capability-evolver

A workflow that lets a coding agent remember outcomes from earlier tasks in the same workspace and use them during later work.

In plain words
What is it for?
Use it during substantial feature work, fixes, and refactors, or when asking the agent to learn from a task.
Why use it?
It reduces repeated problem-solving by recording what worked, what failed, and where improvements were needed in a local memory file.

Skill for Claude CodeCodex

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 skills/evomap/evolver-claude-code-plugin/capability-evolver
Any agent
npx skills add EvoMap/evolver-claude-code-plugin --skill capability-evolver
Clone the repo
git clone --depth 1 https://github.com/EvoMap/evolver-claude-code-plugin

Made for: Claude Code, Codex.

Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,108 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 $0.00083 $0.01108
Opus 5 $0.00042 $0.00554
Sonnet 5 $0.00017 $0.00222
Haiku 4.5 $0.00008 $0.00111

Measured yesterday against content hash f8859e6a36aa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

capability-evolver 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.

skills/capability-evolver/SKILL.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Capability Evolver

This plugin gives the agent a persistent, auditable evolution memory built on the Genome Evolution Protocol (GEP). The goal is simple: stop re-solving the same problem from scratch. Past outcomes — what worked, what failed — are carried forward into future sessions.

How it works (automatic)

Three hooks run on their own; you don't invoke them:

  • SessionStart — injects a short summary of recent successful outcomes for this workspace (filtered to score ≥ 0.5, < 7 days old, max 3) as context. The agent sees "here's what worked recently" before it starts.
  • PostToolUse (Write/Edit) — scans edits for improvement signals (log_error, perf_bottleneck, capability_gap, test_failure, …) and nudges the agent to record the outcome when relevant.
  • Stop — at the end of a task, collects the git diff, classifies the outcome, and appends it to the evolution memory graph (scoped to the workspace so other projects' memory never leaks in).

Memory is written to a local JSONL graph. With no extra setup it lands in ~/.evolver/memory/evolution/memory_graph.jsonl; inside an evolver-managed project it lands under that project's memory/evolution/.

What you (the agent) should do

For any substantive task — a feature, a non-trivial fix, a refactor:

  1. Before starting, check the injected evolution memory (it arrives as session-start context). If a recent successful outcome matches the task, reuse that approach. If a recent failure matches, avoid repeating it.
  2. Do the work.
  3. After finishing, the Stop hook records the outcome automatically. You don't need to call anything — but if the task had a clear lesson worth a one-line note, say so in your final message so it's captured in the diff context the hook reads.

Trivial or purely conversational turns don't need this — skip it.

Signals

The hooks classify work by signal. Knowing the vocabulary helps you describe outcomes in terms the memory graph indexes well:

Read the full file on GitHub · 94 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. yesterday First seen · 94 lines · 83 tokens per session scan A f8859e6a36aa

Subscribe to this mod's changes

capability-evolver is a skill published in the GitHub repository EvoMap/evolver-claude-code-plugin (12 stars, last pushed 12d ago), licensed MIT. It adds 83 tokens to every session and 1,108 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 2026-08-30.

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