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Expand use cases with concrete workflows per domain

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Each use case now shows exactly what to ingest, what to query, and
what the wiki produces — Research, Reading, Personal KB, Business
intelligence, and Competitive analysis.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Anil Matcha
2026-04-07 07:21:13 +05:30
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@@ -20,56 +20,46 @@ Three layers:
- **`wiki/`** — Claude-maintained markdown pages (Claude writes, you read)
- **`graph/`** — auto-generated knowledge graph visualization
## Quick Start — Any Coding Agent (no API key needed)
Works with Claude Code, Codex, OpenCode, Gemini CLI, and any agent that reads a config file from the repo root.
| Agent | Config file read automatically |
|---|---|
| [Claude Code](https://claude.ai/code) | `CLAUDE.md` + `.claude/commands/` |
| [OpenAI Codex](https://openai.com/codex) | `AGENTS.md` |
| OpenCode / Pear AI | `AGENTS.md` |
| [Gemini CLI](https://github.com/google-gemini/gemini-cli) | `GEMINI.md` |
| Any other agent | Point it at `AGENTS.md` or `README.md` |
## Quick Start
```bash
git clone https://github.com/SamurAIGPT/GPT-Agent.git
cd GPT-Agent
claude # Claude Code
codex # OpenAI Codex
opencode # OpenCode
gemini # Gemini CLI
git clone https://github.com/SamurAIGPT/llm-wiki-agent.git
cd llm-wiki-agent
```
Each agent reads its config file and follows the same workflows. Then talk to it:
Open it in your coding agent — **no API key or Python setup needed**:
```bash
claude # Claude Code
codex # OpenAI Codex
opencode # OpenCode / Pear AI
gemini # Gemini CLI
```
Each agent reads its config file automatically (`CLAUDE.md`, `AGENTS.md`, or `GEMINI.md`) and follows the same workflows. Then just talk to it:
```
# Claude Code slash commands:
# Claude Code — slash commands:
/wiki-ingest raw/articles/my-article.md
/wiki-query what are the main themes across all sources?
/wiki-lint
/wiki-graph
# Any agent (plain English):
# Any agent — plain English works too:
"Ingest this paper: raw/papers/my-paper.md"
"What does the wiki say about X?"
"Check for contradictions"
"Build the knowledge graph"
```
## Quick Start — Standalone Python (requires API key)
| Agent | Config file |
|---|---|
| [Claude Code](https://claude.ai/code) | `CLAUDE.md` + `.claude/commands/` |
| [OpenAI Codex](https://openai.com/codex) | `AGENTS.md` |
| OpenCode / Pear AI | `AGENTS.md` |
| [Gemini CLI](https://github.com/google-gemini/gemini-cli) | `GEMINI.md` |
```bash
pip install -r requirements.txt
export ANTHROPIC_API_KEY=your_key_here
python tools/ingest.py raw/articles/my-article.md
python tools/query.py "What are the main themes?"
python tools/query.py "How does X relate to Y?" --save
python tools/build_graph.py --open
python tools/lint.py --save
```
> **Standalone use** (without a coding agent): `pip install -r requirements.txt`, set `ANTHROPIC_API_KEY`, then use `python tools/ingest.py`, `python tools/query.py`, etc.
## Architecture
@@ -144,10 +134,126 @@ Community detection (Louvain) clusters nodes by topic. The output is a self-cont
## Use Cases
- **Research** — go deep on a topic over weeks; every paper/article updates the same wiki
- **Reading** — build a companion wiki as you read a book; by the end you have a rich reference
- **Personal knowledge** — file journal entries, health notes, goals; build a structured picture over time
- **Business** — feed in meeting transcripts, Slack threads, docs; LLM does the maintenance no one wants to do
### Research
Going deep on a topic over weeks or months — reading papers, articles, reports.
```
# Each paper you read gets ingested:
/wiki-ingest raw/papers/attention-is-all-you-need.md
/wiki-ingest raw/papers/llama2.md
/wiki-ingest raw/papers/rag-survey.md
# Wiki builds up entity pages (e.g. "Meta AI", "Google Brain") and
# concept pages (e.g. "Attention Mechanism", "RLHF") automatically.
# Ask synthesis questions across everything you've read:
/wiki-query "What are the main approaches to reducing hallucination?"
/wiki-query "How has context window size evolved across models?"
# Check where your knowledge has gaps:
/wiki-lint
# → "No sources on mixture-of-experts — consider reading the Mixtral paper"
```
By the end of a research project you have a structured, interlinked reference that reflects everything you've read — not a folder of PDFs you'll never reopen.
---
### Reading a Book
File each chapter as you go. Build out pages for characters, themes, plot threads.
```
# After each chapter:
/wiki-ingest raw/book/chapter-01-the-beginning.md
/wiki-ingest raw/book/chapter-02-the-conflict.md
# Wiki creates pages like:
# entities/ElonMusk.md, entities/Tesla.md
# concepts/FirstPrinciplesThinking.md
# Mid-book:
/wiki-query "How has the protagonist's motivation evolved?"
/wiki-query "What contradictions exist in the author's argument so far?"
# End of book — build the graph:
/wiki-graph
# Open graph.html → see every character/theme/event and how they connect
```
Think fan wikis like the Tolkien Gateway — thousands of interlinked pages. You can build something like that as you read, with the agent doing all the cross-referencing.
---
### Personal Knowledge Base
Track goals, health, psychology, self-improvement — file journal entries, articles, podcast notes.
```
# File your journal entries:
/wiki-ingest raw/journal/2026-01-week1.md
/wiki-ingest raw/journal/2026-01-week2.md
# File articles and podcast notes that resonated:
/wiki-ingest raw/articles/huberman-sleep-protocol.md
/wiki-ingest raw/articles/atomic-habits-summary.md
# Ask introspective questions:
/wiki-query "What patterns show up in my journal entries about energy levels?"
/wiki-query "What habits have I tried and what was the outcome?"
# The wiki builds a structured picture of you over time —
# entities like "Sleep", "Exercise", "Deep Work" accumulate evidence
# from every source you've filed.
```
---
### Business / Team Intelligence
Feed in meeting transcripts, Slack exports, project docs, customer calls.
```
# Onboard new context:
/wiki-ingest raw/meetings/q1-planning-transcript.md
/wiki-ingest raw/docs/product-roadmap-2026.md
/wiki-ingest raw/calls/customer-interview-acme.md
# Wiki creates pages for projects, people, decisions, recurring themes.
# Ask strategic questions:
/wiki-query "What feature requests have come up most across customer calls?"
/wiki-query "What decisions were made in Q1 planning and what was the rationale?"
# Lint catches things like:
# → "Project X mentioned in 5 pages but no dedicated page"
# → "Roadmap contradicts customer interview on priority of feature Y"
```
The wiki stays current because the agent does the maintenance no one on the team wants to do.
---
### Competitive Analysis / Due Diligence
Track a company, market, or technology area over time.
```
# Feed in everything you find:
/wiki-ingest raw/competitors/openai-announcements.md
/wiki-ingest raw/competitors/anthropic-blog-posts.md
/wiki-ingest raw/market/ai-funding-report-q1.md
# Wiki builds entity pages per company, concept pages per technology.
# Ask comparison questions:
/wiki-query "How do OpenAI and Anthropic differ in their approach to safety?"
/wiki-query "Which companies have announced multimodal models in the last 6 months?"
# Save the answer back as a reusable synthesis:
/wiki-query "Competitive landscape summary as of today" --save
```
## Tips