karpathy-llm-wiki
astro-han/karpathy-llm-wiki · skills.sh
Open source Repository Open in the app JSON README (API)
About
Skill publicada por astro-han/karpathy-llm-wiki no skills.sh. Instale com: npx skills add astro-han/karpathy-llm-wiki@karpathy-llm-wiki
Details
- Kind
- Agent skills
- Topic
- AI, RAG & memory
- Publisher
- astro-han
- Origin
- skillssh
- Category
- ferramentas
- Stars
- 2,222
- Forks
- 261
- Open pull requests
- 2
- Last push
- 2026-07-23T16:57:44Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-30 15:21:25
- Updated
- 2026-09-08 15:02:18
- Origin id
astro-han/karpathy-llm-wiki/karpathy-llm-wiki
README
# karpathy-llm-wiki **A reusable skill for building Karpathy-style LLM wikis with Claude Code, Cursor, Codex, and other Agent Skills tools.** [](LICENSE) [](https://github.com/Astro-Han/karpathy-llm-wiki) [](https://github.com/Astro-Han/karpathy-llm-wiki) [](https://agentskills.io) [](https://github.com/Astro-Han/karpathy-llm-wiki#install) <p align="center"> <img src="assets/karpathy-tweet.png" alt="Karpathy's tweet about LLM Wiki" width="560"> </p> `karpathy-llm-wiki` packages [Karpathy's LLM Wiki idea](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) into one installable [Agent Skills](https://agentskills.io) skill. Your coding agent ingests sources into `raw/`, compiles durable knowledge pages into `wiki/`, answers questions with citations, and lints the wiki for consistency. ## What Is an LLM Wiki? An **LLM wiki** is a knowledge system where the LLM maintains structured wiki pages instead of re-searching raw documents on every question. New sources are compiled into durable markdown pages, cross-references are updated over time, and answers cite the wiki pages that already contain the synthesized knowledge. This skill gives you three operations: | Operation | What it does | Output | |-----------|--------------|--------| | **Ingest** | Collects a source into `raw/`, triages it, then creates or updates wiki articles — or just logs it when nothing is new | New or updated wiki pages | | **Query** | Searches the wiki and answers with citations | Grounded answers linking to markdown pages | | **Lint** | Checks index integrity, links, and wiki health | Auto-fixes plus reported issues | See [SKILL.md](SKILL.md) for the full skill specification. ## LLM Wiki vs RAG | Approach | Knowledge lives in | When synthesis happens | Good for | |----------|--------------------|------------------------|----------| | **RAG** | Raw chunks and embeddings | At query time | Broad retrieval across large corpora | | **LLM Wiki** | Curated markdown pages | During ingest and maintenance | Compounding knowledge, summaries, and durable cross-links | This skill is optimized for the wiki model: knowledge that improves over time instead of re-deriving relationships on every query. ## Usage Stats Based on a production knowledge base maintained daily since April 2026: - **94** wiki articles across **13** topic directories - **99** source materials ingested - **87** operation log entries in the last 7 days See [examples/](examples/) for sample wiki pages, source files, and operation logs. ## Install ```bash npx add-skill Astro-Han/karpathy-llm-wiki ``` Works with any tool that supports the [Agent Skills](https://agentskills.io) standard. ## Quick Start ### 1. Ingest your first source Give the skill a URL, a file, or pasted text: > "Ingest this article: https://example.com/attention-is-all-you-need" The skill stores the source in `raw/`, then compiles or updates the right knowledge pages in `wiki/`. ### 2. Ask your wiki a question > "What do I know about attention mechanisms?" The skill searches the wiki and answers with citations linking back to your markdown pages. ### 3. Keep the wiki healthy > "Lint my wiki" Checks for broken links, missing index entries, stale cross-references, and related issues. ## How the Workflow Works The core idea from Karpathy: the LLM maintains the wiki while the human focuses on choosing sources and asking good questions. ```text your-project/ ├── raw/ ← Immutable source material │ └── topic/ │ └── 2026-04-03-source-article.md ├── wiki/ ← Compiled knowledge pages maintained by the LLM │ ├── topic/ │ │ └── concept-name.md │ ├── index.md ← Global table of contents │ └── log.md ← Append-only operation log ``` Each new source can update multiple pages, strengthen cross-references, and record contradictions. That is what makes the wiki compound over time. ## Tool Compatibility This skill follows the [agentskills.io](https://agentskills.io) open standard: | Tool | Install method | |------|----------------| | Claude Code | `npx add-skill Astro-Han/karpathy-llm-wiki` | | Cursor | `npx add-skill Astro-Han/karpathy-llm-wiki` | | Codex CLI | Copy to `.agents/skills/karpathy-llm-wiki/` | | OpenCode | `npx add-skill Astro-Han/karpathy-llm-wiki` | | Other tools | Copy `SKILL.md`, `references/`, and `scripts/` into the tool's skill directory | ## FAQ ### What is the difference between an LLM wiki and a personal wiki? An LLM wiki is maintained by the model. It updates summaries, cross-links, index entries, and contradictions as new material arrives. A normal personal wiki depends on manual editing. ### What sources can I ingest? Web pages, papers, blog posts, PDFs, markdown files, text files, and pasted text. The skill converts everything into markdown under `raw/` and compiles it into `wiki/`. ### Is this production-ready? The workflow is based on a real knowledge base with 94 articles and 99 sources maintained daily since April 2026. The repo includes examples, templates, and a design spec. ## Design Boundaries Deliberately not built, after three months of production logs and a survey of the ecosystem (LLM Wiki v2, llm-wiki-compiler, OKF, agent-memory literature): - **Source-hash freshness tracking** — raw/ is immutable, so hashes guard against events that cannot happen. Genuinely new information arrives as new sources through normal ingest. - **Persisted line-number citations** — every observed fidelity error was "value absent from the source", which a whole-file grep catches. Anchors only disambiguate a failure mode that has not occurred, and the annotation friction makes agents skip the rule. - **Numeric confidence or quality scores** — false precision with no calibration behind it. Evidence strength belongs in the prose. - **Per-article review dates** — nobody can predict at compile time how fast a domain moves. Maintenance is driven by whole-wiki lint, not per-page timers. - **Access-based decay** — frequently asked is not the same as true. - **Retract / bad-source machinery** — has not happened yet. Handle it manually until it does. - **Automatic hooks and scheduled runs** — those belong to the agent harness, not a tool-agnostic skill. - **Vector or graph search** — at 50K–100K tokens of curated wiki, grep and read are more reliable. Add search tooling only when recall measurably degrades. - **Typed relationship ontologies** — link semantics live in the prose around the link. - **OKF conformance** — the spec is a v0.1 draft with a minimal tooling ecosystem. Tracked; will be revisited. - **MCP servers, UIs, output subsystems** — outside the boundary of a tool-agnostic skill. ## Inspired By Unofficial community implementation of the workflow from [Karpathy's LLM Wiki idea](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f). The value here is the reusable workflow, prompt structure, and battle-tested knowledge-compilation rules. See also: [lucasastorian/llmwiki](https://github.com/lucasastorian/llmwiki), [atomicmemory/llm-wiki-compiler](https://github.com/atomicmemory/llm-wiki-compiler). We are tracking Google's [Open Knowledge Format](https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/main/okf) draft and will evaluate compatibility once the spec and tooling mature. ## License [MIT](LICENSE)