ingest Workflow defined in CLAUDE.md exactly:
1. Read the source file at the given path
2. Read wiki/index.md and wiki/overview.md for current context
3. Write wiki/sources/<slug>.md (source page format per CLAUDE.md)
4. Update wiki/index.md — add the new entry under Sources
5. Update wiki/overview.md — revise synthesis if warranted
6. Create/update entity pages (wiki/entities/) for key people, companies, projects
7. Create/update concept pages (wiki/concepts/) for key ideas and frameworks
8. Flag any contradictions with existing wiki content
9. Append to wiki/log.md: ## [today's date] ingest | <Title>
After completing all writes, summarize: what was added, which pages were created or updated, and any contradictions found.

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Health-check the LLM Wiki for issues.
Usage: /wiki-lint
Follow the Lint Workflow defined in CLAUDE.md:
Structural checks (use Grep and Glob tools):
1. Orphan pages — wiki pages with no inbound [[wikilinks]] from other pages
2. Broken links — [[WikiLinks]] pointing to pages that don't exist
3. Missing entity pages — names referenced in 3+ pages but lacking their own page
Semantic checks (read and reason over page content):
4. Contradictions — claims that conflict between pages
5. Stale summaries — pages not updated after newer sources changed the picture
6. Data gaps — important questions the wiki can't answer; suggest specific sources to find
Output a structured markdown lint report. At the end, ask if the user wants it saved to wiki/lint-report.md.
Append to wiki/log.md: ## [today's date] lint | Wiki health check

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Query the LLM Wiki and synthesize an answer.
Usage: /wiki-query $ARGUMENTS
$ARGUMENTS is the question to answer, e.g. `What are the main themes across all sources?`
Follow the Query Workflow defined in CLAUDE.md:
1. Read wiki/index.md to identify the most relevant pages
2. Read those pages (up to ~10 most relevant)
3. Synthesize a thorough markdown answer with [[PageName]] wikilink citations
4. Include a ## Sources section at the end listing pages you drew from
5. Ask the user if they want the answer saved as wiki/syntheses/<slug>.md
If the wiki is empty, say so and suggest running /wiki-ingest first.

230
CLAUDE(ENGLISH).md Normal file
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# LLM Wiki Agent — Schema & Workflow Instructions
This wiki is maintained entirely by Claude Code. No API key or Python scripts needed — just open this repo in Claude Code and talk to it.
## Slash Commands (Claude Code)
| Command | What to say |
|---|---|
| `/wiki-ingest` | `ingest raw/my-article.md` |
| `/wiki-query` | `query: what are the main themes?` |
| `/wiki-lint` | `lint the wiki` |
| `/wiki-graph` | `build the knowledge graph` |
Or just describe what you want in plain English:
- *"Ingest this file: raw/papers/attention-is-all-you-need.md"*
- *"What does the wiki say about transformer models?"*
- *"Check the wiki for orphan pages and contradictions"*
- *"Build the graph and show me what's connected to RAG"*
Claude Code reads this file automatically and follows the workflows below.
---
## Directory Layout
```
raw/ # Immutable source documents — never modify these
wiki/ # Claude owns this layer entirely
index.md # Catalog of all pages — update on every ingest
log.md # Append-only chronological record
overview.md # Living synthesis across all sources
sources/ # One summary page per source document
entities/ # People, companies, projects, products
concepts/ # Ideas, frameworks, methods, theories
syntheses/ # Saved query answers
graph/ # Auto-generated graph data
tools/ # Optional standalone Python scripts (require ANTHROPIC_API_KEY)
```
---
## Page Format
Every wiki page uses this frontmatter:
```yaml
---
title: "Page Title"
type: source | entity | concept | synthesis
tags: []
sources: [] # list of source slugs that inform this page
last_updated: YYYY-MM-DD
---
```
Use `[[PageName]]` wikilinks to link to other wiki pages.
