LLM-Wiki
Autonomous knowledge base plugin for Claude Code - captures reserch, ideas, and decisions into an interlinked wiki with reserch-on-miss, semantic search, and a Wikipedia-style web UI. Knowledge compounds as you work.
Install / Use
claude mcp add Oshayr -- npx -y github:Oshayr/LLM-WikiIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AI & Machine LearningSupported Platforms
Our assessment of LLM-Wiki
LLM-Wiki scores 83/100 on our quality scale, 684th of 963 AI & Machine Learning skills we index.
Its MCP Server is 23 KB long, well organised into 46 sections with 11 code examples: a thorough specification that gives an agent plenty to work with.
It has 50 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated about 5 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- Our last check on 2026-09-28 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 98/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
LLM-Wiki compared with similar skills
All 4 of these similar skills score higher than LLM-Wiki; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| LLM-Wiki (this skill)by Oshayr | 83 | 50 | 5mo ago | MCP Server |
| claude-memby thedotmack | 100 | 97.5k | today | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 93.0k | 22d ago | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 85.5k | 1d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.6k | today | CLAUDE.md |
Frequently asked questions
- How do I install LLM-Wiki?
- Run
claude mcp add Oshayr -- npx -y github:Oshayr/LLM-Wiki. The install tabs above show the steps for each supported agent. - Which AI agents does LLM-Wiki work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is LLM-Wiki safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 98/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is LLM-Wiki still maintained?
- The repository was last updated about 5 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubllm-wiki
An autonomous knowledge base that grows as you work.
LLM Wiki is a Claude Code plugin that captures research, ideas, and decisions into an interlinked wiki with semantic search, automatic research, and a Wikipedia-style web UI. Knowledge compounds over time — the more you use it, the smarter it gets.
Inspired by Andrej Karpathy's LLM Wiki pattern: raw sources are immutable, the LLM maintains the wiki layer, and a schema governs behavior.
Core Features
Knowledge Management
- Automatic capture — saves research, ideas, decisions, and findings to the wiki as you work
- Smart retrieval with research-on-miss — checks wiki first, automatically researches and ingests if not found
- Full-text search — TF-IDF keyword search with content-aware scoring and snippet extraction
- Block references & transclusion —
[[page#heading]]links and![[page#section]]embeds - Backlink panel with automatic unlinked mention detection
- Frontmatter query language — Dataview-like queries:
SELECT title, type FROM pages WHERE confidence = "high" - Intelligent freshness — 9-tier staleness system from
live(15 min) topermanent(never expires)
Research-on-Miss
- Automatic research —
/wiki-readresearches topics not in the wiki using available tools - Tool discovery — works with whatever tools the user has (WebSearch, WebFetch, Wikipedia API, MCP tools)
- Auto-ingestion — saves findings to wiki with proper citations
Web UI Features
- Wikipedia-style browsable website with 4 themes (light, dark, terminal, wikipedia)
- Interactive knowledge graph (Cytoscape.js) with multiple layouts, clustering, and neighborhood highlighting
- Canvas/whiteboard view for spatial page arrangement
- Split-pane markdown editor with live preview and AI assist
- Live research — click any red link to auto-research the topic
- Spaced repetition review interface (FSRS-based scheduling)
- Content gap analysis dashboard
- WebSocket chat sidebar with RAG-augmented Q&A
Maintenance & Health
- Self-maintaining — lints broken links, merges duplicates, upgrades confidence, flags stale content
- Daily notes and journal workflows
- Smart caching with adaptive TTL and stale-while-revalidate
- Circuit breakers for external API resilience
- Git integration with auto-commit, attribution, and undo
How It Works
The wiki operates in a simple cycle: when you ask a question, it first checks its knowledge base. If found, it returns a cited answer. If not found, it automatically researches the topic, ingests the findings, and provides an answer—all without breaking your workflow.
sequenceDiagram
User->>wiki-reader: /wiki-read "What is X?"
wiki-reader->>wiki-index: Check knowledge base
alt Found in wiki
wiki-index-->>wiki-reader: Page exists
wiki-reader->>User: Cited answer from wiki
else Not found
wiki-index-->>wiki-reader: No results
wiki-reader->>search-orchestrator: Research needed
search-orchestrator->>search-channel: Fan out queries (web, academic, code, docs)
search-channel->>research-processor: Raw search results
research-processor->>wiki-writer: Processed findings
wiki-writer->>wiki-pages: Create/update page
wiki-writer->>User: Cited answer with new page
end
Quick Start
Installation
-
Install the plugin:
claude plugin install ./llm-wikiOr copy manually:
cp -r llm-wiki .claude/plugins/ -
Restart Claude Code — dependencies install automatically on first session.
