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Get Started Free →Run dependency health audits across supported ecosystems
.claude/skills/hashgraph-online-deps-doctor/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-18 | ✗→✓ | ▲ Improved | -63% | 0% |
| case-08 | ✓→✗ | ▼ Worse | -88% | 0% |
| case-13 | ✓→✗ | ▼ Worse | -72% | 0% |
| case-23 | ✓→✗ | ▼ Worse | -68% | 0% |
| case-14 | ✓→✓ | ＝ Same ✓ | -14% | 0% |
Run python3 scripts/doctor.py "$@" from the deps-doctor plugin root.
Purpose: detect supported dependency ecosystems, run available local audit tools, and summarize advisories without failing when tools are missing.
Inputs: optional --format, --severity, and --ecosystem arguments supplied by the user.
Boundaries: do not edit files, do not install packages, do not use the network directly, and do not hide skipped tools.
Output contract: return either the helper output or a concise failure summary with command, exit status, and next step.
Verification contract: accept success only when the helper exits 0 and prints JSON or markdown matching the requested format.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | 11,942 | 10,760 | -10% | 1 | 1 | 0% | 498 | 478 | -4% | 0 | 0 | — |
case-01 | fail→fail | 6,609 | 19,190 | +190% | 1 | 1 | 0% | 1,205 | 771 | -36% | 0 | 0 | — |
case-02 | fail→fail | 41,737 | 5,342 | -87% | 1 | 1 | 0% | 4,439 | 354 | -92% | 0 | 0 | — |
case-04 | fail→fail | 11,427 | 17,299 | +51% | 1 | 1 | 0% | 1,109 | 893 | -19% | 0 | 0 | — |
case-05 | fail→fail | 9,824 | 13,296 | +35% | 1 | 1 | 0% | 925 | 837 | -10% | 0 | 0 | — |
case-06 | fail→fail | 26,900 | 13,190 | -51% | 1 | 1 | 0% | 3,142 | 623 | -80% | 0 | 0 | — |
case-07 | fail→fail | 22,643 | 12,005 | -47% | 1 | 1 | 0% | 2,951 | 467 | -84% | 0 | 0 | — |
case-08 | pass→fail | 19,033 | 9,679 | -49% | 1 | 1 | 0% | 2,846 | 346 | -88% | 0 | 0 | — |
case-09 | fail→fail | 15,250 | 15,233 | -0% | 1 | 1 | 0% | 1,858 | 581 | -69% | 0 | 0 | — |
case-10 | fail→fail | 14,071 | 13,105 | -7% | 1 | 1 | 0% | 778 | 477 | -39% | 0 | 0 | — |
case-11 | fail→fail | 8,277 | 4,585 | -45% | 1 | 1 | 0% | 392 | 441 | +13% | 0 | 0 | — |
case-12 | fail→fail | 6,283 | 6,660 | +6% | 1 | 1 | 0% | 1,078 | 553 | -49% | 0 | 0 | — |
case-13 | pass→fail | 16,629 | 13,915 | -16% | 1 | 1 | 0% | 1,925 | 539 | -72% | 0 | 0 | — |
case-18 | fail→pass | 14,054 | 2,924 | -79% | 1 | 1 | 0% | 1,579 | 586 | -63% | 0 | 0 | — |
case-14 | pass→pass | 15,229 | 7,671 | -50% | 1 | 1 | 0% | 1,744 | 1,501 | -14% | 0 | 0 | — |
case-15 | fail→fail | 12,555 | 12,946 | +3% | 1 | 1 | 0% | 2,182 | 557 | -74% | 0 | 0 | — |
case-16 | pass→pass | 15,154 | 7,611 | -50% | 1 | 1 | 0% | 1,782 | 763 | -57% | 0 | 0 | — |
case-17 | pass→pass | 8,743 | 8,018 | -8% | 1 | 1 | 0% | 1,391 | 486 | -65% | 0 | 0 | — |
case-19 | pass→pass | 9,414 | 3,576 | -62% | 1 | 1 | 0% | 1,480 | 735 | -50% | 0 | 0 | — |
case-20 | pass→pass | 12,240 | 8,787 | -28% | 1 | 1 | 0% | 1,192 | 731 | -39% | 0 | 0 | — |
case-21 | pass→pass | 12,186 | 8,824 | -28% | 1 | 1 | 0% | 2,130 | 813 | -62% | 0 | 0 | — |
case-22 | fail→fail | 7,143 | 16,039 | +125% | 1 | 1 | 0% | 604 | 426 | -29% | 0 | 0 | — |
case-23 | pass→fail | 7,443 | 10,254 | +38% | 1 | 1 | 0% | 1,330 | 421 | -68% | 0 | 0 | — |
case-24 | fail→fail | 16,369 | 10,090 | -38% | 1 | 1 | 0% | 2,026 | 401 | -80% | 0 | 0 | — |
case-25 | pass→pass | 10,690 | 2,719 | -75% | 1 | 1 | 0% | 910 | 496 | -45% | 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. 25 cases were attempted, and 8 counted toward the lift figure. The other 17 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 -8 percentage points is the difference between those two pass rates over the 8 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.