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Get Started Free →PRD(プロダクト要件定義書)の作成、機能仕様の策定、受け入れ基準の定義を行うスキル。 「PRDを書いて」「機能仕様を作って」「要件定義して」等のリクエストで発動。
.claude/skills/minicoohei-feature-spec/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 123% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 102% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 130% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 75% | 0% |
You are an expert at writing product requirements documents (PRDs) and feature specifications. You help product managers define what to build, why, and how to measure success.
A well-structured PRD follows this template:
Write user stories in standard format: "As a user type], I want capability] so that benefit]"
Guidelines:
Example:
Must-Have (P0): The feature cannot ship without these. These represent the minimum viable version of the feature. Ask: "If we cut this, does the feature still solve the core problem?" If no, it is P0.
Nice-to-Have (P1): Significantly improves the experience but the core use case works without them. These often become fast follow-ups after launch.
Future Considerations (P2): Explicitly out of scope for v1 but we want to design in a way that supports them later. Documenting these prevents accidental architectural decisions that make them hard later.
For each requirement:
See the success metrics section below for detailed guidance.
Good user stories are:
Metrics that change quickly after launch (days to weeks):
Metrics that take time to develop (weeks to months):
Write acceptance criteria in Given/When/Then format or as a checklist:
Given/When/Then:
Example:
Checklist format:
Scope creep happens when:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-18 | pass→pass | 14,243 | 14,306 | +0% | 1 | 1 | 0% | 2,283 | 4,175 | +83% | 0 | 0 | — |
case-11 | fail→pass | 14,888 | 14,385 | -3% | 1 | 1 | 0% | 2,355 | 4,128 | +75% | 0 | 0 | — |
case-01 | fail→fail | 28,739 | 30,582 | +6% | 1 | 1 | 0% | 4,522 | 6,611 | +46% | 0 | 0 | — |
case-02 | fail→pass | 16,303 | 27,391 | +68% | 1 | 1 | 0% | 2,787 | 6,215 | +123% | 0 | 0 | — |
case-03 | fail→pass | 21,746 | 51,570 | +137% | 1 | 1 | 0% | 3,527 | 7,128 | +102% | 0 | 0 | — |
case-04 | pass→pass | 20,632 | 29,375 | +42% | 1 | 1 | 0% | 3,416 | 6,859 | +101% | 0 | 0 | — |
case-05 | pass→pass | 26,880 | 28,772 | +7% | 1 | 1 | 0% | 4,167 | 6,097 | +46% | 0 | 0 | — |
case-06 | fail→fail | 4,656 | 4,052 | -13% | 1 | 1 | 0% | 625 | 2,476 | +296% | 0 | 0 | — |
case-07 | pass→pass | 11,724 | 8,819 | -25% | 1 | 1 | 0% | 1,920 | 3,310 | +72% | 0 | 0 | — |
case-08 | fail→pass | 9,872 | 9,351 | -5% | 1 | 1 | 0% | 1,453 | 3,339 | +130% | 0 | 0 | — |
case-09 | pass→pass | 16,937 | 18,153 | +7% | 1 | 1 | 0% | 2,990 | 5,050 | +69% | 0 | 0 | — |
case-10 | pass→pass | 19,161 | 19,449 | +2% | 1 | 1 | 0% | 3,189 | 5,065 | +59% | 0 | 0 | — |
case-12 | pass→pass | 17,712 | 14,655 | -17% | 1 | 1 | 0% | 3,051 | 4,660 | +53% | 0 | 0 | — |
case-13 | fail→pass | 14,623 | 14,059 | -4% | 1 | 1 | 0% | 2,477 | 4,326 | +75% | 0 | 0 | — |
case-14 | fail→pass | 14,533 | 13,109 | -10% | 1 | 1 | 0% | 2,286 | 3,920 | +71% | 0 | 0 | — |
case-15 | pass→pass | 17,409 | 12,038 | -31% | 1 | 1 | 0% | 2,601 | 3,677 | +41% | 0 | 0 | — |
case-16 | fail→pass | 10,101 | 6,415 | -36% | 1 | 1 | 0% | 1,484 | 2,755 | +86% | 0 | 0 | — |
case-17 | fail→pass | 13,374 | 13,567 | +1% | 1 | 1 | 0% | 2,072 | 4,263 | +106% | 0 | 0 | — |
case-19 | pass→pass | 13,641 | 14,650 | +7% | 1 | 1 | 0% | 1,915 | 4,038 | +111% | 0 | 0 | — |
case-20 | pass→pass | 16,522 | 21,608 | +31% | 1 | 1 | 0% | 2,514 | 5,219 | +108% | 0 | 0 | — |
case-21 | pass→pass | 18,432 | 17,176 | -7% | 1 | 1 | 0% | 2,696 | 4,405 | +63% | 0 | 0 | — |
case-22 | pass→pass | 11,788 | 11,256 | -5% | 1 | 1 | 0% | 1,866 | 3,804 | +104% | 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 +36 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.