benchmark-optimization-loop
Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
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https://deepseekmodel.com/api/download.php?id=affaan-m-ecc-skills-benchmark-optimization-loop-skill-md&format=skill
下载 .skill
标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name benchmark-optimization-loop description Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests. license MIT metadata {"origin":"ECC"} tools Read, Write, Edit, Bash, Grep, Glob Benchmark Optimization Loop 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. Required Baseline Do not optimize until these exist: the operation being optimized; the correctness gate that must stay green; the metric: wall time, p95 latency, rows/sec, cost/run, memory, error rate; the current baseline; the search budget: max variants, max time, max spend, max data impact. If the user asks for an unrealistic target, keep the ambition but make the loop bounded and measurable. Loop Measure the baseline. Identify bottlenecks from evidence. Generate variants that test one hypothesis each. Run variants with the same input shape. Reject variants that fail correctness, safety, or reproducibility. Promote the fastest safe variant. Codify the winning path in a script, command, test, config, or doc. Rerun the baseline and winner to confirm the delta. Variant Table 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 Recursive Search For recursive or hyperparameter work: persist every run to a ledger; compare against the prior accepted winner, not only the previous run; keep a holdout or replay check; stop when improvement is within noise, correctness fails, cost exceeds the budget, or the search starts changing more variables than it can explain. Use phrases like "best measured safe variant" instead of "global optimum" unless the search space was actually exhaustive. Promotion Gate A variant cannot become the new default until: correctness tests pass; the performance delta is repeated or explained; rollback is obvious; the change is encoded in source control or a durable runbook; the final summary includes exact commands and measurements.
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |