ΩmegaWiki

Karpathy's LLM-Wiki Vision, Fully Realized

A wiki-centric full-lifecycle AI research platform powered by Claude Code

From paper ingestion to publication — your research knowledge compounds, never decays.

English | 中文

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What is ΩmegaWiki?

Andrej Karpathy proposed LLM-Wiki: an LLM that builds and maintains a persistent, structured wiki from your sources — not a throwaway RAG answer, but compounding knowledge that grows smarter with every paper you feed it.

ΩmegaWiki takes that idea and runs the full distance. It's not just a wiki builder — it's a complete research lifecycle platform: from paper ingestion → knowledge graph → gap detection → idea generation → experiment design → paper writing → peer review response. All driven by 23 Claude Code skills, all centered on one wiki as the single source of truth.

Drop your .tex / .pdf files in a folder. Run one command. Get a fully cross-referenced knowledge base — and then use it to generate novel research ideas, design experiments, write papers, and respond to reviewers.

Why Wiki-Centric, Not RAG?

	RAG	ΩmegaWiki
Knowledge persistence	Rediscovered on every query	Compiled once, maintained forever
Structure	Flat chunk store	9 typed entities with relationships
Cross-references	None — chunks are isolated	Bidirectional wikilinks + typed graph
Knowledge gaps	Invisible	Explicitly tracked, drive research
Failed experiments	Lost	First-class anti-repetition memory
Output	Chat answers	Papers, surveys, experiment plans, rebuttals
Compounding	No — same cost every query	Yes — each paper enriches the whole graph

Architecture

Every skill reads from and writes back to the wiki. Knowledge compounds — each new paper enriches the whole graph. Failed experiments aren't discarded; they become anti-repetition memory that prevents re-exploring dead ends.

Quick Start

Prerequisites: Python 3.9+, Node.js 18+

# 1. Clone
git clone https://github.com/skyllwt/OmegaWiki.git
cd OmegaWiki

# 2. Install Claude Code
npm install -g @anthropic-ai/claude-code
claude login

# 3. One-click setup
chmod +x setup.sh && ./setup.sh        # Linux / macOS
# Windows (PowerShell):
#   powershell -ExecutionPolicy Bypass -File .\setup.ps1

# 4. Put your papers in raw/papers/ (.tex or .pdf)

# 5. Build your wiki
claude
# Then type: /init <your-research-topic>

Manual setup (Linux / macOS)

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env                 # Edit to add API keys
cp config/settings.local.json.example .claude/settings.local.json

Manual setup (Windows / PowerShell)

python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
Copy-Item .env.example .env          # Edit to add API keys
Copy-Item config\settings.local.json.example .claude\settings.local.json

Note: native Windows is supported for the local pipeline. Remote-GPU experiments via /exp-run --env remote rely on ssh/rsync/screen and are best run from WSL2 or Linux/macOS.

API Keys

Key	Required?	How to get	What it enables
ANTHROPIC_API_KEY	Yes	claude login (automatic)	Powers all Claude Code skills
SEMANTIC_SCHOLAR_API_KEY	Optional	semanticscholar.org/product/api (free)	Citation graph, paper search
DEEPXIV_TOKEN	Optional	setup.sh auto-registers	Semantic search, TLDR, trending
LLM_API_KEY + LLM_BASE_URL + LLM_MODEL	Optional	Any OpenAI-compatible API	Cross-model review

Cross-model review: ΩmegaWiki uses a second LLM as an independent reviewer for ideas, experiments, and paper drafts. Works with any OpenAI-compatible API — DeepSeek, OpenAI, Qwen, OpenRouter, SiliconFlow, etc. If not configured, skills still work in Claude-only mode.

Skills

23 slash commands spanning the full research lifecycle:

Phase 0: Setup

Command	What it does
/setup	First-time configuration (API keys, language, dependencies)
/reset <scope>	Destructive cleanup: wiki | raw | log | checkpoints | all

Phase 1: Knowledge Foundation

Command	What it does
/prefill <domain>	Seed foundations/ with background knowledge (suggested before /init)
/init <topic>	Bootstrap a full wiki from raw/
/ingest <source>	Parse a paper → wiki pages + cross-references
/edit <request>	Add/remove sources or update wiki content
/ask <question>	Query the wiki, crystallize answers back
/check	Health scan: broken links, missing cross-refs, consistency