First Commands
Start using the wiki immediately with any of these:
| Command | Purpose |
|---------|---------|
| /wiki-write https://example.com/article | Ingest a web page |
| /wiki-read "What is transformer attention?" | Ask — researches if not in wiki |
| /wiki-serve | Browse the wiki at localhost:8420 |
| /wiki-maintain | Health check and optimization |
Dependencies
Core dependencies (fastapi, uvicorn, mcp, etc.) are installed automatically via the plugin's SessionStart hook. For optional enhanced features:
pip install trafilatura # fallback content extraction
pip install numpy sqlite-vec # vector search and caching
Skills Reference
/wiki-write — Add or Update Content
Ingest from URLs, files, or text. Auto-creates .wiki/ on first use.
| Mode | Command | Purpose |
|------|---------|---------|
| Ingest | /wiki-write <url> | Fetch and ingest web page or paper |
| Ingest | /wiki-write <file> | Ingest local file (markdown, text, PDF) |
| Ingest | /wiki-write "text..." | Ingest inline text directly |
| Batch | /wiki-write --batch <dir> | Ingest all .md files in directory |
| Update | /wiki-write --update <slug> | Autonomously update existing page |
| Refresh | /wiki-write --refresh-stale | Find and refresh stale pages |
Page types: concept, idea, brainstorming, status, rules, config, skill, memory, reference, or custom types from .wiki/templates/
/wiki-read — Search and Query
Ask the wiki questions. Automatically researches if knowledge is missing.
| Depth | Command | Behavior |
|-------|---------|----------|
| Quick | /wiki-read quick <question> | Index scan only, no research fallback (fastest) |
| Standard | /wiki-read <question> | Search wiki + auto-research if missing |
| Deep | /wiki-read deep <question> | Full search + raw sources + multi-channel research |
All answers include [[slug]] citations. Contradictions between sources are explicitly noted.
/wiki-serve — Web UI
Launch Wikipedia-style browsable website at localhost:8420.
Features:
- 4 themes (light, dark, terminal, wikipedia)
- Interactive knowledge graph (Cytoscape.js)
- Split-pane markdown editor with live preview
- WebSocket chat with RAG-augmented Q&A
- Live research (click red links to auto-research)
- Spaced repetition review (FSRS-based)
- Content gap analysis dashboard
- Canvas/whiteboard spatial view
Stop: /wiki-serve stop
/wiki-maintain — Health Maintenance
Comprehensive wiki maintenance and quality control.
| Subcommand | Purpose |
|------------|---------|
| /wiki-maintain | Run all maintenance steps |
| /wiki-maintain lint | Fix broken links, missing frontmatter, orphans |
| /wiki-maintain dedup | Find and merge near-duplicate pages |
| /wiki-maintain gaps | Analyze knowledge gaps and missing coverage |
Maintenance steps:
- Lint — fix broken
[[links]], missing frontmatter, orphan pages - Deduplicate — merge pages with >60% slug token overlap
- Confidence upgrade — promote pages based on source count (low->medium->high)
- Stale detection — flag pages past their freshness tier TTL
- Fact-checking — verify claims on high-confidence pages
- Concept synthesis — auto-generate articles connecting 3+ related pages
- Index regeneration — rebuild
index.mdfrom all pages
/wiki-view — Dashboard and Export
Read-only dashboard, statistics, and export capabilities.
