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Get Started Free →Triggered by "tidy up", "clean up transactions", "categorize uncategorized", "organize my transactions"
.claude/skills/davepoon-tidy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -23% | 0% |
Batch-categorize uncategorized transactions by clustering similar ones and applying categories in bulk.
query MCP tool:json { "detail": true, "is_uncategorized": true, "period": "last_90d", "limit": 200, "sort": "-amount" } If $ARGUMENTS contains a time period (e.g. "this month", "last 30 days"), use that instead of last_90d.
Ask the user to approve, modify, or skip each cluster.
admin { "entity": "rule", "action": "preview", ... }admin { "entity": "rule", "action": "create", ... }json { "action": "categorize", "filter": { "search": "<pattern>" }, "category_name": "<approved_category>" } Also set the party if one was approved: json { "action": "set_party", "filter": { "search": "<pattern>" }, "party_name": "<approved_party>" }
Stick to the facts. Present findings and suggestions without judgement — no commentary on spending habits. Just clear, plain-language observations and actionable options.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 3,445 | 4,472 | +30% | 1 | 1 | 0% | 544 | 889 | +63% | 0 | 0 | — |
case-02 | fail→fail | 7,632 | 5,219 | -32% | 1 | 1 | 0% | 1,265 | 1,028 | -19% | 0 | 0 | — |
case-03 | fail→fail | 6,854 | 5,521 | -19% | 1 | 1 | 0% | 1,141 | 940 | -18% | 0 | 0 | — |
case-04 | fail→fail | 1,952 | 5,170 | +165% | 1 | 1 | 0% | 291 | 929 | +219% | 0 | 0 | — |
case-05 | pass→pass | 6,320 | 2,960 | -53% | 1 | 1 | 0% | 993 | 1,208 | +22% | 0 | 0 | — |
case-06 | fail→pass | 5,552 | 4,382 | -21% | 1 | 1 | 0% | 1,006 | 1,523 | +51% | 0 | 0 | — |
case-07 | fail→fail | 6,322 | 4,457 | -30% | 1 | 1 | 0% | 949 | 1,418 | +49% | 0 | 0 | — |
case-08 | fail→pass | 9,248 | 4,496 | -51% | 1 | 1 | 0% | 1,503 | 1,495 | -1% | 0 | 0 | — |
case-09 | fail→pass | 6,065 | 5,108 | -16% | 1 | 1 | 0% | 1,032 | 1,484 | +44% | 0 | 0 | — |
case-10 | fail→pass | 11,248 | 3,176 | -72% | 1 | 1 | 0% | 1,304 | 1,261 | -3% | 0 | 0 | — |
case-11 | fail→pass | 12,110 | 2,556 | -79% | 1 | 1 | 0% | 1,578 | 1,209 | -23% | 0 | 0 | — |
case-12 | pass→pass | 2,938 | 8,449 | +188% | 1 | 1 | 0% | 596 | 1,640 | +175% | 0 | 0 | — |
case-13 | pass→pass | 9,545 | 5,807 | -39% | 1 | 1 | 0% | 1,369 | 1,725 | +26% | 0 | 0 | — |
case-14 | fail→fail | 5,583 | 1,896 | -66% | 1 | 1 | 0% | 972 | 1,034 | +6% | 0 | 0 | — |
case-15 | pass→pass | 7,610 | 3,891 | -49% | 1 | 1 | 0% | 1,183 | 1,376 | +16% | 0 | 0 | — |
case-16 | fail→pass | 7,015 | 6,506 | -7% | 1 | 1 | 0% | 905 | 1,882 | +108% | 0 | 0 | — |
case-17 | pass→pass | 12,326 | 5,149 | -58% | 1 | 1 | 0% | 2,006 | 1,474 | -27% | 0 | 0 | — |
case-18 | fail→pass | 20,034 | 2,545 | -87% | 1 | 1 | 0% | 2,413 | 1,166 | -52% | 0 | 0 | — |
case-19 | fail→fail | 9,822 | 3,596 | -63% | 1 | 1 | 0% | 1,439 | 1,310 | -9% | 0 | 0 | — |
case-20 | pass→pass | 14,259 | 12,829 | -10% | 1 | 1 | 0% | 2,452 | 3,208 | +31% | 0 | 0 | — |
case-21 | pass→pass | 4,660 | 4,186 | -10% | 1 | 1 | 0% | 774 | 1,393 | +80% | 0 | 0 | — |
case-22 | pass→pass | 7,570 | 10,349 | +37% | 1 | 1 | 0% | 1,449 | 2,454 | +69% | 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, and 18 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +32 percentage points is the difference between those two pass rates over the 18 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.