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Get Started Free →Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
.claude/skills/dokhacgiakhoa-dispatching-parallel-agents/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-01 | ✓→✓ | = Same ✓ | -22% | 0% |
| case-04 | ✓→✓ | = Same ✓ | -8% | 0% |
When you have multiple unrelated failures (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.
Core principle: Dispatch one agent per independent problem domain. Let them work concurrently.
dotdigraph when_to_use { "Multiple failures?" [shape=diamond]; "Are they independent?" [shape=diamond]; "Single agent investigates all" [shape=box]; "One agent per problem domain" [shape=box]; "Can they work in parallel?" [shape=diamond]; "Sequential agents" [shape=box]; "Parallel dispatch" [shape=box]; "Multiple failures?" -> "Are they independent?" [label="yes"]; "Are they independent?" -> "Single agent investigates all" [label="no - related"]; "Are they independent?" -> "Can they work in parallel?" [label="yes"]; "Can they work in parallel?" -> "Parallel dispatch" [label="yes"]; "Can they work in parallel?" -> "Sequential agents" [label="no - shared state"]; }
Use when:
Don't use when:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 21,163 | 23,543 | +11% | 1 | 1 | 0% | 3,914 | 3,065 | -22% | 0 | 0 | — |
case-02 | fail→pass | 17,526 | 10,620 | -39% | 1 | 1 | 0% | 3,036 | 2,333 | -23% | 0 | 0 | — |
case-03 | fail→pass | 16,199 | 12,321 | -24% | 1 | 1 | 0% | 2,782 | 2,768 | -1% | 0 | 0 | — |
case-04 | pass→pass | 16,255 | 11,223 | -31% | 1 | 1 | 0% | 2,766 | 2,558 | -8% | 0 | 0 | — |
case-05 | pass→pass | 18,050 | 8,052 | -55% | 1 | 1 | 0% | 2,602 | 1,907 | -27% | 0 | 0 | — |
case-06 | pass→pass | 11,706 | 8,321 | -29% | 1 | 1 | 0% | 2,164 | 1,969 | -9% | 0 | 0 | — |
case-07 | pass→pass | 20,151 | 12,583 | -38% | 1 | 1 | 0% | 2,851 | 2,677 | -6% | 0 | 0 | — |
case-17 | pass→pass | 14,628 | 6,988 | -52% | 1 | 1 | 0% | 1,958 | 1,680 | -14% | 0 | 0 | — |
case-08 | pass→pass | 7,887 | 4,081 | -48% | 1 | 1 | 0% | 1,103 | 1,125 | +2% | 0 | 0 | — |
case-09 | pass→pass | 17,272 | 11,222 | -35% | 1 | 1 | 0% | 2,672 | 2,090 | -22% | 0 | 0 | — |
case-10 | pass→pass | 25,240 | 11,282 | -55% | 1 | 1 | 0% | 3,620 | 2,418 | -33% | 0 | 0 | — |
case-11 | pass→pass | 12,537 | 6,025 | -52% | 1 | 1 | 0% | 1,837 | 1,411 | -23% | 0 | 0 | — |
case-12 | pass→pass | 10,800 | 6,756 | -37% | 1 | 1 | 0% | 1,691 | 1,447 | -14% | 0 | 0 | — |
case-13 | pass→pass | 14,822 | 10,046 | -32% | 1 | 1 | 0% | 2,410 | 2,208 | -8% | 0 | 0 | — |
case-14 | fail→pass | 12,306 | 4,776 | -61% | 1 | 1 | 0% | 1,709 | 1,248 | -27% | 0 | 0 | — |
case-15 | pass→pass | 12,818 | 9,824 | -23% | 1 | 1 | 0% | 1,981 | 1,801 | -9% | 0 | 0 | — |
case-16 | pass→pass | 9,048 | 4,334 | -52% | 1 | 1 | 0% | 1,301 | 1,085 | -17% | 0 | 0 | — |
case-18 | pass→pass | 12,491 | 12,404 | -1% | 1 | 1 | 0% | 1,915 | 2,087 | +9% | 0 | 0 | — |
case-19 | fail→fail | 10,690 | 6,243 | -42% | 1 | 1 | 0% | 1,429 | 1,476 | +3% | 0 | 0 | — |
case-20 | pass→pass | 12,102 | 7,047 | -42% | 1 | 1 | 0% | 1,622 | 1,589 | -2% | 0 | 0 | — |
case-21 | pass→pass | 10,736 | 5,029 | -53% | 1 | 1 | 0% | 1,431 | 1,251 | -13% | 0 | 0 | — |
case-22 | pass→pass | 10,172 | 7,039 | -31% | 1 | 1 | 0% | 1,512 | 1,529 | +1% | 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. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.