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Get Started Free →Pre-flight validation checks for Output SDK workflow operations. Ensures conventions are followed, requirements are gathered, and quality gates are passed before workflow execution.
.claude/skills/growthxai-output-meta-pre-flight/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 32% | 0% |
subagent="" XML attribute, you MUST use the specified subagent to perform the instructions for that stepEnsure you have a deep understanding of the Output SDK and its capabilities. If not, use Claude Skill: output-meta-project-context and read it carefully.
Before proceeding with any workflow operation, verify:
.js extension for ESM modules.json()/.text() or cancel unused bodies withresponse.body?.cancel()
npx output dev auto-restarts the worker on file changes; if the worker runs detached, restart it manually with docker restart <project>-worker-1When information is not explicitly provided, apply these defaults:
output-dev-model-selection to pick the current default for the chosen provider. Don't pin a specific model ID here — the listing changes faster than the docs.Only stop to ask for clarification on:
{workflow_name} - The workflow being planned{project_root} - Root project directory path{requirements} - User-provided requirements{current_date} - Current date in YYYY-MM-DD format{sdk_version} - Current Output SDK versionBefore proceeding past pre-flight:
.outputai/plans directory does not exist, create it.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | fail→pass | 13,216 | 8,664 | -34% | 1 | 1 | 0% | 2,368 | 2,307 | -3% | 0 | 0 | — |
case-14 | fail→pass | 6,622 | 2,633 | -60% | 1 | 1 | 0% | 1,128 | 1,286 | +14% | 0 | 0 | — |
case-01 | fail→pass | 12,189 | 12,329 | +1% | 1 | 1 | 0% | 2,074 | 2,845 | +37% | 0 | 0 | — |
case-02 | fail→pass | 15,059 | 13,676 | -9% | 1 | 1 | 0% | 2,490 | 2,127 | -15% | 0 | 0 | — |
case-03 | fail→pass | 12,082 | 13,588 | +12% | 1 | 1 | 0% | 1,917 | 2,538 | +32% | 0 | 0 | — |
case-04 | fail→pass | 10,613 | 7,106 | -33% | 1 | 1 | 0% | 2,126 | 2,153 | +1% | 0 | 0 | — |
case-06 | pass→pass | 4,517 | 2,441 | -46% | 1 | 1 | 0% | 751 | 1,123 | +50% | 0 | 0 | — |
case-07 | pass→pass | 12,049 | 6,062 | -50% | 1 | 1 | 0% | 1,944 | 1,771 | -9% | 0 | 0 | — |
case-08 | fail→pass | 6,749 | 1,904 | -72% | 1 | 1 | 0% | 1,026 | 1,069 | +4% | 0 | 0 | — |
case-09 | pass→pass | 8,961 | 1,822 | -80% | 1 | 1 | 0% | 1,339 | 1,026 | -23% | 0 | 0 | — |
case-10 | fail→pass | 11,839 | 1,945 | -84% | 1 | 1 | 0% | 1,792 | 1,075 | -40% | 0 | 0 | — |
case-11 | fail→pass | 12,313 | 2,639 | -79% | 1 | 1 | 0% | 1,958 | 1,234 | -37% | 0 | 0 | — |
case-12 | pass→pass | 2,706 | 1,706 | -37% | 1 | 1 | 0% | 398 | 1,020 | +156% | 0 | 0 | — |
case-13 | fail→fail | 6,215 | 5,618 | -10% | 1 | 1 | 0% | 1,152 | 1,851 | +61% | 0 | 0 | — |
case-15 | fail→fail | 9,775 | 2,655 | -73% | 1 | 1 | 0% | 1,328 | 1,228 | -8% | 0 | 0 | — |
case-16 | pass→pass | 11,963 | 2,438 | -80% | 1 | 1 | 0% | 1,628 | 1,241 | -24% | 0 | 0 | — |
case-17 | pass→pass | 12,011 | 4,469 | -63% | 1 | 1 | 0% | 1,750 | 1,564 | -11% | 0 | 0 | — |
case-18 | fail→pass | 11,011 | 1,601 | -85% | 1 | 1 | 0% | 1,612 | 1,012 | -37% | 0 | 0 | — |
case-19 | fail→fail | 9,648 | 2,963 | -69% | 1 | 1 | 0% | 1,377 | 1,281 | -7% | 0 | 0 | — |
case-20 | pass→pass | 16,791 | 13,228 | -21% | 1 | 1 | 0% | 3,350 | 3,229 | -4% | 0 | 0 | — |
case-21 | pass→pass | 13,507 | 10,762 | -20% | 1 | 1 | 0% | 2,279 | 2,718 | +19% | 0 | 0 | — |
case-22 | pass→fail | 8,686 | 6,527 | -25% | 1 | 1 | 0% | 1,575 | 1,113 | -29% | 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 21 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 +41 percentage points is the difference between those two pass rates over the 21 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.