by atomicstrata
The knowledge compiler. Raw sources in, interlinked wiki out. Inspired by Karpathy's LLM Wiki pattern.
# Add to your Claude Code skills
git clone https://github.com/atomicstrata/llm-wiki-compilerGuides for using ai agents skills like llm-wiki-compiler.
Last scanned: 10/2/2026
{
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"message": "@vitest/mocker: Vitest: Path Traversal / Arbitrary File Read via @vitest/mocker Redirect Mock",
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{
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"message": "body-parser: body-parser vulnerable to denial of service when invalid limit value silently disables size enforcement",
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"message": "brace-expansion: brace-expansion: DoS via exponential-time expansion of consecutive non-expanding {} groups",
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{
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"message": "fast-uri: fast-uri vulnerable to host confusion via literal backslash authority delimiter",
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{
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"message": "hono: Hono: ReDoS in CORS middleware via Access-Control-Request-Headers",
"severity": "medium"
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"message": "ip-address: ip-address: Address4 decodes leading-zero octets as decimal while resolvers decode them as octal, allowing SSRF and trust-boundary bypass",
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"message": "js-yaml: JS-YAML: Quadratic CPU consumption in !!omap resolution (3.x and 4.x) — CVE-2026-59870 fix not backported",
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"type": "npm-audit",
"message": "markdown-it: markdown-it linkify: true has two quadratic paths, so a few hundred KB of markdown blocks the event loop for tens of seconds",
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"message": "nanoid: nanoid: non-secure generators can loop indefinitely with negative size",
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"message": "postcss: PostCSS: incomplete fix of GHSA-6g55-p6wh-862q — attacker-controlled sourceMappingURL reads arbitrary .map files when `from` is unset",
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"message": "sanitize-html: ApostropheCMS: Stored XSS via SVG SMIL URI-list scheme-policy bypass",
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}See how llm-wiki-compiler compares with popular alternatives.
llm-wiki-compiler is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by atomicstrata. The knowledge compiler. Raw sources in, interlinked wiki out. Inspired by Karpathy's LLM Wiki pattern. It has 2,157 GitHub stars.
llm-wiki-compiler returned warnings in SkillsLLM's automated security scan. It has no critical vulnerabilities, but review the flagged issues in the Security Report section before adding it to your workflow.
Clone the repository with "git clone https://github.com/atomicstrata/llm-wiki-compiler" and add it to your Claude Code skills directory (see the Installation section above).
llm-wiki-compiler is primarily written in TypeScript. It is open-source under atomicstrata on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh llm-wiki-compiler against similar tools.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Release notes · Upgrade guide · Install llmwiki
Meet Scientific Clay, with soft surfaces and rounded typography, and Minimal, which follows your system’s light or dark setting. Switch instantly between four themes, including Nebula Light and Dark.
The same page in a demonstration wiki. Click either screenshot for a closer look.
Recursive source folders, path exclusions, project-specific compile instructions, and storage for larger embedding indexes.
Browse profile-defined categories, declared fields, connected records, provenance, and supporting source passages in the local viewer.
Create signed template distributions, discover them through explicitly trusted catalogs, and install or update them with compatibility checks.
Build a knowledge system around the way you work. Define your records, relationships, review gates, and workflows in one validated profile. Start with AutoSci for research or Newsroom for editorial work, or create your own.
Explore Configurable Lifecycle Profiles →
Compile raw sources into an interlinked, citation-traceable markdown wiki that agents and humans can browse, query, lint, export, and reuse. The default profile preserves the classic concepts-and-queries layout; optional profiles add domain-specific types and workflows without adding domain branches to the compiler.
llmwiki implements the LLM Wiki pattern: instead of re-discovering knowledge from raw files at query time, compile it once into durable pages that accumulate structure, provenance, review state, and retrieval metadata over time.
Use llmwiki when you need a persistent knowledge base from raw material:
llms.txt.Do not use llmwiki as a general static-site generator, a heavy ontology database, or a replacement for ad-hoc search over fast-changing raw logs. It is strongest when source knowledge is worth compiling, reviewing, and reusing.
concept, entity, comparison, and overview..llmwiki/profile.json can declare entity schemas, typed relations, lifecycle state machines, transition requirements, workflows, artifacts, connectors, content tiers, and retrieval policy.llmwiki template init autosci creates a research project with papers, ideas, experiments, manuscripts, evidence artifacts, workflows, and Crossref import. newsroom demonstrates the same machinery for editorial work.llmwiki lint validates the links.llmwiki view opens a read-only browser UI with search, page metadata, graph exploration, source-freshness badges, and citation chips.llmwiki lint and llmwiki next surface stale/orphaned pages; llmwiki refresh --stale repairs changed knowledge without compiling unrelated new sources.llmwiki eval reports health score, a per-page health distribution that flags the worst pages, wikilink-graph health, citation coverage/precision, corpus stats, regression deltas, and optional judge-model citation support.llmwiki serve exposes ingest, compile, query, lint, read, status, eval, context-pack, and OKF exchange tools to MCP-compatible agents.createWiki({ root }) drives ingest, compile, query, context, status, export, eval, and OKF import/export from TypeScript without shelling out.llms.txt for downstream systems.CLP turns llmwiki's knowledge compiler into a reusable substrate for domain-specific knowledge systems. A validated .llmwiki/profile.json is the single contract for:
These rules are enforced by the runtime, not left as prompt conventions. The CLI, SDK, MCP server, viewer, context builder, lint, status, export, and OKF exchange surfaces all operate from the same profile contract. Invalid profiles and writes that bypass a declared gate fail closed.
CLP is backward-compatible by construction: a project without .llmwiki/profile.json uses the built-in default concepts-and-queries profile and preserves the pre-1.0 behavior. You can start three ways — scaffold your own profile, install a built-in or local template, or install a signed template from a trusted tap:
# author your own profile, one entity type at a time
llmwiki profile init research --entity paper
# or install a built-in or local declarative template
llmwiki template list
llmwiki template inspect autosci
llmwiki template init autosci
llmwiki profile validate
llmwiki workflow list
autosci is a practical research system with papers, ideas, experiments, manuscripts, evidence artifacts, workflows, and Crossref ingestion. newsroom applies the same generic machinery to articles, desks, bylines, and editorial workflows. Templates contain configuration and examples, never executable plugin code.
Templates can also be distributed securely. Publishers build signed, offline distributions with llmwiki template publish — Ed25519 signing, key rotation, and package revocation — and verify them with template publish verify. Consumers add explicitly trusted taps, discover and inspect signed catalogs, and install or update templates with continuity, revocation, and compatibility checks enforced under lock.
Read the CLP concept guide, follow the AutoSci research workflow, or explore the Newsroom editorial workflow.
Andrej Karpathy described the LLM Wiki pattern as a way to turn raw material into compiled knowledge that future agents can reuse. llmwiki is a concrete compiler for that pattern.
The key shift is moving work from query time to compile time. Traditional RAG repeatedly retrieves raw chunks and asks the model to reconstruct relationships for each question. llmwiki first turns sources into typed, interlinked pages with citations, metadata, and review state. Queries, context packs, exports, and MCP t