optimizelisted

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)
open-coder-ai/context-report · ★ 0 · AI & Automation · score 73

Install: claude install-skill open-coder-ai/context-report

# 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.