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Get Started Free →Analyse billing leakage — unbilled services, suboptimal coding, claims rejection patterns by payer/procedure/provider, time-to-collection bottlenecks, aged debt analysis, and estimate total revenue currently left on the table. Use monthly or when revenue doesn't match activity levels.
.claude/skills/myceldigital-revenue-integrity/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -7% | 0% |
You are the Revenue Cycle Forensic 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 context/CONTEXT.md for financial baseline.
Ask: "How many patient appointments were completed last month? How many were billed? Is there a gap?" The gap between appointments-completed and appointments-billed is your FIRST source of revenue leakage. Common causes:
Ask: "What procedure/service codes do you use most frequently? Are you using the most specific code available, or defaulting to general codes?" Analyse:
Ask: "What is your claims rejection rate by payer? What are the top rejection reasons?" For each payer:
Ask: "What is your average days-to-payment from date of service? How much is in your aged debt (30/60/90/120+ days)?" Analyse:
Calculate:
Total: this is the revenue currently on the table.
For each gap: specific action, owner, expected recovery, timeline. Rank by recovery-to-effort ratio. Quick wins first.
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-10 | pass→pass | 12,752 | 15,322 | +20% | 1 | 1 | 0% | 2,169 | 3,286 | +51% | 0 | 0 | — |
case-01 | fail→pass | 31,297 | 21,898 | -30% | 1 | 1 | 0% | 6,235 | 5,063 | -19% | 0 | 0 | — |
case-02 | fail→pass | 22,550 | 26,656 | +18% | 1 | 1 | 0% | 4,205 | 5,984 | +42% | 0 | 0 | — |
case-03 | fail→pass | 34,725 | 25,617 | -26% | 1 | 1 | 0% | 6,220 | 5,613 | -10% | 0 | 0 | — |
case-04 | pass→pass | 11,300 | 11,921 | +5% | 1 | 1 | 0% | 1,959 | 2,882 | +47% | 0 | 0 | — |
case-05 | fail→pass | 12,221 | 13,819 | +13% | 1 | 1 | 0% | 2,081 | 3,281 | +58% | 0 | 0 | — |
case-06 | pass→pass | 13,385 | 19,377 | +45% | 1 | 1 | 0% | 2,277 | 3,851 | +69% | 0 | 0 | — |
case-07 | fail→pass | 12,390 | 6,234 | -50% | 1 | 1 | 0% | 2,091 | 1,946 | -7% | 0 | 0 | — |
case-08 | fail→pass | 16,586 | 12,751 | -23% | 1 | 1 | 0% | 2,887 | 2,984 | +3% | 0 | 0 | — |
case-09 | pass→pass | 11,213 | 12,264 | +9% | 1 | 1 | 0% | 2,010 | 3,121 | +55% | 0 | 0 | — |
case-11 | pass→pass | 13,306 | 16,139 | +21% | 1 | 1 | 0% | 2,167 | 3,613 | +67% | 0 | 0 | — |
case-12 | pass→pass | 10,142 | 10,213 | +1% | 1 | 1 | 0% | 1,827 | 2,645 | +45% | 0 | 0 | — |
case-13 | pass→pass | 13,306 | 11,839 | -11% | 1 | 1 | 0% | 2,149 | 2,794 | +30% | 0 | 0 | — |
case-14 | fail→fail | 10,447 | 1,797 | -83% | 1 | 1 | 0% | 1,716 | 1,170 | -32% | 0 | 0 | — |
case-15 | pass→pass | 6,216 | 1,407 | -77% | 1 | 1 | 0% | 999 | 1,116 | +12% | 0 | 0 | — |
case-16 | fail→pass | 9,460 | 3,749 | -60% | 1 | 1 | 0% | 1,420 | 1,472 | +4% | 0 | 0 | — |
case-17 | fail→pass | 9,935 | 3,513 | -65% | 1 | 1 | 0% | 1,497 | 1,439 | -4% | 0 | 0 | — |
case-18 | fail→pass | 9,271 | 4,121 | -56% | 1 | 1 | 0% | 1,405 | 1,349 | -4% | 0 | 0 | — |
case-19 | fail→pass | 10,188 | 4,968 | -51% | 1 | 1 | 0% | 1,678 | 1,527 | -9% | 0 | 0 | — |
case-20 | fail→pass | 20,189 | 17,629 | -13% | 1 | 1 | 0% | 3,821 | 3,665 | -4% | 0 | 0 | — |
case-21 | fail→pass | 20,980 | 16,195 | -23% | 1 | 1 | 0% | 3,844 | 3,120 | -19% | 0 | 0 | — |
case-22 | pass→pass | 11,990 | 8,384 | -30% | 1 | 1 | 0% | 2,077 | 2,347 | +13% | 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 +55 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.