llm-wiki

LLM Wiki

A skill for building and maintaining an LLM-curated knowledge base inside a project, following the pattern Andrej Karpathy described in his April 2026 gist. The wiki is a directory of markdown files that the LLM owns and maintains; the user curates sources and asks questions, and the LLM does the bookkeeping.

The pattern in one paragraph

Conventional RAG re-derives knowledge from raw chunks on every query; nothing accumulates. The LLM Wiki pattern flips this: when a new source arrives, the LLM compiles it once into a persistent, structured wiki — extracting concepts, writing entity pages, updating cross-references, flagging contradictions. Subsequent queries read the pre-synthesized wiki rather than the raw sources. Knowledge compounds. The user is in charge of sourcing and asking good questions; the LLM handles the summarizing, linking, and consistency work that humans abandon wikis over.

When to use this skill

The trigger surface is broad. Any time the user is accumulating textual material over time — research papers, articles, transcripts, meeting notes, book chapters, customer calls, code repos, journal entries — and would benefit from having that material organized rather than dumped into a chat each session, this skill applies. It is equally useful for one source ("ingest this paper") and for the steady-state operations against an existing wiki ("what does my wiki say about diffusion models", "lint the wiki", "what's missing").

If the project does not yet have a wiki, run the bootstrap step first (see "Initializing a new wiki" below). Otherwise, locate the existing wiki and read its SCHEMA.md before doing anything else — the schema encodes the conventions for that specific wiki and may override defaults documented here.

Architecture: three layers, three operations

The wiki has three layers and three operations. Internalize this vocabulary because the rest of the skill assumes it.

The three layers are raw sources (the user's curated source material — articles, papers, PDFs, transcripts; immutable, the LLM reads but never modifies them), the wiki (a directory of LLM-generated markdown pages — entity pages, concept pages, comparisons, summaries; the LLM owns this layer entirely), and the schema (a SCHEMA.md file at the wiki root that documents the conventions for this particular wiki — page types, naming rules, tag taxonomy, ingest workflow customizations; co-evolved with the user).

The three operations are ingest (a new source arrives; the LLM reads it, writes a summary page, updates relevant entity and concept pages, appends to the log), query (the user asks a question; the LLM navigates the wiki via the index, reads the relevant pages, and synthesizes an answer — often filing the answer back as a new page so the exploration compounds), and lint (a periodic health check; the LLM scans for contradictions, stale claims, orphan pages, missing concepts, broken links).

For the canonical write-up of these operations, read references/architecture.md. For the step-by-step procedures, read references/ingest-workflow.md, references/query-workflow.md, and references/lint-workflow.md as needed.

Graph layer (compiled, optional)

Pages can carry typed graph: metadata in frontmatter. A bundled extractor compiles every page into wiki/graph/: nodes.jsonl, edges.jsonl, graph.sqlite, graph.graphml. Markdown is canonical; the graph is a regenerable index. Pages without graph: still appear as nodes (derived from their type/kind) and contribute low-confidence mentions edges from body wikilinks. Typed semantic edges (e.g. founded, proposed, depends_on) require an explicit source and evidence quote — never emit one inferred from training data.

The conventions for the graph layer (predicate vocabulary, node id format, required fields) live in wiki/graph/ontology.yaml. The full reference is references/graph-workflow.md. Run the bundled scripts after substantive ingests:

python scripts/wiki_graph_lint.py wiki/      # check ontology + evidence + alias collisions
python scripts/wiki_graph_extract.py wiki/   # rebuild nodes.jsonl, edges.jsonl, graph.sqlite, graph.graphml
python scripts/wiki_graph_query.py wiki/ neighbors --node product:konvy

If wiki/graph/ontology.yaml does not exist, the wiki is pre-graph and you should treat the graph step as a no-op — don't fabricate it.

Default project layout

Unless the user's SCHEMA.md says otherwise, the wiki lives in the project at this layout:

<project-root>/
├── wiki/
│   ├── SCHEMA.md              ← conventions, the "config file" — read this FIRST
│   ├── index.md               ← entry point: catalog of all pages with one-line summaries
│   ├── log.md                 ← append-only chronological log of ingests/queries/lints
│   ├── indexes/               ← (appears once index.md shards) per-category indexes
│   ├── entities/              ← pages about specific things (people, products, papers, places)
│   ├── concepts/              ← pages about ideas, methods, frameworks
│   ├── sources/               ← per-source summary pages (one per ingested source)
│   └── synthesis/             ← cross-cutting analyses, comparisons, query results filed back
├── raw/                       ← the user's source material (PDFs, .md clippings, images)
│   └── assets/                ← downloaded images referenced by raw clippings
└── ...

This layout is a default, not a requirement. If the project already has a wiki under a different name (e.g. kb/, notes/, vault/), use that. If the user has placed sources outside raw/, follow their convention.