| Subcommand | Purpose |
|------------|---------|
| /wiki-view | Dashboard summary (page counts, recent activity, health) |
| /wiki-view pages | List all pages grouped by type |
| /wiki-view stats | Detailed statistics and distributions |
| /wiki-view graph | Knowledge graph visualization (Mermaid) |
| /wiki-view graph <slug> | Graph centered on page (2-hop neighborhood) |
| /wiki-view export html | Export as self-contained HTML |
| /wiki-view export md | Export as single markdown bundle |
| /wiki-view export json | Export as JSON knowledge graph |
| /wiki-view artifacts <type> | Generate study guide, timeline, glossary, or comparison |
How Research-on-Miss Works
When you ask a question that's not in the wiki, the entire research pipeline activates automatically. Here's the flow:
sequenceDiagram
actor User
participant WR as wiki-reader
participant Index as wiki index
participant SO as search-orchestrator
participant SC as search-channel
participant RP as research-processor
participant WW as wiki-writer
participant BM as backlink-manager
User->>WR: /wiki-read "What is X?"
WR->>Index: Check for matching pages
alt Page found
Index-->>WR: Return page
WR-->>User: Cited answer from wiki
else No match
Index-->>WR: No results
WR->>WR: Detect query intent & complexity
WR->>SO: Trigger research
SO->>SO: Route to search channels
SO->>SC: Dispatch to web, academic, code, docs channels
par Parallel Research
SC->>SC: Web search
SC->>SC: Academic search
SC->>SC: Code search
SC->>SC: Docs search
end
SC-->>RP: Raw results
RP->>RP: Deduplicate, condense, rank
RP-->>WW: Processed findings
WW->>WW: Synthesize findings into page
WW->>BM: Update backlinks
BM->>Index: Register page
WW-->>User: Cited answer with new wiki page
end
Architecture
System Overview
LLM Wiki consists of 5 entry points (skills), 10 autonomous agents, utilities in the bin/, and a persistent data layer in .wiki/.
flowchart TD
User([User]) -->|Invokes| Skills
subgraph Skills["5 Entry Points"]
W["/wiki-write<br/>Ingest & Update"]
R["/wiki-read<br/>Search & Ask"]
S["/wiki-serve<br/>Web UI"]
M["/wiki-maintain<br/>Health Check"]
V["/wiki-view<br/>Dashboard"]
end
Skills -->|Route to| Agents
subgraph Agents["10 Autonomous Agents"]
subgraph write["Write Pipeline"]
WW["wiki-writer<br/>(Sonnet)"]
BM["backlink-manager<br/>(Haiku)"]
end
subgraph read["Read Pipeline"]
WR["wiki-reader<br/>(Haiku)"]
SO["search-orchestrator<br/>(Sonnet)"]
SC["search-channel<br/>(Haiku)"]
end
subgraph research["Research Pipeline"]
RL["research-loop<br/>(Sonnet)"]
RP["research-processor<br/>(Haiku)"]
end
subgraph quality["Quality Pipeline"]
WA["wiki-auditor<br/>(Haiku)"]
FC["fact-checker<br/>(Sonnet)"]
CE["citation-explorer<br/>(Sonnet)"]
end
end
Agents -->|Read/Write| Data
Agents -->|Use| Bin
subgraph Bin["Utilities (bin/)"]
Search["search.py<br/>TF-IDF"]
Cache["cache.py<br/>Vectors"]
BL["backlinks.py<br/>Links"]
Gap["gaps.py<br/>Analysis"]
Git["git.py<br/>Tracking"]
end
subgraph Data[".wiki/ Data Layer"]
Pages["pages/<br/>Markdown"]
Index["index.md<br/>Catalog"]
Cache2["cache/<br/>SQLite"]
Raw["raw/<br/>Sources"]
Schema["SCHEMA.md<br/>Rules"]
end
S -->|Serves| UI["Web Server<br/>localhost:8420"]
UI -->|Renders| UIFeatures["4 Themes, Graph,<br/>Editor, Chat, Review"]
Directory Structure
llm-wiki/
.claude-plugin/ Plugin metadata (plugin.json, marketplace.json)
agents/ 10 autonomous agents
bin/ 23 CLI utilities (search, backlinks, gaps, cache, git, ...)
mcp/ MCP serve
Truncated for display — read the full file on GitHub.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
