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Get Started Free →Coordinate complex engineering work across bounded subagents while protecting the main context, preventing edit conflicts, and measuring whether delegation was worthwhile. Use when the user asks to delegate, parallelize, orchestrate, use subagents, fan out a review, or handle a large task with independent workstreams; do not use for small, tightly coupled, or inherently sequential tasks.
.claude/skills/hashgraph-online-orchestrate-engineering/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 5% | 0% |
AGENTS.md files and the task’s acceptance criteria.Subagents normally consume more total tokens than a comparable single-agent run. Optimize for fewer failed loops, cleaner context, better coverage, or lower elapsed time—not the highest agent count.
Read references/context-budget.md when selecting a topology, preparing worker context, defining summaries, or evaluating efficiency.
Give each worker:
textObjective: Bounded scope: Required context: Allowed actions and permissions: Required evidence: Output format: Stop condition:
Provide the minimum task-local context. Do not copy the entire main-thread history into every worker.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 18,805 | 16,078 | -15% | 1 | 1 | 0% | 2,221 | 2,572 | +16% | 0 | 0 | — |
case-05 | fail→pass | 28,035 | 21,522 | -23% | 1 | 1 | 0% | 3,755 | 4,160 | +11% | 0 | 0 | — |
case-01 | fail→pass | 47,785 | 32,416 | -32% | 1 | 1 | 0% | 8,215 | 5,357 | -35% | 0 | 0 | — |
case-02 | fail→pass | 30,404 | 17,803 | -41% | 1 | 1 | 0% | 5,364 | 3,962 | -26% | 0 | 0 | — |
case-03 | fail→pass | 37,843 | 25,027 | -34% | 1 | 1 | 0% | 5,553 | 3,455 | -38% | 0 | 0 | — |
case-06 | pass→pass | 12,155 | 7,199 | -41% | 1 | 1 | 0% | 1,595 | 1,809 | +13% | 0 | 0 | — |
case-07 | fail→pass | 20,385 | 14,359 | -30% | 1 | 1 | 0% | 2,531 | 2,652 | +5% | 0 | 0 | — |
case-08 | pass→pass | 13,616 | 11,138 | -18% | 1 | 1 | 0% | 1,350 | 1,508 | +12% | 0 | 0 | — |
case-09 | pass→pass | 17,687 | 14,776 | -16% | 1 | 1 | 0% | 2,361 | 2,375 | +1% | 0 | 0 | — |
case-10 | pass→pass | 10,516 | 4,706 | -55% | 1 | 1 | 0% | 1,344 | 1,375 | +2% | 0 | 0 | — |
case-11 | pass→pass | 14,608 | 13,351 | -9% | 1 | 1 | 0% | 2,174 | 1,828 | -16% | 0 | 0 | — |
case-12 | pass→pass | 13,689 | 6,530 | -52% | 1 | 1 | 0% | 1,739 | 1,705 | -2% | 0 | 0 | — |
case-13 | pass→pass | 13,887 | 12,787 | -8% | 1 | 1 | 0% | 2,081 | 1,960 | -6% | 0 | 0 | — |
case-14 | fail→fail | 7,271 | 4,165 | -43% | 1 | 1 | 0% | 1,275 | 1,394 | +9% | 0 | 0 | — |
case-15 | pass→pass | 16,133 | 6,701 | -58% | 1 | 1 | 0% | 1,979 | 1,949 | -2% | 0 | 0 | — |
case-16 | pass→pass | 13,989 | 10,446 | -25% | 1 | 1 | 0% | 1,426 | 1,706 | +20% | 0 | 0 | — |
case-17 | pass→pass | 13,853 | 8,558 | -38% | 1 | 1 | 0% | 2,122 | 2,144 | +1% | 0 | 0 | — |
case-18 | pass→pass | 16,158 | 11,917 | -26% | 1 | 1 | 0% | 1,730 | 1,854 | +7% | 0 | 0 | — |
case-19 | pass→pass | 11,991 | 12,387 | +3% | 1 | 1 | 0% | 1,873 | 1,955 | +4% | 0 | 0 | — |
case-20 | pass→fail | 16,027 | 10,442 | -35% | 1 | 1 | 0% | 3,210 | 980 | -69% | 0 | 0 | — |
case-21 | pass→fail | 22,493 | 10,017 | -55% | 1 | 1 | 0% | 2,557 | 958 | -63% | 0 | 0 | — |
case-22 | pass→pass | 10,269 | 9,847 | -4% | 1 | 1 | 0% | 1,707 | 2,357 | +38% | 0 | 0 | — |
case-23 | pass→pass | 5,979 | 6,420 | +7% | 1 | 1 | 0% | 1,152 | 1,835 | +59% | 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. 23 cases were attempted, and 21 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 +13 percentage points is the difference between those two pass rates over the 21 comparable cases. 2 cases got worse with the skill loaded, and they are 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.