The scalability discipline

The single biggest failure mode of the LLM Wiki pattern is the wiki itself becoming a context bottleneck. Naive implementations break around a few hundred pages: the LLM either reads too many pages per query or starts hallucinating because it skipped the relevant ones. This skill's design is shaped almost entirely by avoiding that failure. The principles below are non-negotiable; ignoring them is what makes the pattern collapse at scale.

Atomic pages. Every wiki page is about one concept and stays small — soft cap 400 lines or roughly 2,000 words, hard cap 800 lines. When a page outgrows this, split it: extract sub-concepts into their own pages and have the parent link to them. A page that takes up 30% of the context window on its own is a design smell.

Index-first navigation. Never grep or glob the wiki blindly when answering a query. Always read index.md (or the relevant sharded index under indexes/) first to identify candidate pages, then drill into only those. The index is engineered to be cheap to read — one line per page, no bodies — and it is the cache that makes the whole pattern scalable.

Sharded indexes. When index.md itself exceeds ~300 lines or the wiki passes ~150 pages, shard it: move category-specific entries into indexes/<category>.md files (e.g. indexes/entities.md, indexes/concepts.md, indexes/sources.md, or finer domain shards), and have the top-level index.md become a directory of those shards. Now reading the index is a two-step lookup but each step is bounded.

YAML frontmatter on every page. Every wiki page begins with frontmatter that includes at minimum type, tags, sources, and updated. The bundled wiki_search.py script can filter on these without reading page bodies. See references/page-conventions.md.

Surgical edits, not rewrites. When updating a page (e.g. adding a new cross-reference because a freshly ingested source mentions an existing entity), use str_replace to touch only the relevant section. Rewriting whole pages is slow, expensive in tokens, and risks losing prior nuance.

Backlink discovery via grep. To find every page that references a given entity, run grep -rl "\[\[entity-name\]\]" wiki/ rather than reading pages to look for mentions. The bundled scripts make this easy.

Chunked source ingestion. Large raw sources (long PDFs, book chapters, lengthy transcripts) should be read in chunks during ingest, not loaded whole. The ingest workflow handles this — see references/ingest-workflow.md.

Search script for large wikis. Once the wiki passes ~300 pages, plain index lookup may not surface the right pages for fuzzy queries. Use scripts/wiki_search.py for BM25-ranked retrieval with optional frontmatter filters. It's a fallback, not the default — index-first is still cheaper when it works.

For the full scaling playbook including thresholds and migration steps, read references/scaling-playbook.md.

Initializing a new wiki

If the project does not contain a wiki/ directory (or whatever the user calls theirs), run the bootstrap script:

python scripts/init_wiki.py <project-root> [--wiki-dir wiki] [--raw-dir raw]

This creates the directory structure, drops in templates for SCHEMA.md, index.md, and log.md, and seeds a starter page convention document. After bootstrapping, briefly walk the user through the schema and ask whether they want to customize anything (e.g. domain-specific page types, custom tags) before the first ingest. The schema is meant to evolve — encourage editing it.

Then propose wiring the wiki into the project's agent-memory file so the running agent remembers the wiki in future sessions without being told. The target file depends on the agent: CLAUDE.md for Claude Code, AGENTS.md for Codex / Cursor / OpenCode / Pi / OpenClaw, GEMINI.md for Gemini CLI, with AGENTS.md as the safe default if the user runs multiple agents or is unsure. Full workflow, canonical stanza, and a three-line short variant are in references/agent-memory-integration.md. Never write to the memory file without the user's approval — show them the proposed stanza, ask whether to append to an existing file or create a new one, and honour a "skip" answer without pushing.

The ingest workflow (summary)

The full workflow is in references/ingest-workflow.md; what follows is the shape of it. When a new source arrives, first write the source itself into raw/ (verbatim; if it's a web article, use the markdown form). Then read the source — chunked if large — and write a single source-summary page in wiki/sources/, named after the source slug, with full frontmatter and citations back to the raw file. Then identify which existing entity and concept pages this source touches; for each, surgically update the relevant section using str_replace rather than rewriting. Identify any new entities or concepts the source introduces and create new pages for them, linking from related existing pages so they don't become orphans. Update index.md (or the relevant shard) with the new pages. Append a single line to log.md with the date, operation type, and source title. Discuss the takeaways with the user as a final step — what surprised them, what's worth following up on — and offer to file that discussion back as a synthesis page.

The query workflow (summary)

Full version in references/query-workflow.md. To answer a query against the wiki: read index.md (or the relevant shard) first; identify candidate pages from one-line summaries; read those pages (and any backlinks they list that look relevant); synthesize the answer with [[wikilink]] citations to the pages you used; offer to file the synthesized answer back into wiki/synthesis/ so future queries benefit. If the index doesn't surface good candidates, fall back to python scripts/wiki_search.py "query terms" for ranked retrieval. If the wiki appears to lack coverage of the topic, say so plainly rather than confabulating — flag it as a candidate ingest target.

