using-cap-evolve — the router
SkillProductivityFront 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the using-cap-evolve — the router skill
What this skill tells your AI
The instructions your AI receives, as published by skillberry-ai/cap-evolve in skills/orchestrate/using-cap-evolve/SKILL.md and read by ahel’s review.
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 (intakeowns 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.yamlsequences 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 runstops 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.
Signals
- GitHub stars
- 56
- Forks
- 16
- Last commit
- Sep 2026
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using-cap-evolve- Source
- github.com/skillberry-ai/cap-evolve