LLM Wiki — LLM-compiled knowledge bases for Claude Code, Codex, OpenCode & any LLM agent

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LLM-compiled knowledge bases for<br>any AI agent with awesome outputs.

Parallel multi-agent research. Thesis-driven investigation. Source ingestion.<br>Wiki compilation. Session memory. Feedback curation. Topic archiving. Inventory tracking.<br>Dataset manifests. Truth-seeking audits. Querying. Artifact generation. Ships as a<br>Claude Code plugin, an OpenAI Codex plugin, an OpenCode instruction file, or a<br>portable AGENTS.md. Obsidian-compatible.

Install<br>Source on GitHub<br>Quick Start<br>Sessions

Every run compounds. Sources become cross-referenced articles.<br>Articles become reports, slide decks, study guides, playbooks,<br>and implementation plans. Session digests keep the agent oriented without<br>turning private chats into topic evidence.

What it does

One command spins up a topic wiki, dispatches up to ten agents,<br>ingests what's worth keeping, collects provenance-rich catalogs before tracking them, archives old topics without deleting them, tracks durable follow-up state, indexes<br>large datasets without copying them, captures redacted session context and feedback candidates without hoarding transcripts, compiles sources into articles, and<br>hands you a deliverable built on top. All plain Markdown you own.

Research

5–10 parallel agents search academic, technical, applied, news, and contrarian angles. --min-time 2h keeps going in rounds, drilling into gaps each round finds.

Thesis mode

Start from a claim. Agents split across supporting, opposing, mechanistic, meta, and adjacent angles. Output is a verdict — not a summary. Round two fights confirmation bias.

Ingest

URLs, files, PDFs, inbox drops, Git doc repos, MediaWiki dumps, message archives, and Wayback CDX snapshots. Raw sources stay immutable; articles synthesize on top.

Collect

Find, dedupe, download bounded public media, and catalog discoverable artifacts, examples, memes, tools, entities, and source candidates. Captures aliases, found-in-context provenance, local asset paths, hashes, scale, media policy, and inventory fit.

Inventory

Track durable things the wiki should remember: items, source candidates, corpora, entities, open questions, watch items, and next actions. Chat views default to compact tables.

Datasets

Index large, external, mutable, or operational data with manifests, samples, profiles, and query recipes. The wiki becomes the interface; the data stays where it belongs.

Archive

Move whole topic wikis to topics/.archive/. Preserved knowledge stays structurally maintainable but out of default query, compile, research, collect, output, and maintenance context.

Compile

Raw sources become synthesized articles with cross-references and confidence scores. Every directory has an _index.md — nothing is scanned blindly.

Query

Quick (indexes), standard (articles), or deep (everything + sibling wikis). --resume picks up where you left off.

Sessions

Default-on hook capture writes redacted events, state JSON, and Markdown digests under .sessions/. Rehydrate future turns with compact context; promote only what belongs in a topic.

Feedback

Curates high-signal corrections, preferences, approvals, and plan acceptance under .sessions/feedback/. Generic acknowledgements are ignored; durable lessons are explicitly promoted.

Librarian

Score every article for staleness and quality. Two-tier scan: fast metadata check, then deep content read for flagged articles. Checkpoint recovery. Machine-readable JSON + human-readable report.

Audit

Answer the broader trust question. Reuse the librarian pass, trace outputs across raw/, wiki/, and output/, detect drift, inspect provenance, and do fresh research when local evidence is not enough.

Lessons

Extract lessons learned from the current session — error→fix patterns, user corrections, discoveries. Saved as structured notes the wiki can query later. --rules emits enforceable rules instead of prose.

Plan

Wiki-grounded implementation plans. Reads the knowledge base, interviews you about requirements, fills gaps with targeted research, and produces a phased plan citing wiki articles as evidence. --format rfc|adr|spec.

Output

Reports, slide decks, study guides, playbooks, implementation plans, timelines, glossaries, comparisons. Filed back into the wiki so the next output builds on every previous one.

Install

Claude Code

Native plugin. Recommended.

claude plugin install wiki@llm-wiki<br>Installs from the public marketplace. Restart Claude Code to apply.

OpenAI Codex

Marketplace plugin. Invoke with @wiki.

codex plugin marketplace add nvk/llm-wiki<br># Then open /plugins, enable "LLM Wiki", use @wiki<br>Or from a local checkout:...