---
## Ingest Workflow
Triggered by: *"ingest <file>"* or `/wiki-ingest`
Steps (in order):
1. Read the source document fully using the Read tool
2. Read `wiki/index.md` and `wiki/overview.md` for current wiki context
3. Write `wiki/sources/<slug>.md` — use the source page format below
4. Update `wiki/index.md` — add entry under Sources section
5. Update `wiki/overview.md` — revise synthesis if warranted
6. Update/create entity pages for key people, companies, projects mentioned
7. Update/create concept pages for key ideas and frameworks discussed
8. Flag any contradictions with existing wiki content
9. Append to `wiki/log.md`: `## [YYYY-MM-DD] ingest | <Title>`
### Source Page Format
```markdown
---
title: "Source Title"
type: source
tags: []
date: YYYY-MM-DD
source_file: raw/...
---
## Summary
24 sentence summary.
## Key Claims
- Claim 1
- Claim 2
## Key Quotes
> "Quote here" — context
## Connections
- [[EntityName]] — how they relate
- [[ConceptName]] — how it connects
## Contradictions
- Contradicts [[OtherPage]] on: ...
```
### Domain-Specific Templates
If the source falls into a specific domain (e.g., personal diary, meeting notes), the agent should use a specialized template instead of the default generic one above:
#### Diary / Journal Template
```markdown
---
title: "YYYY-MM-DD Diary"
type: source
tags: [diary]
date: YYYY-MM-DD
---
## Event Summary
...
## Key Decisions
...
## Energy & Mood
...
## Connections
...
## Shifts & Contradictions
...
```
#### Meeting Notes Template
```markdown
---
title: "Meeting Title"
type: source
tags: [meeting]
date: YYYY-MM-DD
---
## Goal
...
## Key Discussions
...
## Decisions Made
...
## Action Items
...
```
---
## Query Workflow
Triggered by: *"query: <question>"* or `/wiki-query`
Steps:
1. Read `wiki/index.md` to identify relevant pages
2. Read those pages with the Read tool
3. Synthesize an answer with inline citations as `[[PageName]]` wikilinks
4. Ask the user if they want the answer filed as `wiki/syntheses/<slug>.md`
---
## Lint Workflow
Triggered by: *"lint the wiki"* or `/wiki-lint`
Use Grep and Read tools to check for:
- **Orphan pages** — wiki pages with no inbound `[[links]]` from other pages
- **Broken links** — `[[WikiLinks]]` pointing to pages that don't exist
- **Contradictions** — claims that conflict across pages
- **Stale summaries** — pages not updated after newer sources
- **Missing entity pages** — entities mentioned in 3+ pages but lacking their own page
- **Data gaps** — questions the wiki can't answer; suggest new sources
Output a lint report and ask if the user wants it saved to `wiki/lint-report.md`.
---
## Graph Workflow
Triggered by: *"build the knowledge graph"* or `/wiki-graph`
When the user asks to build the graph, run `tools/build_graph.py` which:
- Pass 1: Parses all `[[wikilinks]]` → deterministic `EXTRACTED` edges
- Pass 2: Infers implicit relationships → `INFERRED` edges with confidence scores
- Runs Louvain community detection
- Outputs `graph/graph.json` + `graph/graph.html`
If the user doesn't have Python/dependencies set up, instead generate the graph data manually:
1. Use Grep to find all `[[wikilinks]]` across wiki pages
2. Build a node/edge list
3. Write `graph/graph.json` directly
4. Write `graph/graph.html` using the vis.js template
---
## Naming Conventions
- Source slugs: `kebab-case` matching source filename
- Entity pages: `TitleCase.md` (e.g. `OpenAI.md`, `SamAltman.md`)
- Concept pages: `TitleCase.md` (e.g. `ReinforcementLearning.md`, `RAG.md`)
- Source pages: `kebab-case.md`
## Index Format
```markdown
# Wiki Index
## Overview
- [Overview](overview.md) — living synthesis
## Sources
- [Source Title](sources/slug.md) — one-line summary
## Entities
- [Entity Name](entities/EntityName.md) — one-line description
## Concepts
- [Concept Name](concepts/ConceptName.md) — one-line description
## Syntheses
- [Analysis Title](syntheses/slug.md) — what question it answers
```
## Log Format
Each entry starts with `## [YYYY-MM-DD] <operation> | <title>` so it's grep-parseable:
```
grep "^## \[" wiki/log.md | tail -10
```
Operations: `ingest`, `query`, `lint`, `graph`

340
CLAUDE.md
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# LLM Wiki Agent — Schema & Workflow Instructions
# LLM Wiki Agent — Schema & Workflow Instructions(中文版增强规范)
This wiki is maintained entirely by Claude Code. No API key or Python scripts needed — just open this repo in Claude Code and talk to it.
