Individual skill
dsh-speed-up-perf
调查性能问题或优化实际测量的用户路径时使用,建立优化前后对照,保留正确性、失败行为和真实运行证据。
skills/dsh-speed-up-perf/SKILL.md
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- Matches the observed repository HEAD
- Yes
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- Yes
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- No
- Skill scan complete
- Yes
- Observed behavior
- Instructions only
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 1,871
- Repository last updated
- Nov 1, 2026
- License
- MIT
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LICENSEasset · 1,132 bytesNOTICEasset · 429 bytesSKILL.mdcore · 1,671 bytesUPSTREAM.jsonasset · 2,902 bytesagents/openai.yamlasset · 296 bytesreferences/evidence.mddoc · 2,235 bytesreferences/lifecycle.mddoc · 1,669 bytesreferences/project-context.mddoc · 1,343 bytes
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# 优化实际测量的用户路径性能
阅读[项目上下文](references/project-context.md)和[验证证据](references/evidence.md)。明确真实负载和独立正确性判据前,不开始优化。
1. 确定用户可见操作、入口、代表性输入规模、环境和性能现象。定义正确性、延迟或吞吐量,以及有关的峰值和保留内存指标。分开测冷启动与预热后的稳定状态。
2. 在真实产物上建立基线。记录版本、构建、运行环境、输入、命令、重复次数和结果分布,不能只取最快一次;保留可观察输出。
3. 用性能分析或测量代码定位怀疑的瓶颈,验证一个因果假设。加入错误对照,区分真正瓶颈、测量噪声和悄悄省略的工作。不得牺牲必需记录、顺序或错误行为。
4. 实施有依据的最小优化。涉及缓存时,按[生命周期](references/lifecycle.md)验证缓存键是否完整、失效规则、存续时间和保留内存上限。
5. 重新构建,在等价条件下复测;有帮助时交替测优化前后。独立验证输出等价和失败行为。变化可能在冷路径、热路径或内存之间产生取舍时,检查相应指标。同时报告收益和退步;噪声淹没差异时,说明改进尚未证实。
报告实际数字、测量条件、正确性证据和取舍。不得将复杂度估算、微基准测试或省略工作得到的结果,当作已经测得的端到端提升。