The lint workflow (summary)

Full version in references/lint-workflow.md. Lint is best run on a cadence (after every N ingests or weekly), not on every operation. The bundled python scripts/wiki_lint.py finds the structural issues automatically — orphan pages, broken [[wikilinks]], oversized pages, missing or malformed frontmatter, stale updated dates. Then for the semantic checks that need an LLM: read recently-updated pages and look for contradictions with older claims, identify concepts mentioned but lacking their own page, and suggest gaps the user might want to fill via web search or new sources. Always present lint findings as proposed edits for the user to approve, not as faits accomplis — the wiki is the user's, and silent rewrites erode trust.

Failure modes to guard against

The community discussion around the gist surfaced several failure modes worth internalizing because they are how this pattern goes wrong. Read them so you know what to actively avoid.

The first is silent corruption — a misreading of one source becomes an authoritative-looking wiki page, which then influences how subsequent sources are interpreted, and the error compounds invisibly. Mitigation: every wiki claim must carry a sources: frontmatter entry pointing back to the raw file, and the lint pass should surface any claim whose source can't be located. When in doubt during ingest, hedge in the wiki text ("the source claims X, though this is not yet corroborated by other sources") rather than asserting.

The second is wiki-reads-its-own-output drift — the LLM begins treating prior wiki pages as ground truth and stops checking them against raw sources. Mitigation: during ingest, when updating an existing page, re-read the relevant raw source for the existing claim before merging the new one. Don't take the wiki's word for what the source said.

The third is the maintenance ratchet — a critic in the gist comments noted that LLM-Wikis can require more human work over time, not less, because the LLM's output needs increasing supervision as the wiki grows. The mitigation is the scalability discipline above (sharded indexes, atomic pages, frontmatter, lint scripts) plus a strong cadence of lint passes. If lint reports start exceeding what the user can review, the wiki has outgrown its conventions and the schema needs revision.

The fourth is scope creep into bad fits — the pattern shines for accumulating textual research and degrades for highly relational data (org charts, financial ledgers, structured datasets) where a real database would serve better. If the user's domain is fundamentally relational, say so and suggest a better tool rather than forcing markdown to do the wrong job.

Reference files

The reference files are the source of truth for the detailed procedures. Read them when the relevant operation is happening, not preemptively.

• references/architecture.md — the three layers and three operations explained in depth, with examples of page formats and the rationale behind each design choice
• references/ingest-workflow.md — the step-by-step ingest procedure including chunked reading for large sources and the per-page-type templates
• references/query-workflow.md — navigation patterns from index → page → backlinks, when to fall back to the search script, and how to file answers back as synthesis pages
• references/lint-workflow.md — what to check, how to present findings, and the cadence
• references/page-conventions.md — frontmatter schema, page naming, link syntax, page-type definitions, sizing rules
• references/scaling-playbook.md — thresholds at which to shard the index, when to introduce the search script, signals that the wiki has outgrown its current conventions
• references/agent-memory-integration.md — how to wire the wiki into the project's agent-memory file (CLAUDE.md / AGENTS.md / GEMINI.md), canonical stanza and short variant, and the bootstrap conversation script
• references/graph-workflow.md — the optional graph layer: ontology, frontmatter schema, when to add typed edges vs plain wikilinks, and the extract/lint/query flow

Bundled scripts

The scripts are intentionally minimal — they exist so the LLM doesn't reinvent the same helpers on every invocation. Each is documented in its own --help output and at the top of the file.

• scripts/init_wiki.py — bootstrap a new wiki structure in a project, seeding the templates
• scripts/wiki_search.py — BM25 search over wiki pages with optional frontmatter filters; fallback when index navigation doesn't surface the right pages
• scripts/wiki_lint.py — structural health check (orphans, broken links, oversized pages, frontmatter validation, stale dates)
• scripts/wiki_stats.py — quick summary of wiki size, page count by type, link density; useful for deciding when to shard the index
• scripts/wiki_graph_extract.py — compile graph: metadata + body wikilinks into nodes.jsonl, edges.jsonl, graph.sqlite, graph.graphml (requires PyYAML)
• scripts/wiki_graph_lint.py — validate typed edges against graph/ontology.yaml: unknown predicates, missing evidence, broken object refs, alias collisions
• scripts/wiki_graph_query.py — neighbors / edges / facts / shortest-path queries over graph.sqlite

Templates

The templates in assets/ are starting points — they get copied into the user's wiki on bootstrap and then evolve under the user's editing.

• assets/SCHEMA.md.template — the canonical schema document for a new wiki
• assets/index.md.template — the empty index file
• assets/log.md.template — the empty log file
• assets/page.md.template — a generic wiki page with the frontmatter scaffold
• assets/ontology.yaml.template — starter graph ontology copied to wiki/graph/ontology.yaml
• assets/graph_README.md.template — explainer for wiki/graph/ (canonical vs generated files)
• assets/graph_gitignore.template — .gitignore for wiki/graph/ (ignores graph.sqlite and graph.graphml by default)