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Get Started Free →Meta-skill for generating team skills following the v4 architecture pattern. Produces complete skill packages with SKILL.md router, coordinator, worker roles, specs, and templates. Triggers on "team-designer", "design team".
.claude/skills/catlog22-team-designer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 77% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 79% | 0% |
Generate complete team skills following the team-lifecycle-v4 architecture: SKILL.md as universal router, coordinator with beat model, worker roles with optional commands/, shared specs, and templates.
┌─────────────────────────────────────────────────────────────────┐
│ Team Skill Designer (SKILL.md) │
│ → Orchestrator: gather requirements, generate files, validate │
└───────────────────────────┬──────────────────────────────────────┘
│
┌───────────┬───────────┼───────────┬───────────┐
↓ ↓ ↓ ↓
┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│ Phase 1 │ │ Phase 2 │ │ Phase 3 │ │ Phase 4 │
│ Require │ │ Scaffold│ │ Content │ │ Valid │
│ Analysis│ │ Gen │ │ Gen │ │ & Report│
└─────────┘ └─────────┘ └─────────┘ └─────────┘
↓ ↓ ↓ ↓
teamConfig SKILL.md roles/ Validated
+ dirs specs/ skill pkg
templates/team-lifecycle-v4 as reference implementation at ~ or <project>/.claude/skills/team-lifecycle-v4/commands/ (2+ commands) vs inline logic (1 command)Progress Tracking Initialization:
└─ functions.update_plan([all 4 phases, Phase 1 as in_progress])
Input Parsing:
└─ Parse user requirements (skill name, roles, pipelines, domain)
Phase 1: Requirements Analysis
└─ Ref: phases/01-requirements-analysis.md
├─ Tasks: Detect input → Gather roles → Define pipelines → Build teamConfig
└─ Output: teamConfig
└─ Progress: mark phase-1 completed, phase-2 in_progress
Phase 2: Scaffold Generation
└─ Ref: phases/02-scaffold-generation.md
├─ Tasks: Create dirs → Generate SKILL.md router → Verify
└─ Output: SKILL.md + directory structure
└─ Progress: mark phase-2 completed, phase-3 in_progress
Phase 3: Content Generation
└─ Ref: phases/03-content-generation.md
├─ Tasks: Coordinator → Workers → Specs → Templates
└─ Output: roles/**/*.md, specs/*.md, templates/*.md
└─ Progress: mark phase-3 completed, phase-4 in_progress
Phase 4: Validation
└─ Ref: phases/04-validation.md
└─ Output: Validation report (PASS/REVIEW/FAIL)
└─ Progress: mark phase-4 completed
Return:
└─ Summary with skill location and usage instructionsPhase Reference Documents (read on-demand when phase executes):
| Phase | Document | Purpose | |-------|----------|---------| | 1 | phases/01-requirements-analysis.md | Gather team skill requirements, build teamConfig | | 2 | phases/02-scaffold-generation.md | Generate SKILL.md router and directory structure | | 3 | phases/03-content-generation.md | Generate coordinator, workers, specs, templates | | 4 | phases/04-validation.md | Validate structure, references, and consistency |
Generated skills follow the architecture of ~ or <project>/.claude/skills/team-lifecycle-v4/:
.claude/skills/<skill-name>/
├── SKILL.md # Universal router (all roles read)
├── roles/
│ ├── coordinator/
│ │ ├── role.md # Orchestrator + beat model + entry router
│ │ └── commands/
│ │ ├── analyze.md # Task analysis
│ │ ├── dispatch.md # Task chain creation
│ │ └── monitor.md # Beat control + callbacks
│ ├── <inline-worker>/
│ │ └── role.md # Phase 2-4 embedded (simple role)
│ └── <command-worker>/
│ ├── role.md # Phase 2-4 dispatcher
│ └── commands/
│ ├── <cmd-1>.md
│ └── <cmd-2>.md
├── specs/
│ ├── pipelines.md # Pipeline definitions + task registry
│ └── <domain-specs>.md # Domain-specific specifications
└── templates/ # Optional document templatesInitialize progress tracking before Phase 1 execution:
functions.update_plan([
