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Using Cap Evolve

skill-skillberry-ai-cap-evolve-using-cap-evolve · by skillberry-ai

Entry-point router for cap-evolve. Use the moment a user asks to OPTIMIZE an agent capability against an eval — "optimize <X>", "make <X> score higher on <benchmark>", "improve this skill/tool/prompt". Decides whether a project already exists and routes accordingly: to intake (Phase 1) for a fresh request, or straight into the phase chain / `cap-evolve run` when `.capevolve/project/` is already s…

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Install

$ agentstack add skill-skillberry-ai-cap-evolve-using-cap-evolve

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

using-cap-evolve — the router

This is the front door. When someone says "optimize \", you land here first. The router does not run any phase — it figures out where the user is and sends them to the right next step, then gets out of the way.

Routing decision

Run the resolver to see the current state and the recommended next command:

python scripts/run.py "$ARGUMENTS" --base .capevolve

It prints {state, next, sequence, ...}:

  • state fresh (no .capevolve/project/): route to /cap-evolve:intake

it interviews the user, scaffolds the project, and gathers inputs. Never skip intake on a fresh request; never fabricate a NEEDED input.

  • state scaffolded (capevolve.yaml exists, check not yet green): route to

/cap-evolve:implement-and-check and stop at the hard gate (cap-evolve check must print {"ok": true}).

  • state ready (check green): offer the two run modes below.
  • state running/finalized: route to /cap-evolve:report for status /

the sealed-test result.

Two ways to run (both honest, same engine)

  1. Standalone phase chain — drive it turn by turn, inspecting each step:

/cap-evolve:intake/cap-evolve:implement-and-check/cap-evolve:baseline/cap-evolve: (e.g. hill-climb, gepa, skillopt) → /cap-evolve:finalize/cap-evolve:report. Use when you want to review each phase, or to run just one phase.

  1. Fully automatic/cap-evolve:orchestrate --execute, or directly:

``bash cap-evolve run --spec .capevolve/project/capevolve.yaml `` Sequences every phase, enforces the hard gate before spending budget, decides when to stop (budget/stall), and ends with the sealed-test number.

Host-agnostic fallback (no plugin / non-Claude host): point the agent at RUN.md and follow it step by step. Same rules, no Claude-only features needed.

The non-negotiable rules (restated; enforced by core + hooks)

  • Split train/val/test once, seeded; test is scored only at finalize, once.
  • Accept on val by significance, never on the data the optimizer edited.
  • Do not edit splits.json, rollouts/test/*, or *test*gold* files —

the plugin's PreToolUse hook blocks it; the seal lives in core.

  • An iteration may not "finish" while cap-evolve check is red (Stop hook).

References

  • references/routing.md — the state machine, the session-start injection JSON

shape, and how this router relates to orchestrate.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.