Turn speedup requests into a measured optimization process with limits on time, cost, and variants. Compare each change against a baseline and correctness checks, then retain the fastest safe option with reproducible commands and measurements.
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Convert 'make it faster' requests into a bounded measured optimization loop — baseline first, generate one-hypothesis variants, benchmark each against a correctness gate, and promote the fastest safe variant with reproducible commands. Use when asked to speed something up, try many variants, run recursive optimization, benchmark latency/throughput/cost, or pick the best implementation by repeated measured tests.
安装前可以查看交给 AI 代理的指令内容。
Use this skill to convert "make it 20x faster" or "try 50 recursive optimizations" into a bounded measured loop that can actually improve a system.
Do not optimize until these exist:
If the user asks for an unrealistic target, keep the ambition but make the loop bounded and measurable.
Track variants like this:
Variant | Hypothesis | Command | Time | Correct? | Notes
baseline | current path | npm run job | 120s | yes | stable
batch-500 | fewer round trips | npm run job -- --batch 500 | 42s | yes | winner
parallel-8 | more workers | npm run job -- --workers 8 | 31s | no | rate limited
For recursive or hyperparameter work:
Use phrases like "best measured safe variant" instead of "global optimum" unless the search space was actually exhaustive.
A variant cannot become the new default until:
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Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.
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Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.
中文简介正在准备中。当前显示英语简介。
用实际开发任务比较多个编程智能体，记录成功率、可获取的API费用、耗时和重复运行的稳定性，为工具选型及更新后的性能评估提供数据。
Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.
中文简介正在准备中。当前显示英语简介。
Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.
中文简介正在准备中。当前显示英语简介。
在AI代理反复失败或重复操作时，记录错误与环境前提、排查原因，采取影响有限的恢复操作，并汇总结果和证据，为有依据的重试与交接提供支持。