How do I install the Using Cap Evolve skill?

Run npx skillmds@latest add skillberry-ai/using-cap-evolve in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Using Cap Evolve safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Using Cap Evolve?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Using Cap Evolve free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Using Cap Evolve?

skillberry-ai (@skillberry-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Using Cap Evolve by skillberry-ai — Agent Skill

Name: Using Cap Evolve
Author: skillberry-ai

Using Cap Evolve

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.

skillberry-ai e403d58 6 files · 17.8 KB Updated Sep 17, 2026

File contents

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

Phase chain — /cap-evolve:<phase> turn by turn, so each step is inspected.
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.
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.

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

---name: using-cap-evolvedescription: 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.---# using-cap-evolve — the routerThe front door: it works out *where the user is* and hands off, running no phase andediting nothing. Boundary: this router picks the door, `orchestrate` drives the run.## Routing decisionRun from the user's project dir; `S` is the absolute path of the directory you loaded thisSKILL.md from — the one location always known here (no env var is set for a plugin install):```bashS=<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 detail1. **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.No plugin, or a non-Claude host: follow `RUN.md` step by step. Same engine, same rules.

Frequently asked questions