Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Verify BRD-lite, PRD, SRS/FRS, UX, and test prerequisites before implementation starts.
.claude/skills/hoangnguyen0403-implementation-readiness/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 150% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 86% | 0% |
> !IMPORTANT] > Verify BRD-lite, PRD, SRS/FRS, UX, and test prerequisites before implementation starts.
Optional args: slug=<feature>, ticket=<id/url>, mode=interactive|autonomous|channel, channel=<id>, auto_continue=true|false, profile=business|hybrid|technical.
When the user asks to perform this workflow, execute the following steps:
Goal: Decide whether a planned change is ready for implementation or must return to planning/design.
implement-feature or dev-fix.plan-feature or design-solution.slug, verdict (READY/BLOCKED/PARTIAL), ready slices, blocking gaps, outcome report, next workflow.md# Implementation Readiness ## Verdict ## Ready Slices ## Blocking Gaps | Area | Gap | Owner/Input Needed | | --- | --- | --- | | [area] | [gap] | [owner/input] | ## Outcome Report feature_status: design_ready | partially_implemented | blocked requirement_trace: BRD-OBJ-* -> REQ-* -> AC-* -> SRS-* -> planned evidence completed_evidence: []; missing_evidence: []; decision_needed: []; recommended_next_workflow: implement-feature | plan-feature | design-solution ## Next Workflow ## Cost Report Call `get_session_cost(workflow="implementation-readiness")` before final handoff.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-09 | fail→pass | 16,917 | 14,584 | -14% | 1 | 1 | 0% | 3,164 | 3,743 | +18% | 0 | 0 | — |
case-01 | fail→fail | 23,928 | 15,479 | -35% | 1 | 1 | 0% | 4,845 | 3,711 | -23% | 0 | 0 | — |
case-02 | fail→fail | 16,068 | 19,456 | +21% | 1 | 1 | 0% | 2,790 | 4,547 | +63% | 0 | 0 | — |
case-03 | fail→pass | 19,515 | 12,129 | -38% | 1 | 1 | 0% | 3,392 | 3,133 | -8% | 0 | 0 | — |
case-04 | fail→fail | 8,348 | 9,677 | +16% | 1 | 1 | 0% | 1,369 | 2,585 | +89% | 0 | 0 | — |
case-05 | pass→pass | 12,646 | 6,706 | -47% | 1 | 1 | 0% | 2,184 | 2,117 | -3% | 0 | 0 | — |
case-06 | fail→pass | 10,223 | 6,037 | -41% | 1 | 1 | 0% | 1,708 | 1,966 | +15% | 0 | 0 | — |
case-07 | pass→pass | 15,518 | 6,057 | -61% | 1 | 1 | 0% | 2,377 | 1,882 | -21% | 0 | 0 | — |
case-08 | fail→pass | 7,079 | 9,850 | +39% | 1 | 1 | 0% | 1,035 | 2,589 | +150% | 0 | 0 | — |
case-10 | fail→pass | 11,467 | 15,406 | +34% | 1 | 1 | 0% | 2,020 | 3,750 | +86% | 0 | 0 | — |
case-11 | fail→pass | 6,710 | 8,103 | +21% | 1 | 1 | 0% | 1,159 | 2,273 | +96% | 0 | 0 | — |
case-12 | pass→pass | 8,619 | 6,478 | -25% | 1 | 1 | 0% | 1,428 | 2,014 | +41% | 0 | 0 | — |
case-13 | fail→pass | 10,943 | 8,825 | -19% | 1 | 1 | 0% | 1,835 | 2,499 | +36% | 0 | 0 | — |
case-14 | fail→pass | 9,256 | 5,862 | -37% | 1 | 1 | 0% | 1,666 | 1,900 | +14% | 0 | 0 | — |
case-21 | pass→fail | 12,717 | 14,226 | +12% | 1 | 1 | 0% | 2,176 | 3,348 | +54% | 0 | 0 | — |
case-15 | fail→fail | 6,224 | 10,897 | +75% | 1 | 1 | 0% | 1,132 | 2,799 | +147% | 0 | 0 | — |
case-16 | fail→pass | 11,747 | 8,230 | -30% | 1 | 1 | 0% | 1,952 | 2,376 | +22% | 0 | 0 | — |
case-17 | pass→pass | 7,823 | 7,318 | -6% | 1 | 1 | 0% | 1,382 | 2,192 | +59% | 0 | 0 | — |
case-18 | fail→pass | 14,132 | 14,203 | +1% | 1 | 1 | 0% | 2,283 | 3,003 | +32% | 0 | 0 | — |
case-19 | pass→fail | 22,273 | 15,158 | -32% | 1 | 1 | 0% | 5,452 | 3,208 | -41% | 0 | 0 | — |
case-20 | pass→fail | 22,164 | 19,021 | -14% | 1 | 1 | 0% | 3,805 | 4,246 | +12% | 0 | 0 | — |
case-22 | pass→fail | 4,498 | 9,143 | +103% | 1 | 1 | 0% | 790 | 2,388 | +202% | 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 +27 percentage points is the difference between those two pass rates over the 22 comparable cases. 4 cases got worse with the skill loaded, and they are 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.