learn

learn is a command for Claude Code from multiplex-ai/muggle-ai-teams. It costs 0 tokens per session (373 once invoked), scanned A, a copy of learn, MIT.

A command that examines the current session for reusable solutions and records them as skills or project instructions. It looks for error fixes, debugging methods, workarounds, and project-specific patterns.

In plain words
What is it for?
It is for turning solved problems and recurring development practices into written guidance for future work.
Why use it?
It prevents useful lessons from being lost after a difficult task is finished.

Command for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions CLAUDE.md.

Part of the muggle-ai-teams plugin — 28 commands, 29 agents shipped together

Install

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.

agentmods
npx agentmods add commands/multiplex-ai/muggle-ai-teams/learn
Clone the repo
git clone --depth 1 https://github.com/multiplex-ai/muggle-ai-teams

Made for: Claude Code.

Or install muggle-ai-teams, the plugin that ships this one along with the rest of its 28 commands, 29 agents.

Wrote 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.

agentmods badge for learn

README.md
[![agentmods](https://agentmods.dev/badge/commands/multiplex-ai/muggle-ai-teams/learn.svg)](https://agentmods.dev/commands/multiplex-ai/muggle-ai-teams/learn)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 373 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.00373
Opus 5 $0.00000 $0.00187
Sonnet 5 $0.00000 $0.00075
Haiku 4.5 $0.00000 $0.00037

Measured 6d ago against content hash 5a0556fb1fd2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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 6d 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.

Origin

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.

commands/learn.md · 71 lines

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:

  1. Error Resolution Patterns

    • What error occurred?
    • What was the root cause?
    • What fixed it?
    • Is this reusable for similar errors?
  2. Debugging Techniques

    • Non-obvious debugging steps
    • Tool combinations that worked
    • Diagnostic patterns
  3. Workarounds

    • Library quirks
    • API limitations
    • Version-specific fixes
  4. 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

  1. Review the session for extractable patterns
  2. Identify the most valuable/reusable insight
  3. Draft the skill file
  4. Ask user to confirm before saving
  5. 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
Changes

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.

  1. 6d ago First seen · 71 lines · 0 tokens per session scan A 5a0556fb1fd2

Subscribe to this mod's changes

learn is a command published in the GitHub repository multiplex-ai/muggle-ai-teams (2 stars, last pushed 3mo 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.