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/cloudnative-co/claude-code-starter-kit/learngit clone --depth 1 https://github.com/cloudnative-co/claude-code-starter-kitWhat 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 | $0.00432 |
| Opus 5 | $0.00000 | $0.00216 |
| Sonnet 5 | $0.00000 | $0.00086 |
| Haiku 4.5 | $0.00000 | $0.00043 |
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 2d 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.
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
Create a skill at ~/.claude/skills/learned/<pattern-name>/SKILL.md so the
current skill discovery mechanism (directory + SKILL.md with YAML
frontmatter) can load it:
---
name: <pattern-name>
description: <one-line description of the problem this solves>
when_to_use: Use when <trigger condition for this pattern>
---
# [Descriptive Pattern Name]
## Problem
[What problem this solves - be specific]
## Solution
[The pattern/technique/workaround]
## Example
[Code example if applicable]
Process
- Review the session for extractable patterns
- Identify the most valuable/reusable insight
- Draft the SKILL.md
- Ask user to confirm before saving
- Save to
~/.claude/skills/learned/<pattern-name>/SKILL.md
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
- Routine learnings are captured automatically by auto-memory; use /learn only to promote a pattern into an activatable 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.
- 2d ago First seen · 75 lines · 0 tokens per session scan A 3582e4684dd4
learn is a command published in the GitHub repository cloudnative-co/claude-code-starter-kit (147 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 432 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 2026-08-30.
Other commands, from other repositories
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.