Phase 2: Research Pipeline

Command	What it does
/daily-arxiv	Auto-fetch & filter new arXiv papers (+ GitHub Actions cron)
/ideate	Multi-phase idea generation from cross-topic connections
/novelty <idea>	Multi-source novelty verification (web + S2 + wiki + review LLM)
/review <artifact>	Cross-model adversarial review for any research artifact
/exp-design <idea>	Claim-driven experiment + ablation design
/exp-run <experiment>	Implement + deploy + monitor (local or remote GPU)
/exp-status	Dashboard for running experiments; auto-collect results
/exp-eval <experiment>	Verdict gate → auto-update claims/ideas/graph
/refine <artifact>	Multi-round: produce → review → fix → re-review

Phase 3: Writing & Submission

Command	What it does
/survey	Generate Related Work from wiki knowledge
/paper-plan <claims>	Outline from claim graph + evidence matrix
/paper-draft <plan>	Draft LaTeX + figures, section by section
/paper-compile <dir>	Compile → PDF, auto-fix, verify page/anonymity
/research <direction>	End-to-end orchestrator with human gates
/rebuttal <reviews>	Parse reviewer comments → draft point-by-point responses

Wiki Structure

9 Entity Types

Type	Directory	Purpose
Paper	papers/	Structured summary with problem/method/results/limitations
Concept	concepts/	Cross-paper technical concept with variants and comparisons
Topic	topics/	Research direction map with SOTA tracker and open problems
Person	people/	Researcher profile with key papers and collaborators
Idea	ideas/	Research idea with lifecycle: proposed → tested → validated/failed
Experiment	experiments/	Full record: hypothesis → setup → results → claim updates
Claim	claims/	Testable claim with evidence list and confidence score
Summary	Summary/	Domain-wide survey across topics
Foundation	foundations/	Background knowledge (terminal: receives inward links, writes none)

Knowledge Graph

9 typed relationships stored in graph/edges.jsonl:

extends · contradicts · supports · inspired_by · tested_by · invalidates · supersedes · addresses_gap · derived_from

All pages use Obsidian [[wikilink]] format — open wiki/ in Obsidian for visual graph exploration.

Automation

GitHub Actions runs /daily-arxiv at UTC 00:00 daily:

1. Add ANTHROPIC_API_KEY to repo Settings → Secrets
2. .github/workflows/daily-arxiv.yml fetches arXiv, runs ingestion, auto-commits

Project Structure

OmegaWiki/
├── CLAUDE.md                    # Runtime schema & rules
├── wiki/                        # Knowledge base (LLM-maintained)
│   ├── papers/                  #   Structured paper summaries
│   ├── concepts/                #   Cross-paper technical concepts
│   ├── topics/                  #   Research direction maps
│   ├── people/                  #   Researcher profiles
│   ├── ideas/                   #   Research ideas (with lifecycle)
│   ├── experiments/             #   Experiment records
│   ├── claims/                  #   Testable research claims
│   ├── Summary/                 #   Domain-wide surveys
│   ├── foundations/             #   Background knowledge (terminal pages)
│   ├── outputs/                 #   Generated artifacts
│   ├── graph/                   #   Auto-generated: edges, context, gaps
│   ├── index.md                 #   Content catalog
│   └── log.md                   #   Chronological log
├── raw/                         # Source materials (read-only)
│   ├── papers/                  #   .tex / .pdf files
│   ├── notes/                   #   .md notes
│   └── web/                     #   HTML / Markdown
├── tools/                       # Deterministic Python helpers
│   ├── research_wiki.py         #   Wiki engine (20 CLI commands)
│   ├── lint.py                  #   Structural validation (10 checks)
│   ├── reset_wiki.py            #   Scoped destructive cleanup helper
│   ├── fetch_arxiv.py           #   arXiv RSS fetcher
│   ├── fetch_s2.py              #   Semantic Scholar API
│   ├── fetch_deepxiv.py         #   DeepXiv semantic search
│   ├── fetch_wikipedia.py       #   Wikipedia fetcher (used by /prefill)
│   └── remote.py                #   SSH ops for remote experiments
├── .claude/skills/              # 23 Claude Code skill definitions
├── i18n/                        # Bilingual: en/ (canonical) + zh/
├── config/                      # Configuration templates
├── tests/                       # 2263 tests
├── mcp-servers/                 # Cross-model review server
└── .github/workflows/           # Daily arXiv cron

Testing

source .venv/bin/activate
python -m pytest tests/ -v

2263 tests covering all tools, skills, and shared references.

