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/ankushdixit/claude-plugins/learngit clone --depth 1 https://github.com/ankushdixit/claude-pluginsWhat 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 | $0.00006 | $0.01176 |
| Opus 5 | $0.00003 | $0.00588 |
| Sonnet 5 | $0.00001 | $0.00235 |
| Haiku 4.5 | $0.00001 | $0.00118 |
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 yesterday.
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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Capture
Record insights, gotchas, and best practices discovered during development.
Step 1: Analyze Session and Generate Learning Suggestions
Review what was accomplished in the current session:
- What code was written/changed
- What problems were solved
- What patterns or approaches were used
- What technical insights were discovered
- What gotchas or edge cases were encountered
Generate 2-3 learning suggestions based on the session work. Good learnings are:
- Specific technical insights (not generic)
- Actionable and memorable
- About tools, patterns, or gotchas encountered
- Clear and concise (1-2 sentences)
Step 2: Ask User to Select Learnings
Use AskUserQuestion with multi-select to let user choose learnings:
Question: Select Learnings from This Session
- Question: "I've identified some potential learnings from this session. Select all that apply, or add your own:"
- Header: "Learnings"
- Multi-select: true
- Options (up to 4 total):
- Option 1: [Your generated learning suggestion 1]
- Option 2: [Your generated learning suggestion 2]
- Option 3: [Your generated learning suggestion 3] (if applicable)
- Option 4: [Your generated learning suggestion 4] (if applicable)
Example Options:
- "TypeScript enums are type-safe at compile time but add runtime overhead"
- "Zod schemas can be inferred as TypeScript types using z.infer<>"
- "React useCallback dependencies must include all values used inside the callback"
Step 3: For Each Selected Learning, Determine Category
For each learning the user selected (or entered), automatically suggest the most appropriate category:
Categories:
architecture_patterns- Design decisions, patterns used, architectural approachesgotchas- Edge cases, pitfalls, bugs discoveredbest_practices- Effective approaches, recommended patternstechnical_debt- Areas needing improvement, refactoring neededperformance_insights- Optimization learnings, performance improvementssecurity- Security-related discoveries, vulnerabilities fixed
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.
- yesterday First seen · 149 lines · 6 tokens per session scan A 63810c63ffc6
learn is a command published in the GitHub repository ankushdixit/claude-plugins (3 stars, last pushed 7mo ago), licensed MIT. It adds 6 tokens to every session and 1,176 once invoked, about $0.0000 per session on Opus 5. 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
learn
Force claude-smart to extract learnings from this session now.
component
Scaffold a new React component grounded in the paper-mono primitives. Requires explicit kind or a nearest-existing-component match. No empty divs, no speculative scaffolding.
memory-store
Store an insight, decision, or pattern to memory.
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.
no-vibe
Enter no-vibe mode in OpenCode (tutor mode, no direct project file writes).
teach-me-testing
Teach testing progressively through structured sessions. Use when user says ""lets learn testing"" or ""I want to study test practices"".