evolve-learning-extractor

A learning-extraction agent that turns useful discoveries from a conversation, such as a debugging solution, into reusable agents, skills, or rules.

In plain words
What is it for?
Use it after debugging or other discoveries to identify candidate lessons, review them, and create approved toolkit components.
Why use it?
It prevents valuable problem-solving patterns from being lost when a session ends.

Agent

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 agents/hknc/claude-evolve/evolve-learning-extractor
Clone the repo
git clone --depth 1 https://github.com/hknc/claude-evolve
Per session 289 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,393 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00289 $0.04393
Opus 5 $0.00144 $0.02197
Sonnet 5 $0.00058 $0.00879
Haiku 4.5 $0.00029 $0.00439

Measured 2d ago against content hash cc9ac72afad0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

plugins/claude-evolve/agents/evolve-learning-extractor.md · 527 lines

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
    }
  ]
}

Read the full file on GitHub · 527 lines

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. 2d ago First seen · 527 lines · 289 tokens per session scan A cc9ac72afad0

Subscribe to this mod's changes

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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