本 Wiki 完全由 Claude Code 自动维护。无需 API Key Python 脚本 —— 只需在 Claude Code 中打开本仓库并与其对话。
## Slash Commands (Claude Code)
---
# 🔴 全局强制规则CRITICAL
| Command | What to say |
## 1. 输出语言(必须遵守)
- 所有输出必须使用**简体中文**
- 专有名词允许保留英文,但首次出现必须附带中文解释
- 禁止中英混合句(术语除外)
- 不允许输出纯英文总结或分析
示例:
Transformer变压器模型一种基于注意力机制的神经网络架构
---
## 2. 输出风格(严格限制)
所有输出必须:
- 去修辞(禁止 narrative 风格)
- 去模糊(禁止“可能”“大概”等词)
- 信息密度最大化
- 面向“知识结构化”,而非阅读体验
优先级:
结构 > 关系 > 结论 > 描述
---
## 3. 结构化语义(必须)
所有页面必须遵循结构化语义规则:
- Summary 必须使用固定字段
- Claim 必须符合标准语法
- Connections 必须使用关系类型
- 禁止自由发挥
---
# Slash CommandsClaude Code
| Command | 使用方式 |
|---|---|
| `/wiki-ingest` | `ingest raw/my-article.md` |
| `/wiki-query` | `query: what are the main themes?` |
| `/wiki-ingest` | `ingest raw/your-file.md` |
| `/wiki-query` | `query: 你的问题` |
| `/wiki-lint` | `lint the wiki` |
| `/wiki-graph` | `build the knowledge graph` |
Or just describe what you want in plain English:
- *"Ingest this file: raw/papers/attention-is-all-you-need.md"*
- *"What does the wiki say about transformer models?"*
- *"Check the wiki for orphan pages and contradictions"*
- *"Build the graph and show me what's connected to RAG"*
---
## 自然语言示例
- ingest raw/papers/attention-is-all-you-need.md
- query: Transformer 的核心机制是什么?
- lint the wiki
- build the graph and analyze RAG
Claude Code 会自动读取本文件并执行以下工作流。
Claude Code reads this file automatically and follows the workflows below.
---
## Directory Layout
# Directory Layout(目录结构)
```
raw/ # Immutable source documents — never modify these
wiki/ # Claude owns this layer entirely
index.md # Catalog of all pages — update on every ingest
log.md # Append-only chronological record
overview.md # Living synthesis across all sources
sources/ # One summary page per source document
entities/ # People, companies, projects, products
concepts/ # Ideas, frameworks, methods, theories
syntheses/ # Saved query answers
graph/ # Auto-generated graph data
tools/ # Optional standalone Python scripts (require ANTHROPIC_API_KEY)
```
raw/ # 原始文档(不可修改)
wiki/ # 知识层(由 Claude 完全维护)
index.md # 页面索引(每次 ingest 必须更新)
log.md # 追加式日志
overview.md # 全局知识总结
sources/ # 每个原始文档对应一个页面
entities/ # 实体(人/公司/产品/项目)
concepts/ # 概念(方法/理论/框架)
syntheses/ # 查询结果沉淀
graph/ # 自动生成的图数据
tools/ # 可选 Python 工具 (require ANTHROPIC_API_KEY)
````
---
## Page Format
# Page Format(页面格式)
Every wiki page uses this frontmatter:
每个页面必须包含:
```yaml
---
id: unique_id
title: "Page Title"
type: source | entity | concept | synthesis
tags: []
sources: [] # list of source slugs that inform this page
sources: [] # 来源
last_updated: YYYY-MM-DD
---
```
````
Use `[[PageName]]` wikilinks to link to other wiki pages.
