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/multiplex-ai/muggle-ai-teamsWrote 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/multiplex-ai/muggle-ai-teams/learn)<a href="https://agentmods.dev/commands/multiplex-ai/muggle-ai-teams/learn"><img src="https://agentmods.dev/badge/commands/multiplex-ai/muggle-ai-teams/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/multiplex-ai/muggle-ai-teams/learn"><img src="https://agentmods.dev/badge/commands/multiplex-ai/muggle-ai-teams/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 | $0.00373 |
| Opus 5.5 | $0.00000 | $0.00149 |
| Sonnet 5.5 | $0.00000 | $0.00075 |
| Haiku 4.5 | $0.00000 | $0.00037 |
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.
This is a copy
91% identical to learn — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/learn - Extract Reusable Patterns
Analyze the current session and extract any patterns worth saving as skills.
Trigger
Run /learn at any point during a session when you've solved a non-trivial problem.
What to Extract
Look for:
-
Error Resolution Patterns
- What error occurred?
- What was the root cause?
- What fixed it?
- Is this reusable for similar errors?
-
Debugging Techniques
- Non-obvious debugging steps
- Tool combinations that worked
- Diagnostic patterns
-
Workarounds
- Library quirks
- API limitations
- Version-specific fixes
-
Project-Specific Patterns
- Codebase conventions discovered
- Architecture decisions made
- Integration patterns
Output Format
Add the pattern to the appropriate rules file or project CLAUDE.md:
# [Descriptive Pattern Name]
**Extracted:** [Date]
**Context:** [Brief description of when this applies]
## Problem
[What problem this solves - be specific]
## Solution
[The pattern/technique/workaround]
## Example
[Code example if applicable]
## When to Use
[Trigger conditions - what should activate this skill]
Process
- Review the session for extractable patterns
- Identify the most valuable/reusable insight
- Draft the skill file
- Ask user to confirm before saving
- Save to appropriate rules file (global pattern) or project CLAUDE.md (project-specific)
Notes
- Don't extract trivial fixes (typos, simple syntax errors)
- Don't extract one-time issues (specific API outages, etc.)
- Focus on patterns that will save time in future sessions
- Keep skills focused - one pattern per skill
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 · 71 lines · 0 tokens per session scan A 5a0556fb1fd2
learn is a command published in the GitHub repository multiplex-ai/muggle-ai-teams (2 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 373 tokens. A static security scan graded it A with 0 findings. It is 91% identical to learn, differing in 4 lines, and is treated as a copy.
Other commands, from other repositories
retex
Retex - Capture lesson learned dans memory après fix, rollback, erreur.
dream
Nightly memory consolidation: prunes stale entries, merges duplicates, resolves contradictions, rebuilds the MEMORY.md index. Use when memory files accumulated over many sessions need cleanup. New decisions get stored by remember; searches run through memory; internals live in memory-fabric.
session-save
Save the current session state — decisions, modified files, current status, and next steps — to a handoff file for later resume.
traspasar
Generar documento de traspaso estructurado para retomar en la próxima sesión.
wrap-up
End-of-day ritual command. Prepares the day for closure and sets the stage for tomorrow.
memorizar
Guardar insights importantes para referencia futura.