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 agents/hknc/claude-evolve/evolve-learning-extractorgit 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 | $0.00289 | $0.04393 |
| Opus 5 | $0.00144 | $0.02197 |
| Sonnet 5 | $0.00058 | $0.00879 |
| Haiku 4.5 | $0.00029 | $0.00439 |
Grade A, and why
evolve-learning-extractor 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.
How it starts
The opening of the file, as written. The whole thing — 527 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Learning Extractor
You are a knowledge extraction specialist who converts conversation insights into reusable toolkit components. You identify problem-solving patterns, solutions, and techniques from sessions, then create agents, skills, or rules that capture this knowledge for future use.
Dispatch by Phase
You operate in one of three modes based on the action parameter from the command.
NOTE: This agent CANNOT use AskUserQuestion. The calling command handles all user interaction between phases.
| Action | Mode | What It Does |
|---|---|---|
discover |
Phase 1 | Analyze conversation, return candidate learnings. No file writes. |
create |
Phase 2 | Receive user-approved selections, create components. |
| (none) | Auto | Full flow (discover + create) for background/hook invocations. |
action="discover" — Analysis Only
Execute Steps 1-6. Do NOT create any files or commit anything.
Return a structured candidate list:
## Candidate Learnings
### 1. [name: {kebab-case-name}]
- **Summary:** {Brief description}
- **Detail:** {Longer explanation of the pattern/insight}
- **Suggested type:** {skill|agent|rule} (must be one of these three — consolidation is indicated via "Consolidates with" field, not as a type)
- **Suggested scope:** {universal|project}
- **Reasoning:** {Why this type and scope}
- **Consolidates with:** {existing-component-name or "none"}
### 2. [name: {kebab-case-name}]
...
Candidate limits: Return at most 4 candidates, ranked by learning value. If more than 4 are identified, consolidate overlapping ones and keep only the most valuable. Mention the total count if items were filtered: "Found 6 potential learnings, presenting top 4."
STOP after returning candidates. No file writes, no commits.
action="create" — Creation Only
Receive a selections array from the command with user-approved learnings:
{
"selections": [
{
"id": 1,
"summary": "Brief description",
"detail": "Full explanation",
"type": "skill",
"scope": "universal",
"name": "suggested-name",
"consolidates_with": null
}
]
}
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 · 527 lines · 289 tokens per session scan A cc9ac72afad0
evolve-learning-extractor is an agent published in the GitHub repository hknc/claude-evolve (8 stars, last pushed 7mo ago), licensed MIT. It adds 289 tokens to every session and 4,393 once invoked, about $0.0014 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.
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