evolve

A workflow for improving a program through repeated testing and code changes. It measures each version against stated goals and keeps the better results.

In plain words
What is it for?
Use it to run evolutionary optimization on a Git repository with a benchmark command, goals such as speed or accuracy, and an optional limit on trials.
Why use it?
It organizes experiments so you can compare changes fairly instead of guessing which code is better.

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/datalab-atom/evoany/evolve
Any agent
npx skills add DataLab-atom/EvoAny --skill evolve
Clone the repo
git clone --depth 1 https://github.com/DataLab-atom/EvoAny

Made for: Claude Code, Codex.

Per session 10 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 934 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 $0.00010 $0.00934
Opus 5 $0.00005 $0.00467
Sonnet 5 $0.00002 $0.00187
Haiku 4.5 $0.00001 $0.00093

Measured 2d ago against content hash d66fecbfdd72, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

evolve 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.

plugin/skills/evolve/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/evolve — Start Evolution

User provides: repo path, benchmark command, objectives (list of {name, direction} specs), and optionally max evaluations.

Step 1 — Deterministic setup

lobster (@openclaw/lobster) is bundled as a dependency and installed automatically with this package. If available, setup and teardown run as atomic lobster pipelines. If for some reason lobster is missing from $PATH, the same steps run as individual exec calls — no functionality is lost, lobster only adds atomicity and better error reporting.

With lobster

Run all pre-evolution setup as a single deterministic lobster workflow. This is atomic: if any step fails, the exact failure step is reported and nothing proceeds.

lobster action:run pipeline:"./plugin/workflows/evo-setup.lobster" args:{
  "repo": "<repo_path>",
  "benchmark": "<benchmark_cmd>",
  "objectives": "[{\"name\": \"score\", \"direction\": \"max\"}]"
}

The workflow handles:

  • Validate repo is clean (git status --porcelain)
  • Run baseline benchmark, capture seed fitness
  • git tag seed-baseline
  • Create memory/ directory structure
  • Initialize ~/clawd/canvas/ for dashboard

Parse the baseline fitness from run_baseline.stdout (last line — whitespace-separated numbers for "numbers" format, or JSON dict for "json" format).

Then call the MCP tools to record it:

  • evo_init with user's config (repo, benchmark, objectives, max_evals)
  • evo_report_seed with the baseline fitness values as list[float]

Without lobster

Fall back to running each step with individual exec calls (same operations, not atomic).

Step 2 — Code analysis (MapAgent)

Spawn MapAgent to identify optimization targets:

sessions_spawn agentId:map_agent

MapAgent reads the benchmark entry file, traces the call chain (using /oracle if available), and calls evo_register_targets.

Step 3 — Approval gate: confirm targets before committing budget

After MapAgent completes, present identified targets to the user and ask for confirmation before spending evaluation budget:

Read the full file on GitHub · 118 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 · 118 lines · 10 tokens per session scan A d66fecbfdd72

Subscribe to this mod's changes

evolve is a skill published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 934 once invoked, about $0.0001 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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