本文へ移動
cccskills
無料GitHub で公開

performance-tradeoff

Judge performance or resource claims on real hardware: benchmark design or review, latency, throughput, memory or headroom numbers, and accept-or-reject decisions on optimizations. Not for refactors with no resource claim.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md1.9 KB
  • agents/openai.yaml205 B

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Performance tradeoff

Before trusting or reporting a number, check:

  • Equivalent arms. Only the intended change differs: build, inputs, load, power/thermal state, output quality. Alternate arm order; a cache warmed by one arm contaminates the next.
  • Separate regimes. Cold, warm and sustained are different results. Claim a cold-start win only from fresh-cache runs of both arms.
  • Noise before change. Compare the delta with each arm's run-to-run spread; overlapping ranges or few samples mean inconclusive. Give latency percentiles with sample counts.
  • Completion, not enqueue. Time async/GPU work to completion, with identical sync and transfer boundaries in both arms.
  • Raw-value arithmetic. Relative change = (candidate − baseline) / baseline, direction stated; recheck supplied percentages. No speedup against a timeout or OOM.
  • One memory scope. Don't sum overlapping counters (a unified-memory footprint may already include GPU allocations). Headroom is peak versus what the target actually has free; note the sampling interval.
  • Phase is not end to end. A faster component isn't a faster request until measured; overlapping work doesn't add serially.

Decide in order: correctness and quality; whether the change enables a supported workload the baseline fails or nearly fails; absolute user-visible cost; maintenance cost. A resource reduction that enables nothing measured doesn't justify a latency, quality or complexity cost. Set thresholds and margins before seeing candidate results.

Answer accept, reject or unproven per hardware and workload case, naming the smallest missing measurement. Report only the metrics that decide it.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when creating, revising, or critiquing a chart, dashboard, or KPI display that makes a quantitative comparison someone will act on. Covers honest comparison and rendered checks; leave palette, marks, and library mechanics to the medium's own skill. Skip decorative graphics and diagrams with no data.

日本語の概要は準備中です。原文の説明を表示しています。

macintog/codex-spine92026年10月9日 更新

Audit a codebase or subsystem for architecture simplification - shallow or pass-through modules, scattered concepts, naming drift, sibling workflows that disagree, weak test seams. Use when the user asks what to simplify or restructure. Not for a narrow fix or for implementing an already chosen design.

日本語の概要は準備中です。原文の説明を表示しています。

macintog/codex-spine92026年10月9日 更新

Use when the deliverable is authored or edited prose whose wording matters (docs, READMEs, release notes, public copy) or when the user asks to tighten, de-AI, or review writing. Not for chat answers, code review, or explanations of current work.

日本語の概要は準備中です。原文の説明を表示しています。

macintog/codex-spine92026年10月9日 更新

Audit an existing agent skill or instruction file for lines that do not change behavior, routing overlap, and portability, or vet a third-party skill before installing it. To create or edit a skill, use the platform's skill-creator instead. Not for running a skill's own task.

日本語の概要は準備中です。原文の説明を表示しています。

macintog/codex-spine92026年10月9日 更新

macintog のスキルをすべて見る

このスキルの問題を報告する