Borrowing it
Nothing to install: this file belongs to terrene-foundation/metis. 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/terrene-foundation/metis/main/.claude/commands/learn.mdgit clone --depth 1 https://github.com/terrene-foundation/metisWrote 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/terrene-foundation/metis/learn)<a href="https://agentmods.dev/commands/terrene-foundation/metis/learn"><img src="https://agentmods.dev/badge/commands/terrene-foundation/metis/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/terrene-foundation/metis/learn"><img src="https://agentmods.dev/badge/commands/terrene-foundation/metis/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 | $0.00000 | $0.00710 |
| Opus 5.5 | $0.00000 | $0.00284 |
| Sonnet 5.5 | $0.00000 | $0.00142 |
| Haiku 4.5 | $0.00000 | $0.00071 |
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 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/learn - Learning System Status
Purpose
View the learning digest and codification history. The learning system captures meaningful signals (user corrections, rule violations, session accomplishments, journal decisions) and feeds them into /codify for integration into real artifacts.
Quick Reference
| Command | Action |
|---|---|
/learn |
Show learning digest summary |
/learn stats |
Show observation statistics and breakdown |
Usage
View Learning Digest
Read .claude/learning/learning-digest.json and present:
- Corrections — Times the user pushed back or redirected. These are the most valuable signals — each represents a gap in the current artifacts.
- Error patterns — Recurring rule violations (which rules are being violated most?).
- Accomplishments — What was completed in recent sessions.
- Decisions — Journal entries (DECISION, DISCOVERY, TRADE-OFF) that may need codification.
- Active frameworks — Which Kailash frameworks are in use.
View Codification History
Read .claude/learning/learning-codified.json to see what /codify has already processed from the digest.
View Stats
node scripts/learning/digest-builder.js --stats
How It Works
- Hooks capture signals — User corrections (UserPromptSubmit), rule violations (PostToolUse), session accomplishments (SessionEnd), journal decisions (SessionEnd). Pure file I/O, no LLM.
- Digest builder aggregates — At session end, observations are summarized into
learning-digest.json. Pure aggregation, no pattern matching or confidence scores. - /codify does the thinking — When
/codifyruns, the LLM reads the digest, journals, and session notes. It decides what to codify into real rules, skills, or agents. No intermediate staging — changes go directly into canonical artifact locations.
File Locations
<project>/.claude/learning/
observations.jsonl # Raw observations (capped at 500, auto-archived)
observations.archive/ # Archived observations
learning-digest.json # Structured summary for /codify
learning-codified.json # What /codify has already processed
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 · 68 lines · 0 tokens per session scan A ba378d9ca780
learn is a command published in the GitHub repository terrene-foundation/metis (2 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 710 tokens. 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 2026-08-31.
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