必须使用 `[[PageName]]` 进行链接。
---
## Ingest Workflow
# Ingest Workflow(摄取流程)
Triggered by: *"ingest <file>"* or `/wiki-ingest`
触发方式:
- `/wiki-ingest`
- 或:`ingest <file>`
Steps (in order):
1. Read the source document fully using the Read tool
2. Read `wiki/index.md` and `wiki/overview.md` for current wiki context
3. Write `wiki/sources/<slug>.md` — use the source page format below
4. Update `wiki/index.md` — add entry under Sources section
5. Update `wiki/overview.md` — revise synthesis if warranted
6. Update/create entity pages for key people, companies, projects mentioned
7. Update/create concept pages for key ideas and frameworks discussed
8. Flag any contradictions with existing wiki content
9. Append to `wiki/log.md`: `## [YYYY-MM-DD] ingest | <Title>`
---
### Source Page Format
## 执行步骤(严格顺序)
1. 使用 Read 工具完整读取 source 文档
2. 读取 `wiki/index.md` 和 `wiki/overview.md`
3. 生成 `wiki/sources/<slug>.md`
4. 更新 `wiki/index.md`
5. 更新 `wiki/overview.md`(如有必要)
6. 创建或更新 Entity 页面
7. 创建或更新 Concept 页面
8. 检测并记录冲突
9. 追加 `wiki/log.md`
---
# Source Page Format增强结构
```markdown
---
@@ -84,28 +139,40 @@ source_file: raw/...
---
## Summary
24 sentence summary.
- 核心主题:
- 问题域:
- 方法/机制:
- 结论/价值:
## Key Claims
- Claim 1
- Claim 2
- (必须符合:主体 + 机制 + 结果)
## Key Quotes
> "Quote here" — context
> "引用内容" — 上下文说明
## Key Concepts
- [[ConceptName]]:定义
## Key Entities
- [[EntityName]]:角色说明
## Connections
- [[EntityName]] — how they relate
- [[ConceptName]] — how it connects
- [[A]] ← depends_on ← [[B]]
- [[C]] ← extends ← [[D]]
## Contradictions
- Contradicts [[OtherPage]] on: ...
- 与 [[OtherPage]] 冲突:
- 冲突点:
- 当前观点:
- 对方观点:
```
### Domain-Specific Templates
---
If the source falls into a specific domain (e.g., personal diary, meeting notes), the agent should use a specialized template instead of the default generic one above:
# Domain-Specific Templates领域模板
## Diary / Journal
#### Diary / Journal Template
```markdown
---
title: "YYYY-MM-DD Diary"
@@ -114,18 +181,16 @@ tags: [diary]
date: YYYY-MM-DD
---
## Event Summary
...
## Key Decisions
...
## Energy & Mood
...
## Connections
...
## Shifts & Contradictions
...
```
#### Meeting Notes Template
---
## Meeting Notes
```markdown
---
title: "Meeting Title"
@@ -134,97 +199,152 @@ tags: [meeting]
date: YYYY-MM-DD
---
## Goal
...
## Key Discussions
...
## Decisions Made
...
## Action Items
...
```
---
## Query Workflow
# Entity & Concept Rules关键增强
Triggered by: *"query: <question>"* or `/wiki-query`
## Entity实体
Steps:
1. Read `wiki/index.md` to identify relevant pages
2. Read those pages with the Read tool
3. Synthesize an answer with inline citations as `[[PageName]]` wikilinks
4. Ask the user if they want the answer filed as `wiki/syntheses/<slug>.md`
创建条件:
- 出现 ≥ 2 次
- 对主题有关键影响
类型:
- 人 / 公司 / 产品 / 项目
---
## Lint Workflow
## Concept概念
创建条件:
- 可抽象
- 可复用
- 非具体实例
---
Triggered by: *"lint the wiki"* or `/wiki-lint`
## 命名规范(强制)
- 使用唯一标准名称
- 所有别名写入页面:
Use Grep and Read tools to check for:
- **Orphan pages** — wiki pages with no inbound `[[links]]` from other pages
- **Broken links** — `[[WikiLinks]]` pointing to pages that don't exist
- **Contradictions** — claims that conflict across pages
- **Stale summaries** — pages not updated after newer sources
- **Missing entity pages** — entities mentioned in 3+ pages but lacking their own page
- **Data gaps** — questions the wiki can't answer; suggest new sources
Output a lint report and ask if the user wants it saved to `wiki/lint-report.md`.
