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Get Started Free →Takes meeting transcripts, extracts action items with owners and deadlines, detects implicit commitments, generates structured meeting summaries, and outputs task files compatible with Linear, GitHub Issues, and other project management tools.
.claude/skills/onewave-ai-meeting-to-tasks/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 194% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 358% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 323% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 452% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 483% | 0% |
Transform raw meeting transcripts into structured, actionable outputs: decisions, action items, owners, deadlines, and open questions -- including implicit commitments participants may not realize they made.
references/extraction-schema.md -- YAML field schemas for every extraction phasereferences/markers.md -- Decision/action/implicit markers, priority + deadline inference, confidence scoringreferences/output-templates.md -- Meeting summary, task files (Linear/GitHub/generic), follow-up email, directory layoutreferences/meeting-types.md -- Meeting-type detection and per-type focusreferences/meeting-types.md.references/extraction-schema.md: metadata, decisions, action items, open questions, parking lot, discussion points. Apply the markers and inference rules in references/markers.md. Preserve the exact source quote for every item.references/markers.md.references/output-templates.md. Confirm which project management format(s) the user wants before generating task files. Always generate the follow-up email, even if unasked.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 3,614 | 11,212 | +210% | 1 | 1 | 0% | 548 | 1,609 | +194% | 0 | 0 | — |
case-02 | pass→pass | 2,692 | 6,735 | +150% | 1 | 1 | 0% | 444 | 1,869 | +321% | 0 | 0 | — |
case-03 | fail→pass | 2,050 | 3,810 | +86% | 1 | 1 | 0% | 329 | 1,506 | +358% | 0 | 0 | — |
case-04 | pass→pass | 4,466 | 5,312 | +19% | 1 | 1 | 0% | 577 | 1,663 | +188% | 0 | 0 | — |
case-05 | pass→pass | 7,759 | 8,161 | +5% | 1 | 1 | 0% | 1,233 | 2,002 | +62% | 0 | 0 | — |
case-06 | pass→pass | 4,637 | 6,958 | +50% | 1 | 1 | 0% | 762 | 1,878 | +146% | 0 | 0 | — |
case-07 | fail→pass | 1,773 | 2,874 | +62% | 1 | 1 | 0% | 300 | 1,270 | +323% | 0 | 0 | — |
case-08 | fail→pass | 2,713 | 5,478 | +102% | 1 | 1 | 0% | 298 | 1,645 | +452% | 0 | 0 | — |
case-09 | fail→fail | 2,311 | 4,782 | +107% | 1 | 1 | 0% | 400 | 1,481 | +270% | 0 | 0 | — |
case-10 | fail→pass | 2,262 | 8,875 | +292% | 1 | 1 | 0% | 396 | 2,307 | +483% | 0 | 0 | — |
case-11 | pass→pass | 3,474 | 7,319 | +111% | 1 | 1 | 0% | 560 | 2,073 | +270% | 0 | 0 | — |
case-12 | pass→pass | 10,340 | 9,698 | -6% | 1 | 1 | 0% | 1,818 | 2,439 | +34% | 0 | 0 | — |
case-13 | pass→pass | 9,751 | 11,124 | +14% | 1 | 1 | 0% | 1,862 | 3,015 | +62% | 0 | 0 | — |
case-14 | pass→pass | 4,262 | 5,122 | +20% | 1 | 1 | 0% | 780 | 1,783 | +129% | 0 | 0 | — |
case-15 | pass→pass | 4,092 | 4,714 | +15% | 1 | 1 | 0% | 822 | 1,764 | +115% | 0 | 0 | — |
case-16 | fail→fail | 2,198 | 1,921 | -13% | 1 | 1 | 0% | 376 | 1,217 | +224% | 0 | 0 | — |
case-17 | fail→pass | 2,585 | 4,831 | +87% | 1 | 1 | 0% | 432 | 1,751 | +305% | 0 | 0 | — |
case-18 | pass→pass | 3,155 | 7,270 | +130% | 1 | 1 | 0% | 595 | 2,123 | +257% | 0 | 0 | — |
case-19 | pass→pass | 2,379 | 5,675 | +139% | 1 | 1 | 0% | 430 | 1,787 | +316% | 0 | 0 | — |
case-20 | pass→pass | 5,689 | 3,171 | -44% | 1 | 1 | 0% | 921 | 1,362 | +48% | 0 | 0 | — |
case-21 | fail→fail | 39,455 | 35,606 | -10% | 1 | 1 | 0% | 6,181 | 7,010 | +13% | 0 | 0 | — |
case-22 | pass→pass | 5,634 | 5,729 | +2% | 1 | 1 | 0% | 981 | 1,803 | +84% | 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.
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.