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 skills/hknc/claude-evolve/learnnpx skills add hknc/claude-evolve --skill learngit clone --depth 1 https://github.com/hknc/claude-evolveWhat 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 | $0.00072 | $0.00845 |
| Opus 5 | $0.00036 | $0.00423 |
| Sonnet 5 | $0.00014 | $0.00169 |
| Haiku 4.5 | $0.00007 | $0.00085 |
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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Extraction
Action
Execute the /learn command flow, which orchestrates the learning-extractor agent in two phases with user selection in between.
Execution
- Check prerequisite: Verify
$HOME/.claude-evolve/activeexists. If not, tell user to run/evolve initfirst and stop. - Check explicit intent: If the user's request contains explicit type and scope (e.g., "save this as a universal rule"), pre-set those values and skip selection steps.
- Phase 1 - Discover: Spawn
claude-evolve:evolve-learning-extractorwith action="discover" and the user's trigger text as topic hint. Agent analyzes conversation context and session signals, returns at most 4 candidate learnings ranked by value. - Present candidates: Show discovered learnings to user as a numbered list with summary, suggested type, and scope.
- User selects: Use AskUserQuestion to let user choose which learnings to capture. Single candidate gets a shortcut (yes/change/skip). Multiple candidates use multiSelect with a "Capture all" option.
- Confirm settings: Use AskUserQuestion to ask "Accept suggested types/scopes or customize each?" If customizing, ask type then scope per learning with the recommended option listed first.
- Phase 2 - Create: Spawn
claude-evolve:evolve-learning-extractorwith action="create" and the user's approved selections (id, summary, detail, type, scope, name, consolidates_with per learning). - Report result: Show user what was created or consolidated. If Phase 2 fails, display error and suggest checking toolkit permissions.
See ${CLAUDE_PLUGIN_ROOT}/commands/learn.md for full implementation details.
Learning Types (Learnings become Components)
| Found | Becomes | Location |
|---|---|---|
| Problem-solving pattern | Skill | $HOME/.claude-evolve/toolkits/{name}/skills/{skill-name}/SKILL.md |
| Investigation method | Agent | $HOME/.claude-evolve/toolkits/{name}/agents/{name}.md |
| Code pattern/approach | Rule | $HOME/.claude-evolve/toolkits/{name}/rules/{name}.md |
| Improvement to existing | Consolidated | Merged into existing file |
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 · 70 lines · 72 tokens per session scan A 4e4de4575fc9
learn is a skill published in the GitHub repository hknc/claude-evolve (8 stars, last pushed 7mo ago), licensed MIT. It adds 72 tokens to every session and 845 once invoked, about $0.0004 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 skills, from other repositories
systematic-debugging
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brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…