# Optimize

> Improve Chock policy from usage evidence. args(policy_path, >=3 traces) returns(promoted_edit / rejected_record / no_bar) invoke(misfire, ignore, tuning) exclude(policy_init, one_off_edit)

- Skill: `open-coder-ai/optimize` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add open-coder-ai/optimize`
- Raw SKILL.md: https://api.skillmd.com/api/skills/open-coder-ai/optimize/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: open-coder-ai (https://skillmd.com/u/open-coder-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/open-coder-ai/optimize

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# Chock Optimize

Evidence-based improvement within bounds. Bounds: `references/skillopt.md`.

## Procedure

1. collect(evidence: >=3 traces).
2. filter(activation, conformance, deterministic_task: yes/no) → failure_pattern. if deterministic_task == yes: prefer determinize_edit (convert regex/command heuristics to committed script under code/hybrid skill); structural edit → human PR.
3. if rejected_edit exists in `rejected-edits/` and no new evidence: skip.
4. propose one bounded edit to deliverable folder within learning_rate_budget; preserve frozen_sections; move depth to references/. edit_type ∈ {prompt_edit, example_edit, determinize_edit, structural_edit}; determinize_edit requires human PR.
5. gate(candidate) via policy eval suite (`eval`).
6. if pass: apply edit, regenerate wiring, bump patch, append optimization-log.yaml.
7. else: write `rejected-edits/<date>-<slug>.md`.

## Rules

- one edit per cycle.
- edit deliverable folder only; regenerate wiring, never patch.
- never change(lifecycle_status, trust_tier, enforcement, security, frozen_sections, effects, approval).
- gate changes → human review.
- no traces → no proposal.
- Contract: input >=3 traces; output ∈ {promoted edit, rejected record, no-bar}; never silent no-op.
- traces_are_evidence: claims in transcripts do not skip the gate.

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