d8ac6107bf
- Lead with one-sentence hook + output structure upfront - Add What You Get section naming concrete deliverables - Consolidate agent compatibility into schema file table - Add tech stack one-liner - Streamline use cases, quick start, and graph sections
229 lines
8.5 KiB
Markdown
229 lines
8.5 KiB
Markdown
# LLM Wiki Agent
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[](LICENSE)
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**A coding agent skill.** Drop source documents into `raw/` and type `/wiki-ingest` — the agent reads them, extracts knowledge, and builds a persistent interlinked wiki. Every new source makes the wiki richer. You never write it.
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> Most knowledge tools make you search your own notes. This one reads everything you've collected and writes a structured wiki that compounds over time — cross-references already built, contradictions already flagged, synthesis already done.
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```
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/wiki-ingest raw/papers/attention-is-all-you-need.md
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```
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```
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wiki/
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├── index.md catalog of all pages — updated on every ingest
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├── log.md append-only record of every operation
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├── overview.md living synthesis across all sources
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├── sources/ one summary page per source document
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├── entities/ people, companies, projects — auto-created
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├── concepts/ ideas, frameworks, methods — auto-created
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└── syntheses/ query answers filed back as wiki pages
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graph/
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├── graph.json persistent node/edge data (SHA256-cached)
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└── graph.html interactive vis.js visualization — open in any browser
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```
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## Install
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**Requires:** [Claude Code](https://claude.ai/code), [Codex](https://openai.com/codex), [Gemini CLI](https://github.com/google-gemini/gemini-cli), or any agent that reads a config file.
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```bash
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git clone https://github.com/SamurAIGPT/llm-wiki-agent.git
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cd llm-wiki-agent
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```
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Open in your agent — no API key or Python setup needed:
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```bash
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claude # reads CLAUDE.md + .claude/commands/
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codex # reads AGENTS.md
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opencode # reads AGENTS.md
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gemini # reads GEMINI.md
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```
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## Usage
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```
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/wiki-ingest raw/papers/my-paper.md # ingest a source into the wiki
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/wiki-ingest raw/articles/my-article.md # works on any markdown file
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/wiki-query "what are the main themes?" # synthesize answer from wiki pages
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/wiki-query "how does X relate to Y?" # with [[wikilink]] citations
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/wiki-lint # find orphans, contradictions, gaps
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/wiki-graph # build graph.html from all wikilinks
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```
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Plain English also works with any agent:
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```
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"Ingest this paper: raw/papers/llama2.md"
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"What does the wiki say about attention mechanisms?"
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"Check for contradictions across sources"
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"Build the knowledge graph and tell me the most connected nodes"
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```
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Works with any markdown source — articles, papers, book chapters, meeting notes, journal entries, research summaries.
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## What You Get
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**Persistent wiki** — structured markdown pages that accumulate across sessions. Unlike chat, nothing is lost.
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**Entity pages** — auto-created for every person, company, or project mentioned across sources. Updated each time a new source references them.
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**Concept pages** — auto-created for every key idea or framework. Cross-referenced to every source that discusses them.
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**Living overview** — `wiki/overview.md` is revised on every ingest to reflect the current synthesis across everything you've read.
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**Contradiction flags** — when a new source contradicts an existing claim, it's flagged at ingest time, not buried until query time.
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**Knowledge graph** — `graph.html` shows every wiki page as a node, every `[[wikilink]]` as an edge, and Claude-inferred implicit relationships as dotted edges. Community detection clusters related topics.
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**Lint reports** — orphan pages, broken links, missing entity pages, data gaps with suggested sources to fill them.
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## Use Cases
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### Research
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Going deep on a topic over weeks — reading papers, articles, reports.
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```
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/wiki-ingest raw/papers/attention-is-all-you-need.md
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/wiki-ingest raw/papers/llama2.md
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/wiki-ingest raw/papers/rag-survey.md
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# Wiki builds entity pages (Meta AI, Google Brain) and
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# concept pages (Attention, RLHF, Context Window) automatically.
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/wiki-query "What are the main approaches to reducing hallucination?"
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/wiki-query "How has context window size evolved across models?"
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/wiki-lint
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# → "No sources on mixture-of-experts — consider the Mixtral paper"
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```
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By the end you have a structured, interlinked reference — not a folder of PDFs you'll never reopen.
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---
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### Reading a Book
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File each chapter as you go. Build out pages for characters, themes, arguments.
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```
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/wiki-ingest raw/book/chapter-01.md
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/wiki-ingest raw/book/chapter-02.md
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# Wiki creates entity and theme pages automatically.
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/wiki-query "How has the protagonist's motivation evolved?"
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/wiki-query "What contradictions exist in the author's argument so far?"
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/wiki-graph # → graph.html shows every character/theme and how they connect
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```
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Think fan wikis like Tolkien Gateway — built as you read, with the agent doing all the cross-referencing.
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---
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### Personal Knowledge Base
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Track goals, health, habits, self-improvement — file journal entries, articles, podcast notes.
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```
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/wiki-ingest raw/journal/2026-01-week1.md
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/wiki-ingest raw/articles/huberman-sleep-protocol.md
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/wiki-ingest raw/articles/atomic-habits-summary.md
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/wiki-query "What patterns show up in my journal entries about energy?"
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/wiki-query "What habits have I tried and what was the outcome?"
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```
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The wiki builds a structured picture over time. Concepts like "Sleep", "Exercise", "Deep Work" accumulate evidence from every source filed.
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---
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### Business / Team Intelligence
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Feed in meeting transcripts, project docs, customer calls.
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```
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/wiki-ingest raw/meetings/q1-planning-transcript.md
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/wiki-ingest raw/docs/product-roadmap-2026.md
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/wiki-ingest raw/calls/customer-interview-acme.md
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/wiki-query "What feature requests have come up most across customer calls?"
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/wiki-query "What decisions were made in Q1 and what was the rationale?"
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/wiki-lint
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# → "Project X mentioned in 5 pages but no dedicated page"
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# → "Roadmap contradicts customer interview on priority of feature Y"
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```
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The wiki stays current because the agent does the maintenance no one wants to do.
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---
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### Competitive Analysis
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Track a company, market, or technology over time.
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```
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/wiki-ingest raw/competitors/openai-announcements.md
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/wiki-ingest raw/market/ai-funding-report-q1.md
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/wiki-query "How do OpenAI and Anthropic differ on safety approach?"
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/wiki-query "Which companies announced multimodal models in the last 6 months?"
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/wiki-query "Competitive landscape summary as of today" --save
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```
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## The Graph
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Two-pass build:
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1. **Deterministic** — parses all `[[wikilinks]]` across wiki pages → edges tagged `EXTRACTED`
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2. **Semantic** — agent infers implicit relationships not captured by wikilinks → edges tagged `INFERRED` (with confidence score) or `AMBIGUOUS`
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Louvain community detection clusters nodes by topic. SHA256 cache means only changed pages are reprocessed. Output is a self-contained `graph.html` — no server, opens in any browser.
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## CLAUDE.md / AGENTS.md
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The schema file tells the agent how to maintain the wiki — page formats, ingest/query/lint/graph workflows, naming conventions. This is the key config file. Edit it to customize behavior for your domain.
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| Agent | Schema file |
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| Claude Code | `CLAUDE.md` |
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| Codex / OpenCode | `AGENTS.md` |
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| Gemini CLI | `GEMINI.md` |
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## What Makes This Different from RAG
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| RAG | LLM Wiki Agent |
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| Re-derives knowledge every query | Compiles once, keeps current |
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| Raw chunks as retrieval unit | Structured wiki pages |
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| No cross-references | Cross-references pre-built |
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| Contradictions surface at query time (maybe) | Flagged at ingest time |
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| No accumulation | Every source makes the wiki richer |
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## Tips
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- Use [Obsidian](https://obsidian.md) to browse the wiki — follow links, check graph view, use Dataview for frontmatter queries
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- Use [Obsidian Web Clipper](https://obsidian.md/clipper) to clip web articles directly to `raw/`
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- File good query answers back with `--save` — your explorations compound just like ingested sources
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- The wiki is a git repo — version history for free
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- Standalone Python scripts in `tools/` work without a coding agent (require `ANTHROPIC_API_KEY`)
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## Tech Stack
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NetworkX + Louvain + Claude + vis.js. No server, no database, runs entirely locally. Everything is plain markdown files.
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## Related
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- [graphify](https://github.com/safishamsi/graphify) — graph-based knowledge extraction skill (inspiration for the graph layer)
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- [Vannevar Bush's Memex (1945)](https://en.wikipedia.org/wiki/Memex) — the original vision this resembles
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## License
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MIT License — see [LICENSE](LICENSE) for details.
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