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Get Started Free →Runs a conversation-first design and tailoring workflow for any project shape (single or multi-service, mixed stacks, infra), then optionally produces a paste-ready sprout handoff for the target folder. Use when the user says /lab-init or asks to bootstrap, initialize, design, or tailor a lab or project before scaffolding. Lab OS npm seed commands apply only when sprout mode is lab-os-seed in this repository.
.claude/skills/bilal140202-lab-init/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 52% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 297% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 109% | 0% |
/lab-init)Vocabulary: Phase A = Plan (design and tailoring with the user and AI). Phase B = Sprout execution (materialize or run agreed steps after confirmation). Planting the seed at a root and sprouting commits that folder as a lab workspace—practically irreversible in the Lab OS model (no automated un-sprout). See docs/60-reference/FOUNDATIONS_VOCABULARY.md.
Default behavior: Phase A — Design & Tailoring (Plan) (conversation first, no file writes and no install/init commands unless the user explicitly asks for execution inside Phase A).
Optional: Phase B — Sprout only after the user confirms target path, sprout mode, and execution.
Non-Cursor users can mirror Phase B for this repo with npm run lab:init / npm run lab:verify when sprout mode is lab-os-seed here.
npm install, npm run init, npm run lab:init, or write project files by default.docs/60-reference/LAB_ARCHITECTURE_FLOWCHART.md) is maintained with /flow-diagram—compact Mermaid, lookup tables, no click. Use /lab-init for Phase A/B design and sprout; /flow-diagram when refreshing that canonical chart after structural changes.Cover what matters; skip irrelevant sections with explicit "N/A".
docs/project-structure.md — tree + annotationsdocs/diagrams/*.md or sections in docs/architecture.md for diagramsdocs/README.md — mini-index linking structure, diagrams, risks, sprout handoffUse clear headings. End Phase A with:
If no: stop with design artifacts in chat (user can save manually).
docs-only — materialize or refine documentation only (design package files).lab-os-seed — apply this repository’s Lab OS lab scaffold (lab.yaml, lab/ tree, validation/promotion scripts) in the target when this repo is the context; requires Node 20+ and npm here.custom — user describes stack-specific steps; agent proposes an ordered plan; still requires confirmation before execution.text[Sprout handoff — paste into AI in <target-folder>] Target: <path> Mode: docs-only | lab-os-seed | custom Summary: <one paragraph: goal, anchors, constraints> Authoritative docs: docs/project-structure.md; docs/diagrams/... or docs/architecture.md; docs/README.md Steps: 1) ... 2) ... Do not: <paths, resources, or actions off limits> STOP before: delete/overwrite/cloud apply/production impact — get explicit user confirmation
lab-os-seed)If the workspace is lab-os-lab and the user confirms execution:
npm installnpm run lab:init -- <target-or-default>npm run lab:verifyAlign targets with the user’s chosen folder. Do not assume every external project uses Node; Lab OS layout is optional and only for this mode in this seed repo.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-17 | pass→pass | 5,135 | 8,639 | +68% | 1 | 1 | 0% | 863 | 3,178 | +268% | 0 | 0 | — |
case-01 | fail→pass | 14,347 | 10,594 | -26% | 1 | 1 | 0% | 2,963 | 3,689 | +25% | 0 | 0 | — |
case-02 | fail→pass | 9,207 | 5,995 | -35% | 1 | 1 | 0% | 1,805 | 2,748 | +52% | 0 | 0 | — |
case-03 | fail→pass | 16,210 | 15,141 | -7% | 1 | 1 | 0% | 3,091 | 4,315 | +40% | 0 | 0 | — |
case-04 | fail→pass | 5,353 | 13,602 | +154% | 1 | 1 | 0% | 1,103 | 4,376 | +297% | 0 | 0 | — |
case-05 | fail→pass | 6,973 | 5,232 | -25% | 1 | 1 | 0% | 1,222 | 2,552 | +109% | 0 | 0 | — |
case-06 | fail→pass | 7,694 | 4,808 | -38% | 1 | 1 | 0% | 1,395 | 2,577 | +85% | 0 | 0 | — |
case-07 | pass→pass | 17,380 | 17,323 | -0% | 1 | 1 | 0% | 3,025 | 4,703 | +55% | 0 | 0 | — |
case-08 | pass→pass | 22,349 | 17,212 | -23% | 1 | 1 | 0% | 4,033 | 4,762 | +18% | 0 | 0 | — |
case-09 | fail→fail | 16,112 | 7,298 | -55% | 1 | 1 | 0% | 2,695 | 2,946 | +9% | 0 | 0 | — |
case-10 | fail→pass | 12,829 | 3,157 | -75% | 1 | 1 | 0% | 2,219 | 2,174 | -2% | 0 | 0 | — |
case-11 | fail→pass | 8,230 | 5,658 | -31% | 1 | 1 | 0% | 1,583 | 2,802 | +77% | 0 | 0 | — |
case-12 | fail→pass | 14,360 | 9,571 | -33% | 1 | 1 | 0% | 2,393 | 3,429 | +43% | 0 | 0 | — |
case-13 | fail→pass | 11,661 | 14,981 | +28% | 1 | 1 | 0% | 1,914 | 4,438 | +132% | 0 | 0 | — |
case-14 | fail→pass | 6,049 | 3,169 | -48% | 1 | 1 | 0% | 1,065 | 2,235 | +110% | 0 | 0 | — |
case-15 | pass→pass | 5,246 | 4,843 | -8% | 1 | 1 | 0% | 1,018 | 2,346 | +130% | 0 | 0 | — |
case-16 | pass→pass | 10,120 | 8,979 | -11% | 1 | 1 | 0% | 2,157 | 3,356 | +56% | 0 | 0 | — |
case-18 | fail→pass | 11,603 | 13,365 | +15% | 1 | 1 | 0% | 2,041 | 4,005 | +96% | 0 | 0 | — |
case-19 | fail→pass | 11,249 | 4,649 | -59% | 1 | 1 | 0% | 1,764 | 2,380 | +35% | 0 | 0 | — |
case-20 | fail→pass | 17,232 | 10,851 | -37% | 1 | 1 | 0% | 2,884 | 3,461 | +20% | 0 | 0 | — |
case-21 | fail→pass | 8,791 | 3,619 | -59% | 1 | 1 | 0% | 1,552 | 2,213 | +43% | 0 | 0 | — |
case-22 | pass→pass | 12,656 | 7,561 | -40% | 1 | 1 | 0% | 2,187 | 2,929 | +34% | 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. The headline lift of +68 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.
Other measured skills in the registry, with their headline benchmark lift.