```markdown
## Aliases
- GPT4
- GPT-4
```
---
## Graph Workflow
## 去重机制(必须)
Triggered by: *"build the knowledge graph"* or `/wiki-graph`
When the user asks to build the graph, run `tools/build_graph.py` which:
- Pass 1: Parses all `[[wikilinks]]` → deterministic `EXTRACTED` edges
- Pass 2: Infers implicit relationships → `INFERRED` edges with confidence scores
- Runs Louvain community detection
- Outputs `graph/graph.json` + `graph/graph.html`
If the user doesn't have Python/dependencies set up, instead generate the graph data manually:
1. Use Grep to find all `[[wikilinks]]` across wiki pages
2. Build a node/edge list
3. Write `graph/graph.json` directly
4. Write `graph/graph.html` using the vis.js template
创建前必须:
1. 搜索 index
2. 判断是否存在
3. 存在则更新
---
## Naming Conventions
# Query Workflow查询流程
- Source slugs: `kebab-case` matching source filename
- Entity pages: `TitleCase.md` (e.g. `OpenAI.md`, `SamAltman.md`)
- Concept pages: `TitleCase.md` (e.g. `ReinforcementLearning.md`, `RAG.md`)
- Source pages: `kebab-case.md`
触发:
- `/wiki-query`
- 或:`query: 问题`
## Index Format
---
## 步骤
1. 读取 index
2. 找到相关页面
3. 使用 Read 工具加载
4. 输出结构化答案
5. 使用 `[[Page]]` 引用
6. 询问是否保存为 synthesis
---
# Lint Workflow校验
检查内容:
- 孤立页面
- 断链
- 冲突
- 过期内容
- 缺失实体
- 知识空白
---
# Graph Workflow知识图谱
触发:
- `/wiki-graph`
---
执行:
- 优先运行 `tools/build_graph.py`
- 否则手动构建:
步骤:
1. 提取所有 `[[links]]`
2. 构建节点与边
3. 输出 `graph.json`
---
# Naming Conventions命名规范
- Sourcekebab-case
- EntityTitleCase
- ConceptTitleCase
---
# Index Format索引结构
```markdown
# Wiki Index
## Overview
- [Overview](overview.md) — living synthesis
- [Overview](overview.md)
## Sources
- [Source Title](sources/slug.md) — one-line summary
- [Title](sources/slug.md)
## Entities
- [Entity Name](entities/EntityName.md) — one-line description
- [Entity](entities/Entity.md)
## Concepts
- [Concept Name](concepts/ConceptName.md) — one-line description
- [Concept](concepts/Concept.md)
## Syntheses
- [Analysis Title](syntheses/slug.md) — what question it answers
- [Title](syntheses/slug.md)
```
## Log Format
---
Each entry starts with `## [YYYY-MM-DD] <operation> | <title>` so it's grep-parseable:
# Log Format日志
```
grep "^## \[" wiki/log.md | tail -10
## [YYYY-MM-DD] ingest | 标题
```
Operations: `ingest`, `query`, `lint`, `graph`
---
# ✅ 最终目标
该系统用于:
- 知识沉淀
- 结构化理解
- 自动图谱构建
- Agent 推理支持
---
# END

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@@ -1,227 +0,0 @@
---
title: 公众号爆款文章制造机:这套 Skill 工具,是我送给你最硬核的赚钱外挂!
source:
author: shenwei
published:
created:
description:
tags: []
---
# 公众号爆款文章制造机:这套 Skill 工具,是我送给你最硬核的赚钱外挂!
> 来源: X (Twitter) @冰河
> 发布时间: 2026年3月12日 23:30
> 原文链接: https://x.com/binghe/status/2032116993378435433
---
容我先吹吹牛逼!公众号内容创业这条路,我们走了五年了,矩阵运营了多个帐号,有了些小小的成绩,下面的内容全是多年精华经验浓缩!
文章长,内容干!如果你看不下去,那就帮我点点赞吧!感谢!
> 注:本篇文章 Skill 已经发布在"钱来有道"社群,欢迎加入,全年免费更新,众多赚钱类教程任意阅读!性感的我会一对一服务!机会难得,广告结束! 哦对了!本 Skill 也会发布在订阅区!欢迎订阅!
