agentleFS
Sign inStart free
skillberry-aiskillberry-ai/cap-evolve/skills/orchestrate/using-cap-evolve/SKILL.md

using-cap-evolve

Skill69 starsChanged 5 months agoApache-2.0View on GitHub

Front door for cap-evolve: routes an optimization request to the right pipeline phase. Use when someone wants an agent, skill, system prompt, tool surface, or MCP toolset to score higher on an eval, benchmark, or task suite — "optimize my skill", "raise the pass rate on these tasks", "my agent keeps failing these cases", "get this prompt''s accuracy up on my evals" — even when they never say "optimize", and whenever a .capevolve/ project or an unfinished run is in the tree. Routes to intake, the check gate, a resumed run, or the report; optimizes nothing itself. Not for making code or a query faster, and not for rewording one prompt with no eval to score it against. When the user names a phase or algorithm outright (baseline, gate, hill-climb, gepa), use that skill directly.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run from the user's project dir.

    line 18
  2. Very specific:

    Follow next; pass reason on to the user.

    line 23
  3. Very specific:

    No plugin, or a non-Claude host: follow RUN.md step by step.

    line 39

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

using-cap-evolve — the router

The front door: it works out where the user is and hands off, running no phase and editing nothing. Boundary: this router picks the door, orchestrate drives the run.

Routing decision

Run from the user's project dir; S is the absolute path of the directory you loaded this SKILL.md from — the one location always known here (no env var is set for a plugin install):

S=<this skill's own directory>; python "$S/scripts/run.py" --base .capevolve

Follow next; pass reason on to the user. Two things the JSON cannot say for itself:

  • On a fresh request go through intake, and if an input it needs is missing, ask the user for it rather than inventing one (intake owns that rule).
  • An existing run is never restarted from zero: interrupted → cap-evolve run --resume; sealed and the user wants another attempt → cap-evolve run --reuse-baseline <run dir>.

Three ways to run — orchestrate has the detail

  1. Phase chain — /cap-evolve:<phase> turn by turn, so each step is inspected.
  2. Deterministic — cap-evolve run --spec .capevolve/project/capevolve.yaml sequences the check gate → baseline → algorithm → finalize → report. It presumes intake already happened; it does not run intake.
  3. Agent handoff — with orchestration_mode: agent, cap-evolve run stops after baseline and hands the loop back to you; no sealed-test number until you finalize. For an unattended run instead, pass cap-evolve run --agent-driver <name> to drive the loop via skills/algorithms/agent-optimize/scripts/host.py.

No plugin, or a non-Claude host: follow RUN.md step by step. Same engine, same rules.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free