Skill: 1knownothing/llm-wiki-skills (version 1.0.0)

--- name: wiki-knowledge-base description: | LLM-powered personal wiki knowledge base system. Use this when user wants to build and maintain a persistent wiki using LLMs following the LLM Wiki pattern (karpathy/442a6bf555914893e9891c11519de94f). This system implements: wiki initialization (two-layer architecture - wiki content and schema), source ingestion with cross-reference maintenance, querying with synthesis and citations, health checking (lint), and schema management. Source files are stored externally (e.g., in project's raw/ folder), not copied into the wiki. Perfect for: personal knowledge management, research wikis, reading companions, team knowledge bases. Triggers on: wiki knowledge base, LLM wiki, personal wiki, build wiki, knowledge management, or any mention of organizing accumulated knowledge with an LLM. compatibility: Write, Read, Glob, Grep, Bash --- # Wiki Knowledge Base - Master Skill This is a comprehensive system for building and maintaining a personal wiki knowledge base using LLMs. ## What This System Does The LLM Wiki pattern differs from traditional RAG: - **RAG**: LLM retrieves relevant chunks at query time, rediscovering knowledge from scratch - **Wiki**: LLM incrementally builds and maintains a persistent wiki with cross-references already in place ## Core Operations This system provides 5 skills for wiki management: ### 1. wiki-init Initialize a new wiki with: - Two-layer architecture (wiki content, schema) - Directory structure for entities, concepts, sources, syntheses - Initial index.md and log.md - CLAUDE.md schema with conventions **Note**: Source files should be stored externally (e.g., in project's raw/ folder) - the wiki stores summaries and analysis, not copies of source documents. **Trigger**: User wants to set up a new wiki ### 2. wiki-ingest Process new sources: - Read and analyze source documents - Create source summary pages - Update entity and concept pages - Flag contradictions with existing claims - Update index.md and log.md **Trigger**: User adds a new source or wants to process a document ### 3. wiki-query Answer questions against the wiki: - Search index.md first - Read relevant pages - Synthesize answers with citations - Support multiple output formats (markdown, tables, slides) - File valuable answers back to wiki **Trigger**: User asks questions about wiki content ### 4. wiki-lint Health check the wiki: - Find contradictions between pages - Identify stale claims superseded by new sources - Find orphan pages with no inbound links - Detect missing pages for important concepts - Identify missing cross-references - Suggest data gaps to fill **Trigger**: User wants to maintain or clean up the wiki ### 5. wiki-maintain Manage wiki schema: - Update CLAUDE.md conventions - Modify page templates - Refine workflows - Add new page types **Trigger**: User wants to update wiki rules or structure ## Usage Select the appropriate sub-skill based on the operation: - **New wiki?** → wiki-init - **Adding content?** → wiki-ingest - **Asking questions?** → wiki-query - **Checking quality?** → wiki-lint - **Updating rules?** → wiki-maintain ## Directory Structure When initialized, the wiki follows this structure: ``` wiki/ ├── entities/ # Entity pages ├── concepts/ # Concept pages ├── sources/ # Source summaries ├── syntheses/ # Cross-source syntheses ├── comparisons/ # Comparison analyses ├── overviews/ # Overview pages ├── schema/CLAUDE.md # Wiki conventions ├── index.md # Content catalog └── log.md # Operation log # Source files should be stored in the project root's raw/ folder (or similar external location) ``` ## Key Concepts - **index.md**: Content-oriented catalog, updated on every ingest - **log.md**: Chronological record with format `## [YYYY-MM-DD] operation | Title` - **CLAUDE.md**: Schema defining conventions and workflows - **Cross-references**: Always use [[wiki/page]] link format

path: wiki/index.md
content: # AI Interpretability Wiki - Index

## Sources (论文摘要)

### Core Papers

| Paper | Key Concept | Author |
|-------|-------------|--------|
| [[wiki/sources/toy-models-superposition]] | Superposition Theory | Transformer Circuits |
| [[wiki/sources/scaling-monosemanticity]] | SAE Feature Extraction | Transformer Circuits |
| [[wiki/sources/circuit-tracing]] | Circuit Analysis Method | Transformer Circuits |
| [[wiki/sources/biology-large-language-model]] | Claude 3.5 Haiku Analysis | Transformer Circuits |
| [[wiki/sources/assistant-axis]] | Persona & Character | Anthropic |
| [[wiki/sources/activation-oracles]] | Activation Explainer | Multiple Authors |
| [[wiki/sources/coffee-feature-coffins]] | Feature Extraction | Ronge, Maier, Eberhardt |

## Concepts (概念)

- [[wiki/concepts/sparse-autoencoder]]: 稀疏自编码器
- [[wiki/concepts/superposition]]: 叠加
- [[wiki/concepts/mechanistic-interpretability]]: 机制解释性
- [[wiki/concepts/feature-steering]]: 特征引导

## Entities (实体)

- [[wiki/entities/anthropic]]: Anthropic AI公司
- [[wiki/entities/transformer-circuits]]: Transformer Circuits研究团队

## Research Topics

### Foundational Theory
- Superposition
- Polysemanticity
- Monosemanticity

### Methods
- Sparse Autoencoders (SAEs)
- Transcoders
- Attribution Graphs
- Circuit Tracing

### Applications
- Feature Steering
- Activation Capping
- Persona Control

### Safety
- Jailbreak Prevention
- Harmful Content Detection
- Deception Identification

## Theme Overview

This wiki focuses on **Mechanistic Interpretability** - the field of understanding how AI models compute their outputs. Key themes include:

1. **Feature Extraction**: Using sparse autoencoders to find interpretable features in model activations
2. **Circuit Analysis**: Understanding how features interact to produce model behavior
3. **Safety Applications**: Using interpretability techniques for AI safety

## Updated
2026-04-09

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