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Get Started Free →A disciplined, scientific method for diagnosing bugs, failing tests, crashes, and regressions. Use this whenever something is broken and the cause is not already known: error messages, stack traces, "it worked before", flaky tests, wrong output, weird behavior, or after two failed fix attempts on the same problem. Reach for this instead of guess-and-check editing.
.claude/skills/adityaarakeri-debug-protocol/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -20% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -50% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -49% | 0% |
Turn each debugging step into evidence that confirms or rejects one explanation.
tdd-loop for the regression test and implementation.git bisect only in a clean disposable worktree or after explicit approval. Record the starting state and always run git bisect reset before leaving it.Report the reproduction, supported cause, rejected hypotheses, affected scope, confidence, and the next safe boundary. Do not round a plausible theory into a confirmed diagnosis.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-15 | fail→pass | 12,958 | 13,338 | +3% | 1 | 1 | 0% | 2,184 | 2,529 | +16% | 0 | 0 | — |
case-01 | fail→fail | 24,765 | 16,499 | -33% | 1 | 1 | 0% | 4,513 | 552 | -88% | 0 | 0 | — |
case-02 | fail→fail | 25,123 | 5,086 | -80% | 1 | 1 | 0% | 4,606 | 580 | -87% | 0 | 0 | — |
case-03 | fail→fail | 19,521 | 10,275 | -47% | 1 | 1 | 0% | 3,525 | 601 | -83% | 0 | 0 | — |
case-04 | pass→pass | 10,443 | 10,548 | +1% | 1 | 1 | 0% | 2,307 | 2,852 | +24% | 0 | 0 | — |
case-05 | fail→fail | 5,627 | 5,843 | +4% | 1 | 1 | 0% | 355 | 762 | +115% | 0 | 0 | — |
case-06 | pass→pass | 19,270 | 16,014 | -17% | 1 | 1 | 0% | 3,081 | 2,961 | -4% | 0 | 0 | — |
case-07 | fail→pass | 13,435 | 8,650 | -36% | 1 | 1 | 0% | 2,198 | 1,760 | -20% | 0 | 0 | — |
case-08 | pass→pass | 2,496 | 2,028 | -19% | 1 | 1 | 0% | 385 | 781 | +103% | 0 | 0 | — |
case-09 | fail→pass | 14,417 | 10,801 | -25% | 1 | 1 | 0% | 2,662 | 2,441 | -8% | 0 | 0 | — |
case-10 | pass→pass | 9,776 | 2,745 | -72% | 1 | 1 | 0% | 1,545 | 800 | -48% | 0 | 0 | — |
case-11 | fail→pass | 9,124 | 2,683 | -71% | 1 | 1 | 0% | 1,559 | 787 | -50% | 0 | 0 | — |
case-12 | fail→pass | 9,761 | 2,633 | -73% | 1 | 1 | 0% | 1,628 | 835 | -49% | 0 | 0 | — |
case-13 | pass→pass | 7,845 | 7,258 | -7% | 1 | 1 | 0% | 1,454 | 1,621 | +11% | 0 | 0 | — |
case-14 | fail→pass | 13,334 | 11,261 | -16% | 1 | 1 | 0% | 2,065 | 2,179 | +6% | 0 | 0 | — |
case-16 | pass→pass | 10,586 | 4,864 | -54% | 1 | 1 | 0% | 1,882 | 1,191 | -37% | 0 | 0 | — |
case-17 | pass→pass | 5,561 | 5,164 | -7% | 1 | 1 | 0% | 980 | 1,279 | +31% | 0 | 0 | — |
case-18 | fail→pass | 12,589 | 4,092 | -67% | 1 | 1 | 0% | 2,025 | 1,087 | -46% | 0 | 0 | — |
case-19 | pass→pass | 10,598 | 4,879 | -54% | 1 | 1 | 0% | 1,763 | 1,190 | -33% | 0 | 0 | — |
case-20 | pass→pass | 11,090 | 8,306 | -25% | 1 | 1 | 0% | 1,939 | 1,830 | -6% | 0 | 0 | — |
case-21 | pass→pass | 7,741 | 5,616 | -27% | 1 | 1 | 0% | 1,379 | 1,254 | -9% | 0 | 0 | — |
case-22 | fail→fail | 9,027 | 2,352 | -74% | 1 | 1 | 0% | 1,589 | 805 | -49% | 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 19 counted toward the lift figure. The other 3 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 19 comparable cases. 1 case got worse with the skill loaded, and it is 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.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/3/2026 | +18% |
| gemini-3.6-flash | verified | 8/3/2026 | +23% |
Other measured skills in the registry, with their headline benchmark lift.