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Get Started Free →Use when the user wants multi-agent division of labor for research-led work and the lead should stay on the critical path while 1-2 bounded sidecars handle low-coupling tasks. Do not use this for tiny tasks, fully sequential debugging, or overlapping refactors.
.claude/skills/cnfjlhj-research-lead-sidecar/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 68% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 87% | 0% |
Use this skill for research-led work where implementation and evidence gathering are coupled, but the main agent should remain the lead. It keeps the critical path local while bounded sidecars handle non-blocking scout, worker, verifier, or writer tasks.
After the user approves an execution contract, use this as the default delegation shape: one lead owns framing, synthesis, and user-facing updates while 1-2 bounded sidecars help without taking over the mission.
Use when:
Do not use when:
When using this skill under an approved execution contract, keep the protocol aligned:
lead-sidecar is the chosen lane继续 should resume the active lead-plus-sidecar laneAt each meaningful checkpoint, the lead should be able to state:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 23,464 | 15,549 | -34% | 1 | 1 | 0% | 1,775 | 2,242 | +26% | 0 | 0 | — |
case-02 | pass→pass | 30,614 | 19,546 | -36% | 1 | 1 | 0% | 3,195 | 2,650 | -17% | 0 | 0 | — |
case-03 | fail→pass | 26,766 | 9,440 | -65% | 1 | 1 | 0% | 1,294 | 2,176 | +68% | 0 | 0 | — |
case-04 | fail→pass | 28,786 | 12,521 | -57% | 1 | 1 | 0% | 2,712 | 1,639 | -40% | 0 | 0 | — |
case-05 | pass→pass | 21,097 | 48,801 | +131% | 1 | 1 | 0% | 3,566 | 3,143 | -12% | 0 | 0 | — |
case-06 | pass→pass | 27,831 | 5,420 | -81% | 1 | 1 | 0% | 1,026 | 1,291 | +26% | 0 | 0 | — |
case-07 | pass→pass | 14,154 | 12,683 | -10% | 1 | 1 | 0% | 1,512 | 1,849 | +22% | 0 | 0 | — |
case-08 | fail→pass | 32,612 | 10,515 | -68% | 1 | 1 | 0% | 1,691 | 1,961 | +16% | 0 | 0 | — |
case-09 | fail→pass | 26,585 | 15,038 | -43% | 1 | 1 | 0% | 3,492 | 2,120 | -39% | 0 | 0 | — |
case-10 | pass→pass | 17,092 | 8,034 | -53% | 1 | 1 | 0% | 1,826 | 1,767 | -3% | 0 | 0 | — |
case-11 | pass→pass | 31,630 | 14,483 | -54% | 1 | 1 | 0% | 2,040 | 1,847 | -9% | 0 | 0 | — |
case-12 | pass→pass | 30,574 | 19,979 | -35% | 1 | 1 | 0% | 2,041 | 2,004 | -2% | 0 | 0 | — |
case-13 | pass→pass | 15,058 | 12,112 | -20% | 1 | 1 | 0% | 2,441 | 1,732 | -29% | 0 | 0 | — |
case-14 | pass→pass | 35,748 | 14,064 | -61% | 1 | 1 | 0% | 2,461 | 2,493 | +1% | 0 | 0 | — |
case-15 | pass→pass | 49,445 | 29,119 | -41% | 1 | 1 | 0% | 1,942 | 1,788 | -8% | 0 | 0 | — |
case-16 | pass→pass | 20,149 | 17,938 | -11% | 1 | 1 | 0% | 1,834 | 1,638 | -11% | 0 | 0 | — |
case-17 | pass→pass | 15,594 | 9,958 | -36% | 1 | 1 | 0% | 1,322 | 1,280 | -3% | 0 | 0 | — |
case-18 | fail→fail | 17,284 | 13,873 | -20% | 1 | 1 | 0% | 1,839 | 1,533 | -17% | 0 | 0 | — |
case-19 | pass→pass | 18,968 | 8,867 | -53% | 1 | 1 | 0% | 2,120 | 1,935 | -9% | 0 | 0 | — |
case-20 | fail→pass | 41,620 | 20,418 | -51% | 1 | 1 | 0% | 1,124 | 2,103 | +87% | 0 | 0 | — |
case-21 | pass→pass | 18,965 | 11,235 | -41% | 1 | 1 | 0% | 2,103 | 2,232 | +6% | 0 | 0 | — |
case-22 | pass→pass | 19,193 | 37,928 | +98% | 1 | 1 | 0% | 2,579 | 2,458 | -5% | 0 | 0 | — |
case-23 | pass→pass | 22,457 | 24,702 | +10% | 1 | 1 | 0% | 2,457 | 2,533 | +3% | 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 22 counted toward the lift figure. The other 1 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 +22 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.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/20/2026 | +5% |
Other measured skills in the registry, with their headline benchmark lift.