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Get Started Free →当用户希望通过并行工作、并发 agents、批量工具调用、隔离 worktree 或多条独立验证通道来大幅加速任务、同时不损失正确性时使用。
.claude/skills/affaan-m-parallel-execution-optimizer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -46% | 0% |
当速度来自同时处理相互独立的工作时，使用此技能： 仓库巡检、文件读取、API 检查、浏览器检查、构建/测试通道、 部署回读，或多 worktree 的实现批次。
行动之前，先把紧迫感转化为依赖图。
在大规模推进之前，写一张紧凑的矩阵：
textLane | Can run in parallel? | Write surface | Risk | Verification Repo scan | yes | none | low | rg/git status outputs Backend patch | maybe | src/api | medium | unit tests Frontend patch | maybe | app/components | medium | browser screenshot Deploy readback | after build | remote service | high | live URL + logs
只有当各通道的写入面互不冲突时，才并行运行。
然后有节奏地主动轮询。
并更新矩阵。
或影响线上客户的部署。
汇报时使用：
textParallel execution result: - Lanes run: 5 - Lanes completed: 4 - Blocked lane: deploy readback, waiting on DNS propagation - Fast path found: batched repo scan + focused tests - Verification: lint pass, unit pass, live smoke pass
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 5,248 | 2,920 | -44% | 1 | 1 | 0% | 321 | 928 | +189% | 0 | 0 | — |
case-02 | fail→fail | 17,719 | 17,956 | +1% | 1 | 1 | 0% | 2,203 | 883 | -60% | 0 | 0 | — |
case-03 | fail→fail | 25,799 | 11,454 | -56% | 1 | 1 | 0% | 4,848 | 2,703 | -44% | 0 | 0 | — |
case-04 | pass→pass | 11,196 | 9,445 | -16% | 1 | 1 | 0% | 1,763 | 2,062 | +17% | 0 | 0 | — |
case-05 | pass→pass | 12,980 | 10,148 | -22% | 1 | 1 | 0% | 2,212 | 2,166 | -2% | 0 | 0 | — |
case-06 | pass→pass | 13,681 | 12,252 | -10% | 1 | 1 | 0% | 1,978 | 2,368 | +20% | 0 | 0 | — |
case-07 | fail→fail | 14,746 | 7,141 | -52% | 1 | 1 | 0% | 2,351 | 1,808 | -23% | 0 | 0 | — |
case-08 | pass→pass | 14,514 | 13,084 | -10% | 1 | 1 | 0% | 2,249 | 2,655 | +18% | 0 | 0 | — |
case-09 | pass→pass | 10,722 | 6,809 | -36% | 1 | 1 | 0% | 1,559 | 1,558 | -0% | 0 | 0 | — |
case-10 | fail→pass | 8,997 | 5,906 | -34% | 1 | 1 | 0% | 1,412 | 1,515 | +7% | 0 | 0 | — |
case-11 | pass→pass | 9,666 | 4,339 | -55% | 1 | 1 | 0% | 1,412 | 1,261 | -11% | 0 | 0 | — |
case-12 | pass→pass | 14,504 | 7,852 | -46% | 1 | 1 | 0% | 2,194 | 1,775 | -19% | 0 | 0 | — |
case-13 | fail→fail | 15,528 | 9,494 | -39% | 1 | 1 | 0% | 2,413 | 2,066 | -14% | 0 | 0 | — |
case-14 | fail→pass | 9,398 | 3,355 | -64% | 1 | 1 | 0% | 1,530 | 1,080 | -29% | 0 | 0 | — |
case-15 | pass→pass | 13,165 | 7,500 | -43% | 1 | 1 | 0% | 1,964 | 1,675 | -15% | 0 | 0 | — |
case-16 | pass→pass | 4,538 | 5,621 | +24% | 1 | 1 | 0% | 684 | 1,445 | +111% | 0 | 0 | — |
case-17 | fail→pass | 13,282 | 7,207 | -46% | 1 | 1 | 0% | 2,185 | 1,352 | -38% | 0 | 0 | — |
case-18 | pass→pass | 12,636 | 6,491 | -49% | 1 | 1 | 0% | 1,899 | 1,534 | -19% | 0 | 0 | — |
case-19 | fail→pass | 8,058 | 5,027 | -38% | 1 | 1 | 0% | 1,150 | 1,338 | +16% | 0 | 0 | — |
case-20 | fail→pass | 15,091 | 4,123 | -73% | 1 | 1 | 0% | 2,323 | 1,253 | -46% | 0 | 0 | — |
case-21 | fail→pass | 13,735 | 11,123 | -19% | 1 | 1 | 0% | 2,033 | 2,291 | +13% | 0 | 0 | — |
case-22 | pass→pass | 11,112 | 7,753 | -30% | 1 | 1 | 0% | 1,724 | 1,693 | -2% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +27 percentage points is the difference between those two pass rates over the 20 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.