By tom5610
Skills for maintaining an LLM-driven personal wiki — ingest sources, query knowledge, lint structure, and extract derivatives
Extract and derive structured knowledge from wiki pages for downstream use — structured data, derivative documents, or compact knowledge packs. Use when the user asks to "extract", "export", "generate a checklist", "create a summary for", "build a reference", or produce any derivative output from wiki content.
Process a raw source document into the wiki — read it, discuss key takeaways, create/update wiki pages, update index and log. Use when the user drops a new document, article, or notes into raw/ or asks to "add", "process", "import", or "incorporate" a source.
Health-check the wiki — find contradictions, orphan pages, stale claims, missing cross-references, and content gaps. Categorized by severity. Use when the user asks to "check", "validate", "review", "clean up", "audit", or "fix" the wiki.
Answer questions against the wiki or search it by keyword — synthesize an answer with citations, or return a navigational results list. Use when the user asks a question, says "what does the wiki say about", "explain", "summarize", or wants an answer drawn from wiki content. Also use when the user asks to "find", "look up", "search for", or "where is" something in the wiki.
Modifies files
Hook triggers on file write and edit operations
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An LLM-maintained personal knowledge base — a persistent, interlinked collection of markdown pages built incrementally from raw source documents. The default domain is writing craft, but lenses allow any domain to be added.
You curate sources, direct the analysis, and ask questions. The LLM does the summarizing, cross-referencing, filing, and bookkeeping that makes a knowledge base compound over time.
Install the skills directly into any project via the Claude Code plugin system:
# Add the marketplace
/plugin marketplace add tom5610/llm-wiki
# Install the plugin
/plugin install llm-wiki@llm-wiki-plugins
Once installed, skills are available as /llm-wiki:ingest, /llm-wiki:query, /llm-wiki:lint, and /llm-wiki:extract.
git clone https://github.com/tom5610/llm-wiki.git
cd llm-wiki
Skills are available directly as /ingest, /query, /lint, /extract when using Claude Code in this directory.
Three layers:
| Layer | Path | Owner | Purpose |
|---|---|---|---|
| Raw sources | raw/ | Human | Immutable source documents (articles, papers, notes, transcripts). Never modified by the LLM. |
| Wiki | wiki/ | LLM | Generated markdown pages — summaries, entity pages, concept pages, technique pages, comparisons, synthesis. Fully owned by the LLM. |
| Schema | CLAUDE.md | Co-evolved | Governs wiki conventions, page formats, and workflows. |
The wiki/ directory is auto-bootstrapped on the first /ingest run — no manual setup needed.
Four core operations:
| Skill | Description |
|---|---|
/ingest | Process a raw source into the wiki. Reads the source, discusses key takeaways, creates/updates wiki pages, updates index and log. |
/query | Answer questions against the wiki or search by keyword. Synthesizes answers with citations, or returns navigational results. |
/lint | Health-check the wiki. Finds contradictions, orphan pages, stale claims, missing cross-references, and content gaps. |
/extract | Derive structured knowledge — JSON/YAML data, checklists, guides, decision trees, or compact knowledge packs for LLM prompts. |
Lenses allow the same wiki to span multiple domains. Each lens defines what to notice during ingest, how to categorize content, and what quality standards apply. Lens files live in .llm-wiki/lenses/.
The LLM proposes lenses during ingest when it encounters content outside the base domain — you review and approve them. Pages are tagged with their lens, not separated into silos, so cross-domain linking works naturally.
raw//ingest (or /llm-wiki:ingest if using the plugin) to process it/query/lint to keep the wiki healthy├── .claude-plugin/ # Plugin manifest and marketplace catalog
│ ├── plugin.json
│ └── marketplace.json
├── skills/ # Plugin-distributed skills
│ ├── ingest/SKILL.md
│ ├── query/SKILL.md
│ ├── lint/SKILL.md
│ └── extract/SKILL.md
├── hooks/ # Plugin hooks (post-write validation)
│ └── hooks.json
├── scripts/ # Hook scripts
│ └── wiki-check.py
├── raw/ # Source documents (user-provided)
├── wiki/ # Generated wiki pages (auto-bootstrapped)
├── evals/ # Skill verification tests
├── CLAUDE.md # Wiki schema and conventions
└── ref/ # Pattern description and references
The eval suite verifies skill correctness via claude -p (programmatic mode):
# Install test dependencies
uv pip install pytest pytest-dependency pytest-timeout
# Run all tests (~7-10 min)
uv run pytest evals/ -v
# Fast mode — skip slow tests (~4-6 min)
uv run pytest evals/ -v -m "not slow"
# Reset wiki state before re-running
rm -rf wiki/ .llm-wiki/ .wiki-snapshot/ extracts/
npx claudepluginhub tom5610/llm-wiki --plugin llm-wikiClaude + Obsidian knowledge companion. Sets up a persistent, compounding wiki vault (Karpathy's LLM Wiki pattern). v1.7 "Compound Vault" + v1.8 methodology modes close 5 of 5 priority gaps from the May 2026 compass artifact. Ships: substrate alignment with kepano/obsidian-skills, default Obsidian CLI transport, hybrid retrieval (contextual prefix + BM25 + cosine rerank per Anthropic's Sept 2024 research), per-file advisory locking for multi-writer safety, pre-commit verifier agent, AND methodology modes (LYT / PARA / Zettelkasten / Generic) for first-class organizational support no other Claude+Obsidian competitor offers. v1.7.x audit closure: every BLOCKER + HIGH + MEDIUM + LOW finding from the v1.7.0 audit is CLOSED or DEFERRED-with-rationale. Optional DragonScale Memory extension (log folds, deterministic addresses, semantic tiling lint, boundary-first autoresearch).
Build and maintain an LLM-curated personal knowledge base in your project — Andrej Karpathy's LLM Wiki pattern, designed to scale to thousands of pages without becoming a context bottleneck. Now with an optional compiled graph layer for typed, provenance-backed relationships.
Complete AI coding workflow system. Self-correcting memory + persistent FTS5-indexed research wikis + auto-research loop + multi-LLM council on a single SQLite store. 33 skills, 8 agents, 22 commands, 37 hook scripts across 24 events. Cross-agent via SkillKit.
Make your AI agent code with your project's architecture, rules, and decisions.