---
公众号赛道,一直都是我认为最低成本,最容易得到正反馈的选择。
只要你会写作,或者"不那么"会写,都能取得成绩。
还记得以前我写过一篇文章手把手教你如何用AI写爆款文章轻松日赚到2000块
很多人通过这篇文章认识了我,加入了社群!
一年过去了,公众号赛道的文章类型和平台政策已经大变样了,我决定在一年后重新把以前的经验整理成 Skill给大家一个新的选择。
好,确定了写公众号这个低成本创收的赛道,那你平时在写作的时候会经常遇到以下几个问题:
(配图)
这些问题,几乎每个做公众号的人都经历过。
今天这个 Skill 我需要它扮演的角色不是个写稿机器 ,而应该是一个会思考的编辑!
市面上的 AI 写作工具,大多数只能做一件事:你说写什么,它就写什么。
但写公众号爆款,从来不是「有文字输出」就够了。
你需要有人帮你判断选题够不够刁钻,需要有人帮你想清楚这件事从哪个角度切进去文字最有力量,需要有人在你想法还模糊的时候,把它描绘清晰——而不是催你「请提供更多信息」。
这就是这个 Skill 要做的事!
---
## 实操来讲解这个 Skill 它帮你解决了哪些真实的痛点?
### 第一步
先把 Skill 文件放到全局目录中的"skills "里,然后在其它地方新建目录,在当前目录下打开 Claude Code
让它调用 Skill ,开始写作流程:
(配图)
可以看到skill 没有开始就准备写,而是帮你写之前整理写作逻辑,结构,和切入角度入手!
比如我直接说:我想写关于娱乐八卦
它调用的就是娱乐类文章写法的 skill设定内容
但其实这个 Skill有好多写作种类的设定
(配图)
不管你选择哪个场景,都会有相对应的 Skill 进行规则设定,让你写的内容更符合当前分类爆款内容结构。
我们下面是拿娱乐八卦来做一下演示。
(配图)
请仔细看看这种分类下的文章结构,这是我拆解了近百篇娱乐类公众号文章提炼的写作精华内容。
下面你可以根据提示,完成下一步具体的写作要求。
比如某个娱乐事件,这就是要求输入主题内容了。
> 一般情况我强烈建议你多上传一些关于这个主题内容的其它不同类文章语料越多写出来的内容越丰富AI 也会综合判断什么样的内容结构排列组合会更有针对性。这样就从根上避免了抄袭!因为这已经是一篇综合多方信息源的原创文章了。
案例操作,简单点!我让他查了查薛之谦最近的娱乐新闻,准备写关于他的内容。
(配图)
同一个热点,一百个人在写!
大多数人写的角度都差不多,读者看了开头就知道后面是什么。
这个 Skill 有一个专门的 `~选题` 功能。
你给它一个方向,它帮你找那个「别人没想到,但你一说出来就觉得绝」的切入角度。
每个角度它都会告诉你爆点在哪里、为什么这个方向有传播潜力,让你在动笔前就心里有数。
(配图)
可以看到,它其实给了我很多个选题(一共五个),每个选题的文章方向、爆点在哪里全部进行了规划!
你从这些规划中参考一下,是否有一些是别人没讲过的,或者是几个选题排列组合一下成为一个新的内容结构,都是可以的,要学会活学活用!
(配图)
我随便先了"角度五",到这里这个 Skill 也没有开始写!这很关键!
我们要先看大纲,每部分的内容规划如何?最后的总字数是否满足你的要求?
核心观点是否寓意突出?
在做了综合判断后,再开始写正文!
(配图)
注意:这里如果你不满意都可随时调整,我把文章字数设定了与写作分类相关,并不是越多越好!但字数少,也不能表现出文章深度!
输入写正文后,产出内容,并同时保存在当前项目目录里!
这里我要说明几个事情:
1. 如果你使用的模型是国产的话,文章内容非常 AI 化格式化喜欢用第一第二不是而是一定要手动润色Claude 的opus 模型就没有这个问题。所以要注意!
2. 内容结构完全是依照娱乐类文章的写作模式在编写。调动情绪,引导评论,制造互动性,如下图结尾部分:
(配图)
3. 所有内容为.md 格式,你可以上网找一些转成公众号的编辑器,直接粘贴进去,就成了公众号文章内容了。我们以前有过分享!
