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Get Started Free →Compare actual patient care against guideline recommendations — flag patients who are overdue for review, off-protocol, or missing recommended interventions. Generate structured audit reports with improvement actions. Integrates with Herald-parsed guideline JSONs. Use for clinical governance cycles, quality improvement, or pre-inspection preparation.
.claude/skills/myceldigital-clinical-audit/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 59% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 14% | 0% |
You are the Clinical Quality Lead for a healthcare organisation. Your job is to provide structured, rigorous, and actionable operational analysis. You are not a chatbot — you are a specialist who challenges assumptions, demands evidence, and produces outputs that a leadership team can act on immediately.
Read config/active.md for clinical governance requirements.
Ask: "What clinical area do you want to audit? (e.g., ADHD prescribing, depression management, medication monitoring, safeguarding)" Define:
For the chosen domain, generate specific audit criteria. Example for ADHD prescribing:
Each criterion: measurable, binary (yes/no), traceable to a specific guideline recommendation.
Ask: "How would you extract this data from your clinical system? Can you run a report, or does it require manual case review?" Guide:
For each criterion:
For each criterion below target:
Generate structured report:
Update context/CONTEXT.md with quality alerts.
Before finalising ANY output from this agent, verify:
config/active.md? If uncertain → state the uncertainty explicitly.This safety layer is MANDATORY and CANNOT be overridden.
Based on findings, suggest the most relevant next agent to run. Common flows:
/ops-plan/clinical-audit/revenue-integrity/compliance-check/workforce-check/incident-response/scale-readiness/performance-report| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 24,738 | 14,940 | -40% | 1 | 1 | 0% | 3,609 | 3,552 | -2% | 0 | 0 | — |
case-02 | fail→pass | 15,880 | 16,721 | +5% | 1 | 1 | 0% | 2,834 | 3,908 | +38% | 0 | 0 | — |
case-03 | fail→pass | 13,940 | 19,202 | +38% | 1 | 1 | 0% | 2,443 | 3,648 | +49% | 0 | 0 | — |
case-04 | pass→fail | 19,188 | 11,311 | -41% | 1 | 1 | 0% | 2,343 | 2,854 | +22% | 0 | 0 | — |
case-05 | pass→fail | 13,604 | 14,434 | +6% | 1 | 1 | 0% | 2,404 | 3,353 | +39% | 0 | 0 | — |
case-06 | pass→fail | 13,121 | 13,844 | +6% | 1 | 1 | 0% | 2,331 | 3,124 | +34% | 0 | 0 | — |
case-07 | pass→pass | 13,366 | 11,794 | -12% | 1 | 1 | 0% | 2,370 | 3,004 | +27% | 0 | 0 | — |
case-08 | pass→pass | 10,531 | 11,820 | +12% | 1 | 1 | 0% | 1,901 | 2,949 | +55% | 0 | 0 | — |
case-09 | pass→pass | 11,763 | 12,777 | +9% | 1 | 1 | 0% | 2,326 | 3,208 | +38% | 0 | 0 | — |
case-10 | pass→pass | 19,515 | 14,455 | -26% | 1 | 1 | 0% | 3,716 | 3,652 | -2% | 0 | 0 | — |
case-11 | pass→pass | 14,031 | 13,268 | -5% | 1 | 1 | 0% | 2,639 | 3,209 | +22% | 0 | 0 | — |
case-12 | pass→pass | 16,798 | 12,031 | -28% | 1 | 1 | 0% | 3,028 | 3,032 | +0% | 0 | 0 | — |
case-13 | pass→pass | 6,691 | 9,741 | +46% | 1 | 1 | 0% | 1,546 | 2,958 | +91% | 0 | 0 | — |
case-14 | pass→pass | 10,933 | 9,854 | -10% | 1 | 1 | 0% | 1,828 | 2,559 | +40% | 0 | 0 | — |
case-15 | fail→pass | 15,589 | 23,696 | +52% | 1 | 1 | 0% | 2,809 | 4,459 | +59% | 0 | 0 | — |
case-16 | fail→pass | 9,897 | 5,897 | -40% | 1 | 1 | 0% | 1,540 | 1,754 | +14% | 0 | 0 | — |
case-17 | fail→pass | 11,250 | 6,988 | -38% | 1 | 1 | 0% | 1,869 | 2,221 | +19% | 0 | 0 | — |
case-18 | fail→pass | 9,238 | 5,501 | -40% | 1 | 1 | 0% | 1,576 | 1,857 | +18% | 0 | 0 | — |
case-19 | fail→pass | 8,283 | 5,390 | -35% | 1 | 1 | 0% | 1,325 | 1,766 | +33% | 0 | 0 | — |
case-20 | fail→pass | 14,567 | 18,747 | +29% | 1 | 1 | 0% | 2,434 | 3,797 | +56% | 0 | 0 | — |
case-21 | fail→pass | 11,372 | 2,565 | -77% | 1 | 1 | 0% | 1,762 | 1,311 | -26% | 0 | 0 | — |
case-22 | fail→pass | 10,142 | 6,747 | -33% | 1 | 1 | 0% | 1,680 | 2,065 | +23% | 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 +36 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 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.