SkillAgentSearch skills...

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-Wiki

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

83/100

Supported Platforms

Claude Code
Claude Desktop

Tags

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.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
7/20
Freshness
11/15

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 found

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.

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.

SkillScoreStarsUpdatedFormat
LLM-Wiki (this skill)by Oshayr83505mo agoMCP Server
claude-memby thedotmack10097.5ktodayCLAUDE.md
Agent-Reachby Panniantong10093.0k22d agoCLAUDE.md
Understand-Anythingby Egonex-AI10085.5k1d agoCLAUDE.md
headroomby headroomlabs-ai10074.6ktodayCLAUDE.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.
<p align="center"> <img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License: MIT"> <img src="https://img.shields.io/badge/version-1.0.0-green.svg" alt="Version"> <img src="https://img.shields.io/badge/platform-Claude%20Code-blueviolet.svg" alt="Platform"> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" alt="PRs Welcome"> </p>

llm-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) to permanent (never expires)

Research-on-Miss

  • Automatic research — /wiki-read researches 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

  1. Install the plugin:

    claude plugin install ./llm-wiki
    

    Or copy manually:

    cp -r llm-wiki .claude/plugins/
    
  2. 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:

  1. Lint — fix broken [[links]], missing frontmatter, orphan pages
  2. Deduplicate — merge pages with >60% slug token overlap
  3. Confidence upgrade — promote pages based on source count (low->medium->high)
  4. Stale detection — flag pages past their freshness tier TTL
  5. Fact-checking — verify claims on high-confidence pages
  6. Concept synthesis — auto-generate articles connecting 3+ related pages
  7. Index regeneration — rebuild index.md from 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.

Related Skills

View on GitHub
GitHub Stars50
CategoryAI
Updated5mo ago
Forks2

Languages

Python

Trust signals

98/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 info