4. 要审稿别直接发布每次产出的内容必审稿AI 不是万能的,你要去对内容中的情绪、事件完整性、数据准确度进行审核,别闹出笑话!
(配图)
以前是先有标题,后有内容,但这次我把这个顺序改了一下,先有内容后产标题!
这样标题的生成就会更有针对性,我们可以输入"~标题",它会调用 Skill 中的标题公式,进行有目的性的产出 10 条备选标题内容!
以 AI 的角度提供3 条选择,当然最终选择权在你这里,如果不满意继续让它产出!
我个人认为这些标题还是非常有"娱乐性"的,他不是标题党,而是情绪先行!
(配图)
选择完标题,自动加入到文章中,然后就到了精细打磨阶段了!
其中有两个指令非常重要就是:"~改稿""~精写"
其实就是哪里不对改哪里,你只需要复制某个段落,或者是某部分内容,然后再加上指令,就可指哪打哪儿!也可以直接改语气,反正只要是修改,使用这两个命令就完了!
这样修改出来的内容是会根据上下文进行改写,不会偏离主题!而不需要全部重写,提升效率!
这里就不做演示了!
最后写完,我们也要分析一下文章到底写的怎么样?所谓当局者迷!
那好,输入指令"~分析"
(配图)
(配图)
分析报告非常详细,到时你自己看吧!如果你觉得它说的有道理,那好就让他继续修改!
如果没有问题就可以继续产出了。
可以看出来,在写之前,我们一直做的是陪伴编辑的角色,这就是这个 Skill 的意义,知其然,也要知其所以然!
不然,你只是个使用工具无情的机器!
(配图)
这就结束了吗?
不,当然没有!
这套 Skill 还有个非常牛的设定:不管你写的是什么内容类型,除了大类规则套用,还可以跨类型通用规则适配!
(配图)
我把常见的通用类型也在规则在做了设计,不管是常见的不常见的,只要写好通用规则,自动启动爆款格式,进行有人味,有立场,有冲突,有情绪,有互动的文章写作逻辑!
当你想用自己的风格写作,当然也可以!
我们在指令中还加入了一个"~风格" 指令,输入这个指令的时候,给你四种选择:
(配图)
A. 查看当前已经写好的文章风格,以后沿用(前提是你喜欢这个笔风)
B. 直接告诉 AI 你就要什么风格,因为你也没想好。
C. 上传你的参考文章,他会提炼风格特征进行模仿,产出属于你自己味道的内容。(上传文件最好是.md 格式,多传一些也没问题。)
D. 保持现状
是不是感觉这套 Skill 有点意思了?
哈哈哈,越灵活的操作越可以发挥它的最大功效!我们不能把自己的思维固住!
可能这么多指令你记不住 ,没关系!
只要输入"~指令",就会把所有指令都做一展示,还有指令功能进行说明。我是不是很贴心?
(配图)
好了,这个 Skill 其实并不复杂,只是里面的写作规则记录的比较丰富!
我们在面对任何写作领域的时候,不管是公众号,推特,头条其它写文章的平台都可以适用!
但好的文章要有温度,有共情,有特别的切入角度,并且能引起讨论,这才是一篇合格的文章!
内容的深度,关键在于你提代的语料是否足够,还与你对信息获取的能力息息相关!
---
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---
title: 夸克资源
source:
author: shenwei
published:
created:
description:
tags: []
---
# 夸克资源
> 来源: https://t.me/quarkF/311117
> 添加时间: 2026-04-04
## 资源链接
https://t.me/quarkF/311117
## 频道信息
- **频道名**:付费资源分享
- **频道ID**@quarkF
- **链接**https://t.me/quarkF/311117
## 频道简介
分享一些我购买的付费课程!
防失联付费资源共享群 @zhishisou
免费加入内部群,所有付费资源免费共享!
网络上能看到的,只是冰山一角。
想解锁更真实、更硬核的内容?来群里搜搜看,也许能发现不一样的世界。
@tangxingvlog 可以搜索全网任何资源
## 内容状态
⚠️ 频道内容需登录 Telegram 才能查看完整详情,公开链接仅显示频道简介和封面图

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