Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Validate results through statistical testing, ROPE judgment, reproducibility re-runs, and final synthesis
.claude/skills/yogsoth-ai-result-validation-loop/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -51% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -20% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 78% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -30% | 0% |
pseudocodeFUNCTION result_validation_loop(raw_results, experiment_design): // Phase 1: Collect and structure structured = SPAWN result-collection(raw_results) VALIDATE structured.complete // Phase 2: Statistical testing stats = SPAWN statistical-testing(structured, experiment_design.hypotheses) // Phase 3: ROPE Judgment rope = experiment_design.rope // pre-registered ROPE bounds IF stats.posterior_in_rope > 0.95: judgment = "ACCEPT_NULL" // practically equivalent ELIF stats.posterior_above_rope > 0.95: judgment = "REJECT_NULL" // meaningful effect detected ELSE: judgment = "UNDECIDED" // need more data END // Phase 4: Reproducibility verification IF judgment != "UNDECIDED": repro = SPAWN reproducibility-verification( experiment_design, n_reruns = 3, seeds = [42, 123, 7] ) IF repro.icc < 0.5: judgment = "NOT_REPRODUCIBLE" ELIF repro.icc < 0.75: judgment = judgment + "_PARTIAL_REPRO" ELSE: judgment = judgment + "_REPRODUCIBLE" END ELSE: // Undecided — still run reproducibility to check if issue is noise repro = SPAWN reproducibility-verification( experiment_design, n_reruns = 5, // more runs for undecided cases seeds = [42, 123, 7, 256, 999] ) IF repro.variance_explained_by_seed > 0.5: judgment = "HIGH_VARIANCE_ACROSS_SEEDS" END END // Phase 5: Synthesis report = SPAWN execution-synthesis({ structured_results: structured, statistical_tests: stats, judgment: judgment, reproducibility: repro, experiment_design: experiment_design }) RETURN report END
| Condition | Action | |-----------|--------| | Results incomplete (missing tasks) | Report gaps, analyze available data | | P(in ROPE) > 95% | Accept null (no practical difference) | | P(above ROPE) > 95% | Reject null (meaningful effect) | | Neither threshold met | Undecided — recommend more data | | ICC > 0.75 | Results reproducible | | ICC 0.5-0.75 | Partially reproducible — flag | | ICC < 0.5 | Not reproducible — investigate sources of variance | | High seed-dependent variance | Report instability, recommend investigation |
Before producing final synthesis:
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | execution-synthesis | Synthesize complete execution report from all results, tests, and reproducibility data | | reproducibility-verification | Verify result reproducibility via re-runs with different seeds and ICC comparison | | result-collection | Collect experiment outputs — metrics, logs, artifacts — into structured result set | | statistical-testing | Execute statistical tests — bootstrap, permutation, Bayesian ROPE — on experiment results |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 16,286 | 12,322 | -24% | 1 | 1 | 0% | 2,034 | 3,530 | +74% | 0 | 0 | — |
case-01 | fail→pass | 42,210 | 22,303 | -47% | 1 | 1 | 0% | 8,267 | 4,012 | -51% | 0 | 0 | — |
case-02 | fail→fail | 36,726 | 26,491 | -28% | 1 | 1 | 0% | 6,905 | 5,081 | -26% | 0 | 0 | — |
case-03 | fail→pass | 39,628 | 27,861 | -30% | 1 | 1 | 0% | 6,501 | 5,228 | -20% | 0 | 0 | — |
case-05 | pass→pass | 30,132 | 26,846 | -11% | 1 | 1 | 0% | 4,154 | 5,139 | +24% | 0 | 0 | — |
case-06 | pass→pass | 22,781 | 27,833 | +22% | 1 | 1 | 0% | 2,956 | 5,623 | +90% | 0 | 0 | — |
case-07 | fail→pass | 21,276 | 10,945 | -49% | 1 | 1 | 0% | 1,096 | 1,946 | +78% | 0 | 0 | — |
case-08 | fail→pass | 12,085 | 4,918 | -59% | 1 | 1 | 0% | 2,067 | 1,868 | -10% | 0 | 0 | — |
case-09 | fail→pass | 24,923 | 11,308 | -55% | 1 | 1 | 0% | 2,614 | 1,840 | -30% | 0 | 0 | — |
case-10 | fail→pass | 9,032 | 7,736 | -14% | 1 | 1 | 0% | 1,555 | 2,181 | +40% | 0 | 0 | — |
case-11 | fail→pass | 34,867 | 3,390 | -90% | 1 | 1 | 0% | 2,263 | 1,564 | -31% | 0 | 0 | — |
case-12 | fail→pass | 27,475 | 7,728 | -72% | 1 | 1 | 0% | 1,757 | 1,786 | +2% | 0 | 0 | — |
case-13 | pass→pass | 13,995 | 9,040 | -35% | 1 | 1 | 0% | 2,215 | 2,141 | -3% | 0 | 0 | — |
case-14 | pass→pass | 13,751 | 9,807 | -29% | 1 | 1 | 0% | 1,905 | 1,924 | +1% | 0 | 0 | — |
case-15 | pass→pass | 11,180 | 4,721 | -58% | 1 | 1 | 0% | 1,803 | 1,608 | -11% | 0 | 0 | — |
case-16 | pass→pass | 5,901 | 3,567 | -40% | 1 | 1 | 0% | 1,074 | 1,396 | +30% | 0 | 0 | — |
case-17 | fail→pass | 21,243 | 8,773 | -59% | 1 | 1 | 0% | 1,322 | 1,480 | +12% | 0 | 0 | — |
case-18 | fail→pass | 9,539 | 46,352 | +386% | 1 | 1 | 0% | 1,381 | 1,392 | +1% | 0 | 0 | — |
case-19 | fail→pass | 29,374 | 2,923 | -90% | 1 | 1 | 0% | 2,380 | 1,261 | -47% | 0 | 0 | — |
case-20 | fail→pass | 10,720 | 4,783 | -55% | 1 | 1 | 0% | 1,619 | 1,557 | -4% | 0 | 0 | — |
case-21 | fail→pass | 21,311 | 5,044 | -76% | 1 | 1 | 0% | 1,740 | 1,429 | -18% | 0 | 0 | — |
case-22 | pass→fail | 28,002 | 31,020 | +11% | 1 | 1 | 0% | 1,517 | 1,347 | -11% | 0 | 0 | — |
case-23 | fail→pass | 38,016 | 30,832 | -19% | 1 | 1 | 0% | 1,223 | 1,883 | +54% | 0 | 0 | — |
case-24 | fail→pass | 24,158 | 11,786 | -51% | 1 | 1 | 0% | 2,084 | 1,789 | -14% | 0 | 0 | — |
case-25 | fail→pass | 516,351 | 12,014 | -98% | 1 | 1 | 0% | 2,367 | 1,727 | -27% | 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 23 counted toward the lift figure. The other 2 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 +60 percentage points is the difference between those two pass rates over the 23 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.
Other measured skills in the registry, with their headline benchmark lift.