Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Methodological quality and bias risk assessment of included studies using validated tools
.claude/skills/yogsoth-ai-quality-assessment-protocol/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -33% | 0% |
Systematically assess methodological quality and risk of bias for each included study using domain-appropriate validated tools.
Select the appropriate quality assessment tool based on study design.
| Study Design | Tool | Domains | |--------------|------|---------| | RCT | RoB 2.0 | Randomization, deviations, missing data, measurement, selection | | Non-randomized interventions | ROBINS-I | Confounding, selection, classification, deviations, missing, measurement, reporting | | Diagnostic accuracy | QUADAS-2 | Patient selection, index test, reference standard, flow/timing | | Prediction models | PROBAST | Participants, predictors, outcome, analysis | | Observational | Newcastle-Ottawa | Selection, comparability, outcome/exposure |
Decision: Match tool to study design. Use one tool consistently within a meta-analysis.
Apply the selected tool to each included study.
SOPs: risk-of-bias-assessment
Plan the summary presentation of quality assessments.
SOPs: sensitivity-analysis-design
Per execution of this tactic:
yamlquality_assessment: tool_used: [RoB2/ROBINS-I/QUADAS-2/PROBAST/NOS] assessments: - study_id: [identifier] domains: - domain: [name] judgment: [Low/Some Concerns/High] rationale: [supporting text] overall: [Low/Some Concerns/High] summary: low_risk_count: [N] some_concerns_count: [N] high_risk_count: [N] problematic_domains: [most common high-risk domains] sensitivity_groups: low_risk_only: [study list] excluding_high_risk: [study list]
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | risk-of-bias-assessment | Assess methodological bias using RoB2, PROBAST, or QUADAS-2 validated tools | | sensitivity-analysis-design | Design leave-one-out, influence diagnostics, subgroup analyses, and robustness checks |
<!-- 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-01 | fail→pass | 37,133 | 25,232 | -32% | 1 | 1 | 0% | 6,870 | 4,685 | -32% | 0 | 0 | — |
case-02 | fail→pass | 27,231 | 12,725 | -53% | 1 | 1 | 0% | 4,547 | 3,242 | -29% | 0 | 0 | — |
case-03 | fail→pass | 27,751 | 18,336 | -34% | 1 | 1 | 0% | 4,630 | 3,343 | -28% | 0 | 0 | — |
case-04 | fail→pass | 30,146 | 24,710 | -18% | 1 | 1 | 0% | 5,381 | 4,617 | -14% | 0 | 0 | — |
case-05 | fail→pass | 27,511 | 19,093 | -31% | 1 | 1 | 0% | 4,514 | 3,018 | -33% | 0 | 0 | — |
case-06 | fail→pass | 19,462 | 23,337 | +20% | 1 | 1 | 0% | 3,720 | 4,095 | +10% | 0 | 0 | — |
case-07 | pass→pass | 25,854 | 21,185 | -18% | 1 | 1 | 0% | 5,019 | 3,847 | -23% | 0 | 0 | — |
case-08 | pass→pass | 26,656 | 19,774 | -26% | 1 | 1 | 0% | 4,450 | 3,265 | -27% | 0 | 0 | — |
case-09 | pass→pass | 40,381 | 28,461 | -30% | 1 | 1 | 0% | 6,911 | 3,963 | -43% | 0 | 0 | — |
case-10 | fail→fail | 33,615 | 23,702 | -29% | 1 | 1 | 0% | 4,802 | 4,262 | -11% | 0 | 0 | — |
case-11 | pass→pass | 26,333 | 23,196 | -12% | 1 | 1 | 0% | 4,781 | 4,384 | -8% | 0 | 0 | — |
case-12 | fail→pass | 31,244 | 19,443 | -38% | 1 | 1 | 0% | 4,557 | 3,483 | -24% | 0 | 0 | — |
case-13 | fail→pass | 37,541 | 21,859 | -42% | 1 | 1 | 0% | 6,174 | 3,896 | -37% | 0 | 0 | — |
case-14 | fail→pass | 29,905 | 19,362 | -35% | 1 | 1 | 0% | 4,016 | 3,292 | -18% | 0 | 0 | — |
case-15 | pass→pass | 18,989 | 18,857 | -1% | 1 | 1 | 0% | 3,509 | 3,331 | -5% | 0 | 0 | — |
case-16 | fail→pass | 24,708 | 14,759 | -40% | 1 | 1 | 0% | 3,927 | 3,775 | -4% | 0 | 0 | — |
case-17 | fail→pass | 26,587 | 19,892 | -25% | 1 | 1 | 0% | 4,116 | 4,367 | +6% | 0 | 0 | — |
case-18 | fail→pass | 37,892 | 24,105 | -36% | 1 | 1 | 0% | 4,171 | 4,269 | +2% | 0 | 0 | — |
case-19 | pass→fail | 27,781 | 42,714 | +54% | 1 | 1 | 0% | 6,776 | 9,029 | +33% | 0 | 0 | — |
case-20 | pass→fail | 22,739 | 37,990 | +67% | 1 | 1 | 0% | 4,160 | 6,270 | +51% | 0 | 0 | — |
case-21 | pass→pass | 16,126 | 35,164 | +118% | 1 | 1 | 0% | 3,263 | 4,205 | +29% | 0 | 0 | — |
case-22 | pass→pass | 69,082 | 21,083 | -69% | 1 | 1 | 0% | 6,998 | 3,778 | -46% | 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 +45 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 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.