Optimize

Optimize

Metric-driven optimization with explicit guards, rollback criteria, experiment log.

Input Schema

{
  "goal": "required measurable improvement objective",
  "mode": "single|campaign",
  "metric_cmd": "required command that emits or validates the target metric",
  "metric_direction": "higher|lower",
  "guard_cmd": "required command that must continue to pass",
  "max_iterations": "optional integer, default 1",
  "min_delta": "optional practical significance threshold",
  "scope_files": [
    "paths the optimization may edit"
  ],
  "done_when": "metric improves without guard regression"
}

Workflow

01: Create run directory

Run python PLUGIN_ROOT/shared/create_run.py --skill optimize once. Retain its single printed path as <run-directory> and substitute that literal path into every later artifact path and helper argument. Never store or reuse the path through a shell variable; shell variables do not persist across tool calls.

02: Validate metric and guard commands

Require:

• Repeatable metric_cmd producing comparable value or pass/fail.
• Known metric_direction.
• guard_cmd fails on unacceptable regressions.
• Bounded scope_files.
• Explicit, bounded max_iterations for campaign.
• Protect files/scripts used by metric_cmd/guard_cmd unless user explicitly scopes them and accepts measurement-integrity risk.

Dry-run both before edit:

Execute the configured metric_cmd and guard_cmd separately with the host-native command runner. Write complete combined output to <run-directory>/metric-baseline.txt and <run-directory>/guard-baseline.txt; retain both exit codes and stop before editing if either command cannot run.

03: Record baseline and hypothesis

Write <run-directory>/hypothesis.md:

• metric to improve
• expected mechanism
• files allowed to change
• guard risk
• rollback condition

For campaign, noisy metrics, GPU/ML performance, or correctness-sensitive code, apply ../../shared/specialist-orchestration.md. Write <run-directory>/specialist-optimization-plan.md with narrow context packs for:

• squeezer: profiling mechanism, bottleneck hypothesis, measurement plan.
• qa-specialist: guard coverage and regression risk.
• data-steward: data pipeline or reproducibility impact.
• scientist: metric validity, ablation design, statistical noise.
• challenger: overfitting to the metric or weakening guard checks.

No fan-out for one small measured change with stable metric/guard. Never let specialist change metric/guard scripts unless explicitly in scope_files and measurement-integrity risk recorded.

Structural context (optional): when scope_files resolves to a Python module/symbol, also probe codemap-py once for callers, coupling, and test impact before the first iteration: python PLUGIN_ROOT/shared/codemap_adapter.py context --category develop --target <qname> --out <run-directory>/codemap-context.json. Per ../../shared/codemap-contract.md, absence/incompatibility is non-fatal — continue with the hypothesis above. Persist the result once here, before step 04 applies any change; any triggered specialist consumes <run-directory>/codemap-context.json, never a fresh query.

Initialize machine-readable iteration log:

Create an empty <run-directory>/experiments.jsonl with the filesystem tool before the first iteration.

04: Apply one minimal optimization change per iteration

One independent hypothesis per iteration. Do not optimize unmeasured paths. Before each, write <run-directory>/iteration-<n>-before.patch with scoped-file diff. If iteration fails and only its patch is present, revert with git apply -R against iteration diff; otherwise fail run when clean reversal cannot be proven. Never use git reset --hard.

05: Re-measure

Re-run the same retained metric_cmd and guard_cmd separately with the host-native command runner. Write complete combined output to <run-directory>/metric-after.txt and <run-directory>/guard-after.txt; retain both exit codes.

06: Compare baseline and after results in <run-directory>/comparison.md

Required fields:

• baseline value
• after value
• delta
• guard status
• confidence
• noise caveats

Append one JSON object/iteration to <run-directory>/experiments.jsonl:

{
  "iteration": 1,
  "hypothesis": "one-line mechanism",
  "metric_before": 0.0,
  "metric_after": 0.0,
  "delta": 0.0,
  "guard": "pass|fail",
  "decision": "kept|reverted|inconclusive|failed",
  "rollback_evidence": "path or reason"
}

07: Decide keep/revert

• Keep only with intended metric movement and passing guards.
• With min_delta, keep only if delta meets/exceeds practical-significance threshold.
• Revert or fail on guard regression.
• For noisy measurement, repeat or mark inconclusive.
• In campaign, stop at first kept result unless user asked continued exploration; otherwise continue only while max_iterations remains and each rejected iteration has rollback evidence.

08: Run shared quality gates

Inspect python PLUGIN_ROOT/shared/run_gates.py --help. Tests runs configured test or guard command; give real commands or explicit reasons for other gates.

09: Write and validate the mandatory result artifact

Follow ../../shared/helper-cli-contract.md and authoritative help. Write OPTIMIZE_METADATA, validate as optimize, promote only validated candidate.

Fail-Fast Rules

1. Missing metric or guard command => fail.
2. Baseline cannot be captured => fail.
3. Scope is unbounded => fail.
4. Guard regression after change => fail unless reverted.
5. Metric/guard script changed without explicit scope and measurement-integrity note => fail.
6. Campaign iteration rejected without rollback evidence or unresolved-risk note => fail.
7. Claimed improvement below min_delta without explicit inconclusive status => fail.
8. Result artifact validator failure => fail.
9. Result artifact missing => fail.

Quality Gates

Required:

• tests: guard command or impacted tests.
• review: metric comparison, rollback decision, relevant campaign ledger, git diff --check.
• artifact: shared validator confirms comparison, experiments JSONL, gate logs, result JSON shape.

Recommended:

• lint, format, types: run for any code edits.

Calibration Hooks

On metric/guard-policy change, update calibration:

• behavioral cases: baseline missing, guard regression, noisy metric overclaim, campaign rollback evidence, below-threshold improvement, artifact validator bypass
• benchmark patterns: optimize

Output Contract

Use shared gate schema from ../../shared/quality-gates.md.

Minimum artifact payload template: result-template.json.