git:20260821.1a05a80 to git:20260821.d979319

---

name: optimize

---

# Optimize

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

## Input Schema

```json

{

"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

Per `../../shared/helper-cli-contract.md`, run `python PLUGIN_ROOT/shared/create_run.py --skill optimize` once; stdout is literal `<run-directory>`; never store it in a shell variable.

### 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:

### 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, read and apply `../../shared/specialist-orchestration.md`; otherwise do not load it. 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, select one task-neutral route and probe codemap-py once before the first iteration: `python PLUGIN_ROOT/shared/codemap_adapter.py context --category implementation --query-kind <kind> [--target <qname>] --out <run-directory>/codemap-context.json`. Use `skip` for an exact localized optimization with no unresolved structural fact, the matching single route (`central`, `callers`, `blast`, `dependencies`, `test-impact`, or `coupling`) for one unresolved fact, and `standard` for broad or unknown scope. Map direct, all, or production caller questions to `callers`; use `blast` only for explicitly transitive caller questions. An explicit user or tool request for structural evidence overrides `skip`. 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

### 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`:

```json

{

"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/quality-gates.md`.

Minimum artifact payload template: `result-template.json`.