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Get Started Free →Verify work actually meets its brief BEFORE declaring it done — a structured self-review pass that catches the gaps, unmet requirements, and untested claims that 'looks finished' hides. Use before handing over any deliverable (document, code, analysis, plan), when past work kept coming back with 'you missed…', or as the standing final step of any multi-step task. Produces the verified deliverable plus a short verification record: what was checked, what was found and fixed, what remains open.
.claude/skills/mohitagw15856-verification-before-completion/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-03 | ✓→✗ | ▼ Worse | 50% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 49% | 0% |
| case-05 | ✓→✗ | ▼ Worse | 37% | 0% |
"Done" is a claim, and most agents (and humans) declare it by feeling — the output looks complete, reads well, compiles. This skill replaces the feeling with a check: re-derive what was actually asked, audit the work against it, try to break it, and only then hand it over. The gap between looks-done and is-done is where rework lives.
(appended to, or accompanying, the deliverable)
Verified: against the original ask, N requirements] · ran: tests/checks/queries] · 1 adversarial read] Found & fixed: the 1-4 real findings] Open / not verified: residuals, stated — "performance under load not tested"]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 34,773 | 48,339 | +39% | 1 | 1 | 0% | 5,882 | 7,933 | +35% | 0 | 0 | — |
case-02 | fail→fail | 45,682 | 28,850 | -37% | 1 | 1 | 0% | 4,843 | 4,396 | -9% | 0 | 0 | — |
case-03 | pass→fail | 37,479 | 49,950 | +33% | 1 | 1 | 0% | 5,758 | 8,618 | +50% | 0 | 0 | — |
case-04 | pass→fail | 16,136 | 16,360 | +1% | 1 | 1 | 0% | 1,597 | 2,382 | +49% | 0 | 0 | — |
case-05 | pass→fail | 25,110 | 32,503 | +29% | 1 | 1 | 0% | 3,087 | 4,233 | +37% | 0 | 0 | — |
case-06 | fail→fail | 23,345 | 16,533 | -29% | 1 | 1 | 0% | 2,935 | 3,491 | +19% | 0 | 0 | — |
case-07 | fail→fail | 44,006 | 23,059 | -48% | 1 | 1 | 0% | 5,999 | 5,315 | -11% | 0 | 0 | — |
case-08 | fail→fail | 33,300 | 42,279 | +27% | 1 | 1 | 0% | 6,405 | 8,345 | +30% | 0 | 0 | — |
case-09 | pass→fail | 46,158 | 45,289 | -2% | 1 | 1 | 0% | 8,233 | 9,114 | +11% | 0 | 0 | — |
case-10 | fail→pass | 45,416 | 47,820 | +5% | 1 | 1 | 0% | 6,287 | 6,539 | +4% | 0 | 0 | — |
case-11 | fail→fail | 39,822 | 38,351 | -4% | 1 | 1 | 0% | 8,224 | 9,105 | +11% | 0 | 0 | — |
case-12 | pass→fail | 23,526 | 21,529 | -8% | 1 | 1 | 0% | 4,915 | 5,504 | +12% | 0 | 0 | — |
case-13 | pass→fail | 28,456 | 28,051 | -1% | 1 | 1 | 0% | 4,696 | 4,014 | -15% | 0 | 0 | — |
case-14 | pass→fail | 23,740 | 34,491 | +45% | 1 | 1 | 0% | 3,476 | 6,329 | +82% | 0 | 0 | — |
case-15 | fail→pass | 38,408 | 26,017 | -32% | 1 | 1 | 0% | 5,656 | 5,121 | -9% | 0 | 0 | — |
case-16 | pass→pass | 23,221 | 27,635 | +19% | 1 | 1 | 0% | 3,273 | 4,811 | +47% | 0 | 0 | — |
case-17 | pass→pass | 19,209 | 30,065 | +57% | 1 | 1 | 0% | 2,104 | 4,489 | +113% | 0 | 0 | — |
case-18 | pass→pass | 21,146 | 22,231 | +5% | 1 | 1 | 0% | 3,037 | 3,474 | +14% | 0 | 0 | — |
case-19 | pass→pass | 28,142 | 31,946 | +14% | 1 | 1 | 0% | 4,734 | 5,329 | +13% | 0 | 0 | — |
case-20 | fail→fail | 28,609 | 34,958 | +22% | 1 | 1 | 0% | 5,183 | 6,784 | +31% | 0 | 0 | — |
case-21 | pass→pass | 34,606 | 45,337 | +31% | 1 | 1 | 0% | 6,032 | 9,106 | +51% | 0 | 0 | — |
case-22 | fail→fail | 30,950 | 25,507 | -18% | 1 | 1 | 0% | 4,507 | 5,013 | +11% | 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. 8 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.