# Eval Evolution > Prepare, run, review, and explicitly promote bounded Skill or existing-MCP Eval Evolution campaigns through the canonical eval facade. - Skill: `miaodx/eval-evolution` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add miaodx/eval-evolution` - Raw SKILL.md: https://api.skillmd.com/api/skills/miaodx/eval-evolution/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: MiaoDX (https://skillmd.com/u/miaodx) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/miaodx/eval-evolution --- # Eval Evolution Use this Skill for maintainer-owned optimization campaigns driven by frozen eval evidence. `roboclaws.evals` remains the control plane. The optimizer and robot trials are distinct OpenAI Agents SDK agents; a provider profile selects their models and never changes the agent engine. ## Prepare Freeze an `eval_evolution_campaign_v1` JSON manifest before the first optimizer call. Bind one target kind and target, the baseline commit and target digest, mutable paths, optimizer and robot provider/model identities, paired training suites, the orchestrator-only sealed confirmation reference, quality and minimum-improvement policy, runtime identity, and explicit turn/trial/token/ cost/time/retry ceilings. The budget block also declares positive `optimizer_call_tokens`, `optimizer_call_cost_usd`, `robot_attempt_tokens`, and `robot_attempt_cost_usd` reservations. Each reservation is a frozen maximum for one optimizer run or one robot attempt, not a post-hoc usage target. Skill campaigns target exactly one `skills//SKILL.md`. Keep `static-full` as the baseline. `no-skill` is a non-promotable negative control. Do not mix Skill and MCP changes. ## Run Inspect the blocked preflight without provider execution: ```bash just agent::eval evolve campaign= ``` Run the frozen campaign only after provider/runtime readiness is established: ```bash just agent::eval evolve campaign= live_execution=run ``` The optimizer can only read the declared target, read sanitized feedback, and submit one hypothesis plus patch. It has no shell, filesystem, git, network, eval-launch, commit, or publication tool. Host-owned validation creates one content-addressed full baseline snapshot and runs deterministic gates before paired robot trials. ## Review Reject missing or mismatched identity, incomplete paired evidence, privacy or checker failures, trajectory/terminal violations, quality regressions, neutral rewrites, and `no-skill`. Efficiency ranks only candidates that meet the frozen quality and minimum-improvement rule. At most one training winner reaches one sealed confirmation. Its evidence never returns to the optimizer. The orchestrator reserves the full optimizer-run or robot-attempt token and cost maximum before provider execution and passes that limit into the Agents SDK model settings. Insufficient remaining campaign capacity is `inconclusive` without starting the provider. A model without catalog output pricing is also blocked before execution because its cost ceiling cannot be derived safely. Behavior/provider failures do not retry. A separately recorded classified infrastructure attempt is allowed only when the frozen campaign permits one retry. ## Promote Promotion requires an accepted selection report and a digest-bound `eval_evolution_promotion_manifest_v1` with `maintainer_approved=true`: ```bash just agent::eval evolve-promote \ report= \ manifest= \ live_execution=run ``` Stop before creating that approval unless a maintainer has reviewed the exact candidate, paired training, sealed confirmation, limitations, and digests. Promotion applies only the reviewed patch. It does not commit, change defaults, or publish a baseline/catalog artifact. MCP description candidates additionally require the Phase 2 structural text-only proof. Never run MCP behavior candidates live until the Phase 3 malicious isolation proof has passed on the selected placement.