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aio-optimize

Skill0 starsChanged 30 days agoMITView on GitHub

Diagnose and improve backend performance using representative profiles and correctness checks.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read algorithms and establish the metric, workload distribution, baseline environment and acceptance target.

    line 12
  2. Very specific:

    Keep, revert or narrow the optimization from measured results and explain tradeoffs.

    line 16

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

AIO Optimize

Diagnose and improve backend performance using representative profiles and correctness checks.

Protocol

  1. Read algorithms and establish the metric, workload distribution, baseline environment and acceptance target.
  2. Profile the actual hot boundary: CPU, memory, database plans/round trips, serialization, contention, queue latency or external waits. A guessed bottleneck is not a measurement.
  3. Make one evidence-backed change, preserving a correctness oracle and relevant failure tests. Bound concurrency/cache/connection changes and inspect cancellation, invalidation and overload effects.
  4. Compare repeated baseline/candidate runs under the same workload and environment. Include p50/p95 or tails, throughput/error rate and resource use where those are the acceptance dimensions.
  5. Keep, revert or narrow the optimization from measured results and explain tradeoffs. Stop when the target is met; do not sacrifice tenant/security/correctness checks for a benchmark win.

Return and handoff

A reproducible baseline/candidate report and verified performance change with measured scope.

In a pipeline, use the stage contract and return to the integrating controller. Run only this specialist's owned scope; preserve the user's accepted decisions and existing authorization.

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