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Get Started Free →Spec creation with pattern references, acceptance criteria, and demo scripts. Use when creating implementation specs, defining acceptance criteria, breaking down user stories, or translating business requirements to technical specs. Do NOT use for implementation work -- this is for planning and specification only.
.claude/skills/bybren-llc-spec-creation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -47% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 8% | 0% |
Guide spec creation with clear acceptance criteria, pattern references, and testable success validation.
markdown# SPEC-{{TICKET_PREFIX}}-{number}: {Feature Name} ## Summary {One paragraph describing the feature} ## User Story As a [user type], I want [goal] so that [benefit]. ## Acceptance Criteria - [ ] {Testable criterion 1} - [ ] {Testable criterion 2} - [ ] {Testable criterion 3} ## Pattern References - **UI**: `patterns_library/ui/{pattern}.md` - **API**: `patterns_library/api/{pattern}.md` - **Database**: `patterns_library/database/{pattern}.md` ## Success Validation Command
{validation command}
## Demo Script
1. Navigate to {page}
2. Click {button}
3. Observe {expected behavior}
## Logical Commits
1. `feat(scope): implement data model [{{TICKET_PREFIX}}-{number}]`
2. `feat(scope): add API endpoint [{{TICKET_PREFIX}}-{number}]`
3. `feat(scope): create UI component [{{TICKET_PREFIX}}-{number}]`markdown# User Actions - [ ] User can {action} → {result} # Data - [ ] Data persists after {action} - [ ] User can only see their own {data type} # Errors - [ ] Invalid input shows {error message}
docs/archive/specs/spec_template.mdpatterns_library/README.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 14,692 | 7,849 | -47% | 1 | 1 | 0% | 2,786 | 2,039 | -27% | 0 | 0 | — |
case-02 | fail→pass | 19,583 | 7,600 | -61% | 1 | 1 | 0% | 3,768 | 1,982 | -47% | 0 | 0 | — |
case-03 | fail→pass | 15,513 | 8,571 | -45% | 1 | 1 | 0% | 2,919 | 2,210 | -24% | 0 | 0 | — |
case-04 | fail→pass | 12,586 | 6,699 | -47% | 1 | 1 | 0% | 2,089 | 1,747 | -16% | 0 | 0 | — |
case-05 | fail→pass | 7,381 | 2,928 | -60% | 1 | 1 | 0% | 1,326 | 1,028 | -22% | 0 | 0 | — |
case-06 | fail→pass | 6,487 | 5,584 | -14% | 1 | 1 | 0% | 1,131 | 1,225 | +8% | 0 | 0 | — |
case-07 | fail→pass | 5,338 | 3,223 | -40% | 1 | 1 | 0% | 966 | 999 | +3% | 0 | 0 | — |
case-08 | pass→pass | 8,838 | 4,048 | -54% | 1 | 1 | 0% | 1,467 | 1,102 | -25% | 0 | 0 | — |
case-09 | fail→pass | 8,907 | 3,969 | -55% | 1 | 1 | 0% | 1,501 | 1,201 | -20% | 0 | 0 | — |
case-10 | pass→pass | 7,435 | 3,505 | -53% | 1 | 1 | 0% | 1,314 | 1,082 | -18% | 0 | 0 | — |
case-11 | fail→pass | 7,558 | 2,864 | -62% | 1 | 1 | 0% | 1,162 | 961 | -17% | 0 | 0 | — |
case-12 | fail→pass | 11,349 | 4,986 | -56% | 1 | 1 | 0% | 1,998 | 1,348 | -33% | 0 | 0 | — |
case-13 | fail→pass | 9,642 | 4,304 | -55% | 1 | 1 | 0% | 1,743 | 1,266 | -27% | 0 | 0 | — |
case-14 | pass→pass | 12,366 | 6,146 | -50% | 1 | 1 | 0% | 2,169 | 1,580 | -27% | 0 | 0 | — |
case-15 | fail→pass | 7,140 | 1,462 | -80% | 1 | 1 | 0% | 1,222 | 690 | -44% | 0 | 0 | — |
case-16 | fail→fail | 3,872 | 2,460 | -36% | 1 | 1 | 0% | 685 | 920 | +34% | 0 | 0 | — |
case-17 | fail→pass | 3,334 | 1,611 | -52% | 1 | 1 | 0% | 556 | 720 | +29% | 0 | 0 | — |
case-18 | pass→pass | 8,008 | 4,097 | -49% | 1 | 1 | 0% | 1,416 | 1,190 | -16% | 0 | 0 | — |
case-19 | pass→pass | 7,982 | 1,925 | -76% | 1 | 1 | 0% | 1,245 | 759 | -39% | 0 | 0 | — |
case-20 | fail→fail | 1,848 | 2,227 | +21% | 1 | 1 | 0% | 251 | 844 | +236% | 0 | 0 | — |
case-21 | pass→fail | 16,472 | 16,276 | -1% | 1 | 1 | 0% | 2,803 | 3,420 | +22% | 0 | 0 | — |
case-22 | pass→pass | 8,683 | 8,464 | -3% | 1 | 1 | 0% | 1,475 | 1,801 | +22% | 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 +50 percentage points is the difference between those two pass rates over the 22 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.