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.
npx agentmods add commands/terrene-foundation/metis/learngit 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.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00710 |
| Opus 5 | $0.00000 | $0.00355 |
| Sonnet 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 3d 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.
- 3d 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 4mo 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.