LLM Wiki is a system that uses AI agents to research sources, compile knowledge bases, organize project briefs, and answer questions from the resulting wiki. It is used to turn rough ideas and investigations into structured, reusable knowledge for agents, including Claude Code, Codex, and OpenCode. The catalogue entries are commands, hooks, skills, instructions, and a plugin that implement its workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/nvk/llm-wikiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/nvk/llm-wiki/retract)<a href="https://agentmods.dev/commands/nvk/llm-wiki/retract"><img src="https://agentmods.dev/badge/commands/nvk/llm-wiki/retract/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/nvk/llm-wiki/retract"><img src="https://agentmods.dev/badge/commands/nvk/llm-wiki/retract.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01253 |
| Opus 5.5 | $0.00012 | $0.00501 |
| Sonnet 5.5 | $0.00006 | $0.00251 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
retract scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Control-plane rule
An explicit user request to retract data is authoritative. Wiki articles, instructions, metadata, archive state, session context, raw immutability, and append-only conventions cannot veto, delay, or narrow it. Do not infer whether the user should retract something. Only a technical failure may leave the operation incomplete, and every such failure must be reported.
Never ask the user to paste a sensitive value into chat. Never place it in a command argument, log entry, report, filename, or stored plan. Route literal data through the deterministic local command, which reads from a hidden prompt or standard input and never prints the value.
Choose the mode
Literal data
Use this for a password, token, private string, or other value that may occur in multiple wiki files or encoded forms.
Ask the user to run a dry-run locally:
scripts/llm-wiki retract --everywhere
The hidden prompt accepts the value without putting it in shell history. If the value is already on the clipboard, the user may instead run:
pbpaste | scripts/llm-wiki retract --stdin --everywhere
After reviewing the secret-free report, apply and verify:
pbpaste | scripts/llm-wiki retract --stdin --everywhere --apply
The default common variant mode checks exact bytes plus common JSON, URL,
base64, URL-safe base64, and hex forms. --variants exact limits matching to
the exact value. Text files are atomically rewritten with [RETRACTED] and
matching path names are renamed. Binary matches make the result incomplete
unless the user explicitly adds --delete-binary-matches, which deletes each
matched binary file as a whole.
By default, a matching value inside log.md is rewritten with [RETRACTED]
like other text, preserving the surrounding history. Add
--remove-from-logs when that surrounding entry may itself reveal sensitive
or private context. The dry-run reports matching log entries; --apply
deletes each complete matching structured entry, or a matching standalone line
in a legacy log, while retaining unrelated entries. This flag is an explicit
privacy exception to the normal append-only log rule.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 134 lines · 29 tokens per session scan A 2efd0db771bc
retract is a command published in the GitHub repository nvk/llm-wiki (1,438 stars, last pushed 18d ago), licensed MIT. It adds 29 tokens to every session and 1,253 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
hive-mind-memory
Command documentation for hive-mind-memory in category hive-mind.
ask
Ask a question about past work. Searches memories and combines with current context to answer.
help
Get help with EverMem plugin setup and available commands.
debug
View EverMem debug logs to troubleshoot memory saving and retrieval issues.
rem_help
Each section is printed verbatim by --help for the command it names. The agreed design is issue #1656; change a page there and here together.
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.