跳到正文
cccskills
免费在 GitHub 公开

optimize-accuracy-first

Investigate performance bottlenecks and validate optimizations without weakening retrieval quality or correctness.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(3)

  • SKILL.md2.1 KB
  • agents/openai.yaml244 B
  • references/measurement-matrix.md7.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Optimize Accuracy First

Use this skill for a performance investigation or an optimization whose benefit needs measurement. Ordinary bug fixes do not need a performance study.

Locate the owner of the work and state the expected improvement before changing it. Preserve the applicable contracts: deterministic results, exact token budgets, Unicode matching, truthful coverage, bounded work, atomic generations, and snapshot consistency. An intentional contract change should be described and tested as such.

Choose evidence that answers the question. Behavioral regressions and differential checks establish correctness; candidate counts can explain work; representative release measurements support latency, CPU, memory, or storage claims. A smaller local timer does not justify worse evidence or more retries. Do not invent agent-success or provider-cost results from retrieval proxies.

Prefer the smallest ownership change supported by the evidence. Remove repeated work or reuse an existing indexed primitive before adding caching, concurrency, or a new abstraction. Keep a simpler design when a proposed optimization has no demonstrated benefit.

Measurement references

Consult only the material relevant to the investigation:

Report the observed result and its limits. Run checks appropriate to the change; broaden them when a failure, risk, or unresolved question warrants it. Missing optional research data limits performance claims, not permission to finish a validated correctness fix.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

报告此技能的问题