{ id: "phase-1", title: "Phase 1: Requirements Analysis", status: "in_progress" },
{ id: "phase-2", title: "Phase 2: Scaffold Generation", status: "pending" },
{ id: "phase-3", title: "Phase 3: Content Generation", status: "pending" },
{ id: "phase-4", title: "Phase 4: Validation", status: "pending" }
])At each phase transition:
functions.update_plan([{id: "phase-1", status: "completed"}, {id: "phase-2", status: "in_progress"}])functions.update_plan([{id: "phase-2", status: "completed"}, {id: "phase-3", status: "in_progress"}])functions.update_plan([{id: "phase-3", status: "completed"}, {id: "phase-4", status: "in_progress"}])functions.update_plan([{id: "phase-4", status: "completed"}])User Input (skill name, roles, pipelines)
↓
Phase 1: Requirements Analysis
↓ Output: teamConfig
↓
Phase 2: Scaffold Generation
↓ Input: teamConfig
↓ Output: SKILL.md + skillDir
↓
Phase 3: Content Generation
↓ Input: teamConfig + skillDir
↓ Output: roles/, specs/, templates/
↓
Phase 4: Validation
↓ Input: teamConfig + all files
↓ Output: validation report
↓
Return summary to userConvert user input to structured format:
SKILL_NAME: [kebab-case name, e.g., team-code-review]
DOMAIN: [what this team does, e.g., "multi-stage code review with security analysis"]
ROLES: [worker roles beyond coordinator, e.g., "analyst, reviewer, security-expert"]
PIPELINES: [pipeline types and flows, e.g., "review-only: SCAN-001 → REVIEW-001 → REPORT-001"]
SESSION_PREFIX: [3-4 char, e.g., TCR]| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | fail→pass | 11,479 | 4,774 | -58% | 1 | 1 | 0% | 1,652 | 2,617 | +58% | 0 | 0 | — |
case-01 | fail→fail | 35,097 | 7,394 | -79% | 1 | 1 | 0% | 6,201 | 2,593 | -58% | 0 | 0 | — |
case-02 | fail→fail | 32,311 | 7,655 | -76% | 1 | 1 | 0% | 6,229 | 2,422 | -61% | 0 | 0 | — |
case-03 | fail→fail | 32,743 | 7,166 | -78% | 1 | 1 | 0% | 5,143 | 2,243 | -56% | 0 | 0 | — |
case-04 | pass→pass | 11,999 | 12,634 | +5% | 1 | 1 | 0% | 2,080 | 4,042 | +94% | 0 | 0 | — |
case-05 | pass→pass | 7,604 | 13,148 | +73% | 1 | 1 | 0% | 1,171 | 4,117 | +252% | 0 | 0 | — |
case-06 | pass→pass | 11,896 | 9,151 | -23% | 1 | 1 | 0% | 2,132 | 3,568 | +67% | 0 | 0 | — |
case-07 | pass→pass | 10,456 | 6,668 | -36% | 1 | 1 | 0% | 1,915 | 2,994 | +56% | 0 | 0 | — |
case-08 | fail→pass | 10,104 | 6,082 | -40% | 1 | 1 | 0% | 1,735 | 3,071 | +77% | 0 | 0 | — |
case-09 | fail→pass | 9,989 | 4,593 | -54% | 1 | 1 | 0% | 1,805 | 2,823 | +56% | 0 | 0 | — |
case-10 | fail→pass | 9,983 | 2,719 | -73% | 1 | 1 | 0% | 1,780 | 2,437 | +37% | 0 | 0 | — |
case-11 | fail→pass | 9,291 | 4,855 | -48% | 1 | 1 | 0% | 1,555 | 2,780 | +79% | 0 | 0 | — |
case-13 | fail→pass | 7,766 | 3,487 | -55% | 1 | 1 | 0% | 1,357 | 2,582 | +90% | 0 | 0 | — |
case-14 | pass→pass | 8,555 | 2,928 | -66% | 1 | 1 | 0% | 1,446 | 2,474 | +71% | 0 | 0 | — |
case-15 | fail→pass | 12,619 | 6,814 | -46% | 1 | 1 | 0% | 1,619 | 3,242 | +100% | 0 | 0 | — |
case-16 | fail→fail | 9,100 | 2,479 | -73% | 1 | 1 | 0% | 1,448 | 2,452 | +69% | 0 | 0 | — |
case-17 | fail→pass | 8,263 | 2,929 | -65% | 1 | 1 | 0% | 1,518 | 2,436 | +60% | 0 | 0 | — |
case-18 | fail→pass | 11,615 | 5,870 | -49% | 1 | 1 | 0% | 1,653 | 3,097 | +87% | 0 | 0 | — |
case-19 | fail→pass | 12,810 | 6,559 | -49% | 1 | 1 | 0% | 1,925 | 3,045 | +58% | 0 | 0 | — |
case-20 | fail→pass | 6,158 | 3,968 | -36% | 1 | 1 | 0% | 824 | 2,558 | +210% | 0 | 0 | — |
case-21 | pass→fail | 9,714 | 3,610 | -63% | 1 | 1 | 0% | 1,624 | 2,601 | +60% | 0 | 0 | — |
case-22 | pass→pass | 8,231 | 2,313 | -72% | 1 | 1 | 0% | 1,299 | 2,389 | +84% | 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 19 counted toward the lift figure. The other 3 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 +45 percentage points is the difference between those two pass rates over the 19 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.