optimize-op

name

optimize-op

description

Drive a single-operator optimization campaign per .agents/guidance/OPTIMIZATION_GUIDELINES.md, with a deterministically enforced definition-of-done and versioned MR summary. Use when asked to optimize an operator, run or finish a performance campaign, generate or refresh its MR performance summary, or verify that a perf MR satisfies correctness, benchmark, baseline, lead-exhaustion, memory, and API/ABI gates.

Optimize Op

Read .agents/guidance/OPTIMIZATION_GUIDELINES.md and follow it as the source of truth. Use tools/optimize_op.py for the deterministic gates and the versioned MR summary. Do not duplicate or weaken the guidance here.

Workflow

1. Run python3 tools/optimize_op.py <Op> --phase preflight. Resolve every readiness GAP in a separate non-perf: coverage commit before optimizing.

2. Survey and baseline the full union expanded from all declared basic and advanced configurations. Iterate profile → lead → one change → correctness/performance evaluation → review/refactor. Run python3 tools/refactor_op.py <Op> --phase assess before every kept perf: commit. Re-profile after each win and stop only at the guidance's measured ridge or three-post-win-strike condition.

3. Initialize the concise v1 MR summary from exact optimized case keys:

   python3 tools/optimize_op.py <Op> --phase summary \
     --benchmark-base <ref> --optimized-cases-file <file> \
     --state provisional --bottleneck Memory-bound \
     --profile-evidence '<measured evidence>' --out <description>
   

   Use Compute-bound when supported by the profile. Keep the human-authored assessment, six hard-evidence checklist lines, and one to five learnings current. Regenerate scope, categories, per-SKU Impact, and Layout comparison; do not hand-edit those derived fields. Maintain the canonical companion ## Memory footprint section outside the bounded v1 block; check its summary item only after ODO-9 passes.

4. Refresh the description with reference-SKU evidence and finalize it:

   python3 tools/optimize_op.py <Op> --phase summary \
     --results <description> --state final --out <description>
   

   Require every configured reference SKU. Keep the visible warning for any non-reference local SKU retained for context.

5. Run the final gate:

   python3 tools/optimize_op.py <Op> --phase evidence \
     --base <ref> --results <description>
   

   Require pixelwise-equality evidence for every changed configuration, updated and non-regressing comparable baselines, full-surface profile/lead coverage (agent-verified per the guidance; the gate does not check it), hard evidence for all six checklist items, exhausted leads, stable API/ABI, and perf: commit hygiene. Treat ODO-9 as the memory-footprint authority; its MANUAL result requires explicit human review before submission. Resolve every GAP and unresolved MANUAL, then rerun. Never weaken or silently retune tests, fabricate baselines, check an evidence item without its hard proof, weaken the ODO-9 memory policy, or change public API/ABI.