Borrowing it
Nothing to install: this file belongs to Lanzelot1/claw-brain. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Lanzelot1/claw-brain/main/.claude/commands/learn.mdgit clone --depth 1 https://github.com/Lanzelot1/claw-brainWrote 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/lanzelot1/claw-brain/learn)<a href="https://agentmods.dev/commands/lanzelot1/claw-brain/learn"><img src="https://agentmods.dev/badge/commands/lanzelot1/claw-brain/learn/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/lanzelot1/claw-brain/learn"><img src="https://agentmods.dev/badge/commands/lanzelot1/claw-brain/learn.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 | $0.00007 | $0.00561 |
| Opus 5.5 | $0.00003 | $0.00224 |
| Sonnet 5.5 | $0.00001 | $0.00112 |
| Haiku 4.5 | $0.00001 | $0.00056 |
Grade A, and why
learn 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 2mo 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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review the current conversation and save reusable insights to memory/lessons.md.
Steps
- Read
memory/lessons.md(if it exists) to see what's already saved - Scan the conversation for:
- Workarounds or solutions discovered through trial-and-error
- User corrections or preferences ("don't do that", "I prefer X")
- Patterns or conventions confirmed
- Mistakes worth avoiding next time
- Filter by priority:
- Always save: user corrections, stated preferences, confirmed conventions
- Save if non-trivial: debugging solutions, workflow patterns, tool quirks
- Skip: obvious facts, session-specific details, raw logs
- Check for duplicates — don't add entries that overlap with existing ones. Update existing entries if the new version is better.
- Append new entries to
memory/lessons.md. Format:- YYYY-MM-DD: [topic] — insight text - Graduation check — Review the extracted learnings for any insight that is a durable fact about the domain (not a personal preference, not a debugging workaround, not session-specific). Examples: "API rate-limits at 100 req/s", "The auth service requires JWT with RS256".
- For each durable insight, ask the user: "This looks like permanent knowledge rather than a session learning:
{insight}. Want me to add it toknowledge/{area}/instead?" - If yes: append to the most relevant existing knowledge file (don't create a new file). Add
source: "session discovery, YYYY-MM-DD"to the frontmatter if needed. Search other knowledge areas for related files and add cross-references using[title](../area/file.md)format under a## Relatedsection. - If no: leave it in
lessons.mdas normal
- For each durable insight, ask the user: "This looks like permanent knowledge rather than a session learning:
- If the file exceeds 50 entries, ask the user which older entries can be consolidated or removed
- Show a summary of what was saved (or "nothing new to save" if the session was routine)
Git
memory/lessons.md is gitignored — no branch or PR needed.
Rules
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.
- 2mo ago First seen · 41 lines · 7 tokens per session scan A c76cef33c9b0
learn is a command published in the GitHub repository Lanzelot1/claw-brain (11 stars, last pushed 6mo ago), licensed MIT. It adds 7 tokens to every session and 561 once invoked, about $0.0000 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
screenshot
Screenshot a URL or document and read it visually.
obsidian-checkin
Ask the owner whether old people and project notes still hold - shows what each note says and when it was last checked, re-asks until answered, saves the news.
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.
speckit.archive
Archive a feature specification into main project memory after merge, resolving gaps and conflicts.
memory-forget
Command "memory-forget" from waittim/MemoryCustodian, covering memory-forget and review the preview, then apply its plan id.
memory-query
Query an existing graphify graph before rereading the full corpus, using query, path, or explain flows.