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Get Started Free →Agent Orchestration Rules
.claude/skills/agent-orchestration/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 15 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 327% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -83% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 885% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 571% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 743% | 0% |
When the user asks to implement something, use implementation agents to preserve main context.
Wrong - burns context:
Main: Read files → Understand → Make edits → Report
(2000+ tokens consumed in main context)Right - preserves context:
Main: Spawn agent("implement X per plan")
↓
Agent: Reads files → Understands → Edits → Tests
↓
Main: Gets summary (~200 tokens)| Task Type | Use Agent? | Reason | |-----------|------------|--------| | Multi-file implementation | Yes | Agent handles complexity internally | | Following a plan phase | Yes | Agent reads plan, implements | | New feature with tests | Yes | Agent can run tests | | Single-line fix | No | Faster to do directly | | Quick config change | No | Overhead not worth it |
Agents read their own context. Don't read files in main chat just to understand what to pass to an agent - give them the task and they figure it out.
Implement Phase 4: Outcome Marking Hook from the Artifact Index plan.
**Plan location:** thoughts/shared/plans/2025-12-24-artifact-index.md (search for "Phase 4")
**What to create:**
1. TypeScript hook
2. Shell wrapper
3. Python script
4. Register in settings.json
When done, provide a summary of files created and any issues.When user says these, consider using an agent:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 4,048 | 9,549 | +136% | 1 | 1 | 0% | 270 | 1,152 | +327% | 0 | 0 | — |
case-02 | fail→fail | 2,866 | 9,587 | +235% | 1 | 1 | 0% | 197 | 921 | +368% | 0 | 0 | — |
case-03 | fail→pass | 25,802 | 4,682 | -82% | 1 | 1 | 0% | 6,181 | 1,055 | -83% | 0 | 0 | — |
case-04 | fail→pass | 4,401 | 9,041 | +105% | 1 | 1 | 0% | 175 | 1,724 | +885% | 0 | 0 | — |
case-05 | fail→fail | 23,915 | 7,558 | -68% | 1 | 1 | 0% | 5,606 | 556 | -90% | 0 | 0 | — |
case-06 | pass→pass | 2,979 | 2,138 | -28% | 1 | 1 | 0% | 523 | 772 | +48% | 0 | 0 | — |
case-07 | pass→fail | 3,471 | 4,851 | +40% | 1 | 1 | 0% | 644 | 630 | -2% | 0 | 0 | — |
case-08 | pass→fail | 3,433 | 3,898 | +14% | 1 | 1 | 0% | 580 | 567 | -2% | 0 | 0 | — |
case-14 | fail→fail | 16,322 | 20,080 | +23% | 1 | 1 | 0% | 3,819 | 5,471 | +43% | 0 | 0 | — |
case-09 | fail→pass | 5,327 | 13,573 | +155% | 1 | 1 | 0% | 267 | 1,792 | +571% | 0 | 0 | — |
case-10 | fail→pass | 5,331 | 6,471 | +21% | 1 | 1 | 0% | 181 | 1,526 | +743% | 0 | 0 | — |
case-11 | fail→fail | 20,156 | 7,005 | -65% | 1 | 1 | 0% | 4,357 | 659 | -85% | 0 | 0 | — |
case-12 | fail→fail | 2,602 | 13,666 | +425% | 1 | 1 | 0% | 278 | 2,063 | +642% | 0 | 0 | — |
case-13 | pass→fail | 14,139 | 10,814 | -24% | 1 | 1 | 0% | 3,182 | 866 | -73% | 0 | 0 | — |
case-15 | fail→fail | 20,028 | 8,408 | -58% | 1 | 1 | 0% | 4,743 | 766 | -84% | 0 | 0 | — |
case-16 | fail→fail | 12,903 | 15,392 | +19% | 1 | 1 | 0% | 2,540 | 3,554 | +40% | 0 | 0 | — |
case-17 | fail→fail | 8,825 | 10,138 | +15% | 1 | 1 | 0% | 1,843 | 2,187 | +19% | 0 | 0 | — |
case-18 | fail→fail | 3,489 | 8,854 | +154% | 1 | 1 | 0% | 116 | 733 | +532% | 0 | 0 | — |
case-19 | fail→pass | 22,262 | 19,343 | -13% | 1 | 1 | 0% | 4,650 | 3,060 | -34% | 0 | 0 | — |
case-20 | fail→fail | 15,392 | 5,760 | -63% | 1 | 1 | 0% | 3,315 | 582 | -82% | 0 | 0 | — |
case-21 | fail→pass | 4,351 | 5,957 | +37% | 1 | 1 | 0% | 189 | 1,354 | +616% | 0 | 0 | — |
case-22 | fail→fail | 11,188 | 7,571 | -32% | 1 | 1 | 0% | 2,093 | 1,118 | -47% | 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 7 counted toward the lift figure. The other 15 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 +18 percentage points is the difference between those two pass rates over the 7 comparable cases. 3 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.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/30/2026 | +36% |
Other measured skills in the registry, with their headline benchmark lift.