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/feedback)<a href="https://agentmods.dev/commands/nvk/llm-wiki/feedback"><img src="https://agentmods.dev/badge/commands/nvk/llm-wiki/feedback/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/feedback"><img src="https://agentmods.dev/badge/commands/nvk/llm-wiki/feedback.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 | $0.00015 | $0.00553 |
| Opus 5.5 | $0.00006 | $0.00221 |
| Sonnet 5.5 | $0.00003 | $0.00111 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
feedback scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your task
Manage the feedback-curator layer for llm-wiki sessions. Feedback candidates are
small distilled records of user corrections, preferences, approvals, and plan
acceptance signals. They live under HUB/.sessions/feedback/ until explicitly
promoted into a topic wiki.
First read references/feedback.md and references/sessions.md, then resolve
HUB by the standard hub-resolution protocol from references/hub-resolution.md.
Prefer the deterministic helper when available:
scripts/llm-wiki-session --hub "$HUB" feedback <subcommand>
Parse $ARGUMENTS
list [--unpromoted] [--type correction|preference|approval|decision|manual] [--min-confidence low|medium|high]: show captured candidates.show <candidate-id>: display one candidate with its distilled lesson and redacted user-feedback preview.capture --text "..." [--session-id <id>]: manually add a feedback candidate.promote <candidate-id> --topic <slug>: write the candidate into the target topic'sraw/notes/layer and append the topic log.
Policy
- Candidate capture is allowed from trusted hooks, but promotion is explicit.
- Do not treat generic acknowledgements (
ok,thanks,cool) as durable lessons. - High-value feedback includes corrections, preferences,
always/neverrules, and explicit statements that the agent did the right thing. - Approval/decision candidates are validation of the immediately preceding context unless corroborated by a session digest.
- Store redacted previews and hashes, not full transcripts.
Examples
scripts/llm-wiki-session --hub "$HUB" feedback list --unpromoted
scripts/llm-wiki-session --hub "$HUB" feedback show fb-abc123
scripts/llm-wiki-session --hub "$HUB" feedback capture --text "next time use the release checklist before tagging"
scripts/llm-wiki-session --hub "$HUB" feedback promote fb-abc123 --topic meta-llm-wiki
Report absolute paths for every promoted note. Append topic log.md when
promoting into a topic wiki.
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 · 48 lines · 15 tokens per session scan A 473b04713fc3
feedback is a command published in the GitHub repository nvk/llm-wiki (1,429 stars, last pushed 16d ago), licensed MIT. It adds 15 tokens to every session and 553 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
flow-nexus-user-tools
User management, storage, and system utilities.
flow-nexus-auth
Flow Nexus authentication and user management.
gtd
Canonical GTD entry point for capture, clarification, organization, review, and engagement.
prune-learnings
Manual command to invoke the pruning script for cleaning up old learnings.
pull-learnings
Pulls shared organization patterns into local org-patterns.toon.
end-session
Wrap up the work session with read-only evidence gathering, owner-classified documentation handoffs, gated issue sync, Kano refinement, and git hygiene checks.