Bilingual Support

ΩmegaWiki ships in English and Chinese:

./setup.sh --lang en   # English (default)
./setup.sh --lang zh   # 中文

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Roadmap

• Wiki knowledge engine (20 CLI commands, 9 entity types, 9 edge types)
• 23 Claude Code skills (full research lifecycle)
• Cross-model review (any OpenAI-compatible API)
• Daily arXiv automation (GitHub Actions)
• Remote GPU experiment support
• Bilingual i18n (EN + ZH)
• Demo dataset (example wiki with pre-ingested papers)
• LaTeX venue templates (NeurIPS, ICML, ACL, etc.)
• Multi-user collaboration
• More language support

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Community / 交流群

Scan to join the ΩmegaWiki WeChat group / 扫码加入微信交流群

Acknowledgments

• Andrej Karpathy — for the LLM-Wiki concept that inspired this project
• Claude Code — the AI agent runtime that powers ΩmegaWiki

License

MIT — use it, fork it, build on it.

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中文

ΩmegaWiki 是什么？

Andrej Karpathy 提出了 LLM-Wiki 概念：让 LLM 构建并维护一个持久的、结构化的 wiki，而不是一次性的 RAG 回答。知识持续积累，每一篇新论文都让整个知识图谱更强。

ΩmegaWiki 将这个理念完整实现。 它不仅是 wiki 构建器，更是完整的研究全流程平台：从论文摄入 → 知识图谱 → 缺口检测 → 想法生成 → 实验设计 → 论文写作 → 同行评审回复。23 个 Claude Code Skills 驱动，一个 wiki 作为唯一的知识中枢。

为什么选择 Wiki 而不是 RAG？

	RAG	ΩmegaWiki
知识持久性	每次查询都重新发现	编译一次，持续维护
结构	扁平的 chunk 存储	9 种实体类型 + 关系图
交叉引用	无 — chunk 彼此孤立	双向 wikilink + 类型化边
知识缺口	不可见	显式追踪，驱动研究方向
失败实验	丢失	一等公民，防止重复探索
输出	聊天回答	论文、综述、实验方案、审稿回复
复利效应	无 — 每次查询成本相同	有 — 每篇论文丰富整个图谱

快速开始

前置条件： Python 3.9+, Node.js 18+

git clone https://github.com/skyllwt/OmegaWiki.git && cd OmegaWiki

# 安装 Claude Code
npm install -g @anthropic-ai/claude-code
claude login

# 一键配置
chmod +x setup.sh && ./setup.sh --lang zh        # Linux / macOS
# Windows (PowerShell):
#   powershell -ExecutionPolicy Bypass -File .\setup.ps1 -Lang zh

# 把论文放入 raw/papers/（.tex 或 .pdf）
# 启动 Claude Code
claude
# 输入：/init <你的研究方向>

Windows 用户：本地 pipeline 已原生支持。/exp-run --env remote 远程 GPU 实验依赖 ssh/rsync/screen，建议在 WSL2 或 Linux/macOS 下运行。

API Key 说明

Key	必须？	获取方式	用途
ANTHROPIC_API_KEY	是	claude login	驱动所有 Skill
SEMANTIC_SCHOLAR_API_KEY	可选	semanticscholar.org（免费）	引用图谱、论文搜索
DEEPXIV_TOKEN	可选	setup.sh 自动注册	语义搜索、热门趋势
LLM_API_KEY + LLM_BASE_URL + LLM_MODEL	可选	任意 OpenAI 兼容 API	跨模型评审

23 个 Skill 命令

命令	功能
/setup	首次配置（API key、语言、依赖）
/reset	按范围销毁性清理：wiki | raw | log | checkpoints | all
/prefill	预填 foundations/ 背景知识（建议在 /init 之前运行）
/init	从 raw/ 搭建完整 wiki
/ingest	消化论文，创建页面 + 交叉引用
/edit	增删 raw 或更新 wiki
/ask	对 wiki 提问
/check	wiki 健康检查
/daily-arxiv	每日 arXiv 新论文（CI 自动）
/ideate	跨方向构思研究 idea
/novelty	多源新颖性验证
/review	跨模型评审
/exp-design	Claim 驱动实验设计
/exp-run	部署 + 监控实验
/exp-status	实验状态看板
/exp-eval	裁决 → 更新 claims
/refine	多轮迭代改进
/survey	生成 Related Work
/paper-plan	Claim 图谱 → 论文提纲
/paper-draft	提纲 + wiki → LaTeX 草稿
/paper-compile	编译 → PDF，自动修复
/research	端到端研究编排器
/rebuttal	解析评审意见 → 逐条回复

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