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Get Started Free →On-demand spawning of multiple engineering agents in parallel for large-context work. Use when a task is too big for a single agent and the work can be split into independent streams.
.claude/skills/evolution-foundation-dev-team/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 852% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 17% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 28% | 0% |
Derived from oh-my-claudecode (MIT, Yeachan Heo). Adapted for the EvoNexus Engineering Layer.
On-demand parallel agent spawning. When a task is too big for one agent and the work can be split into independent streams, spin up multiple engineering agents working in parallel.
workspace/development/research/[C]team-{topic}-{date}.mdFor "audit the entire auth module":
@scout-explorer → map all auth files@vault-security → security audit@lens-reviewer → code quality review@grid-tester → test coverage analysis@apex-architect → architecture analysisAll spawn in parallel, results combine into one report.
markdown## Team Investigation — {topic} ### Streams 1. {stream 1 result summary} 2. {stream 2 result summary} ... ### Cross-stream Findings [Insights that emerged from combining results] ### Recommendation [Unified next step]
dev-autopilot (which can spawn its own team internally)@compass-planner (often the consumer of team output)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 30,967 | 27,661 | -11% | 1 | 1 | 0% | 3,852 | 3,374 | -12% | 0 | 0 | — |
case-02 | fail→fail | 4,866 | 6,877 | +41% | 1 | 1 | 0% | 391 | 762 | +95% | 0 | 0 | — |
case-03 | fail→fail | 3,939 | 5,362 | +36% | 1 | 1 | 0% | 259 | 699 | +170% | 0 | 0 | — |
case-04 | pass→pass | 6,374 | 4,767 | -25% | 1 | 1 | 0% | 1,068 | 1,254 | +17% | 0 | 0 | — |
case-05 | pass→pass | 5,156 | 3,520 | -32% | 1 | 1 | 0% | 804 | 1,026 | +28% | 0 | 0 | — |
case-06 | pass→pass | 3,238 | 3,708 | +15% | 1 | 1 | 0% | 515 | 977 | +90% | 0 | 0 | — |
case-07 | fail→fail | 4,226 | 4,612 | +9% | 1 | 1 | 0% | 241 | 803 | +233% | 0 | 0 | — |
case-08 | fail→fail | 7,851 | 12,319 | +57% | 1 | 1 | 0% | 860 | 2,131 | +148% | 0 | 0 | — |
case-09 | fail→fail | 6,686 | 6,181 | -8% | 1 | 1 | 0% | 177 | 792 | +347% | 0 | 0 | — |
case-10 | fail→fail | 4,466 | 3,932 | -12% | 1 | 1 | 0% | 193 | 617 | +220% | 0 | 0 | — |
case-11 | fail→pass | 21,651 | 14,066 | -35% | 1 | 1 | 0% | 1,937 | 2,074 | +7% | 0 | 0 | — |
case-12 | fail→fail | 5,339 | 4,257 | -20% | 1 | 1 | 0% | 291 | 762 | +162% | 0 | 0 | — |
case-13 | fail→pass | 5,502 | 18,771 | +241% | 1 | 1 | 0% | 336 | 3,200 | +852% | 0 | 0 | — |
case-14 | fail→fail | 5,824 | 4,869 | -16% | 1 | 1 | 0% | 184 | 704 | +283% | 0 | 0 | — |
case-15 | fail→fail | 5,135 | 4,521 | -12% | 1 | 1 | 0% | 211 | 722 | +242% | 0 | 0 | — |
case-16 | fail→fail | 5,091 | 5,053 | -1% | 1 | 1 | 0% | 163 | 768 | +371% | 0 | 0 | — |
case-17 | fail→fail | 5,360 | 5,931 | +11% | 1 | 1 | 0% | 183 | 778 | +325% | 0 | 0 | — |
case-18 | fail→fail | 23,843 | 6,972 | -71% | 1 | 1 | 0% | 4,294 | 768 | -82% | 0 | 0 | — |
case-19 | fail→fail | 7,654 | 8,438 | +10% | 1 | 1 | 0% | 661 | 667 | +1% | 0 | 0 | — |
case-20 | fail→fail | 3,548 | 4,102 | +16% | 1 | 1 | 0% | 139 | 715 | +414% | 0 | 0 | — |
case-21 | fail→fail | 4,631 | 5,623 | +21% | 1 | 1 | 0% | 214 | 854 | +299% | 0 | 0 | — |
case-22 | fail→fail | 6,956 | 5,824 | -16% | 1 | 1 | 0% | 430 | 830 | +93% | 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 6 counted toward the lift figure. The other 16 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 +14 percentage points is the difference between those two pass rates over the 6 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.