Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add majiang213/OpenClaw-MAS --skill healthcare-cdss-patternsgit clone --depth 1 https://github.com/majiang213/OpenClaw-MASWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/majiang213/openclaw-mas/healthcare-cdss-patterns)<a href="https://agentmods.dev/skills/majiang213/openclaw-mas/healthcare-cdss-patterns"><img src="https://agentmods.dev/badge/skills/majiang213/openclaw-mas/healthcare-cdss-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/majiang213/openclaw-mas/healthcare-cdss-patterns"><img src="https://agentmods.dev/badge/skills/majiang213/openclaw-mas/healthcare-cdss-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.02275 |
| Opus 5.5 | $0.00019 | $0.00910 |
| Sonnet 5.5 | $0.00009 | $0.00455 |
| Haiku 4.5 | $0.00005 | $0.00228 |
Grade A, and why
healthcare-cdss-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to healthcare-cdss-patterns — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Healthcare CDSS Development Patterns
Patterns for building Clinical Decision Support Systems that integrate into EMR workflows. CDSS modules are patient safety critical — zero tolerance for false negatives.
When to Use
- Implementing drug interaction checking
- Building dose validation engines
- Implementing clinical scoring systems (NEWS2, qSOFA, APACHE, GCS)
- Designing alert systems for abnormal clinical values
- Building medication order entry with safety checks
- Integrating lab result interpretation with clinical context
How It Works
The CDSS engine is a pure function library with zero side effects. Input clinical data, output alerts. This makes it fully testable.
Three primary modules:
checkInteractions(newDrug, currentMeds, allergies)— Checks a new drug against current medications and known allergies. Returns severity-sortedInteractionAlert[]. UsesDrugInteractionPairdata model.validateDose(drug, dose, route, weight, age, renalFunction)— Validates a prescribed dose against weight-based, age-adjusted, and renal-adjusted rules. ReturnsDoseValidationResult.calculateNEWS2(vitals)— National Early Warning Score 2 fromNEWS2Input. ReturnsNEWS2Resultwith total score, risk level, and escalation guidance.
EMR UI
↓ (user enters data)
CDSS Engine (pure functions, no side effects)
├── Drug Interaction Checker
├── Dose Validator
├── Clinical Scoring (NEWS2, qSOFA, etc.)
└── Alert Classifier
↓ (returns alerts)
EMR UI (displays alerts inline, blocks if critical)
Drug Interaction Checking
interface DrugInteractionPair {
drugA: string; // generic name
drugB: string; // generic name
severity: 'critical' | 'major' | 'minor';
mechanism: string;
clinicalEffect: string;
recommendation: string;
}
function checkInteractions(
newDrug: string,
currentMedications: string[],
allergyList: string[]
): InteractionAlert[] {
if (!newDrug) return [];
const alerts: InteractionAlert[] = [];
for (const current of currentMedications) {
const interaction = findInteraction(newDrug, current);
if (interaction) {
alerts.push({ severity: interaction.severity, pair: [newDrug, current],
message: interaction.clinicalEffect, recommendation: interaction.recommendation });
}
}
for (const allergy of allergyList) {
if (isCrossReactive(newDrug, allergy)) {
alerts.push({ severity: 'critical', pair: [newDrug, allergy],
message: `Cross-reactivity with documented allergy: ${allergy}`,
recommendation: 'Do not prescribe without allergy consultation' });
}
}
return alerts.sort((a, b) => severityOrder(a.severity) - severityOrder(b.severity));
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 246 lines · 47 tokens per session scan A 892b425b9ac7
healthcare-cdss-patterns is a skill published in the GitHub repository majiang213/OpenClaw-MAS (4 stars, last pushed 6mo ago), licensed MIT. It adds 47 tokens to every session and 2,275 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to healthcare-cdss-patterns, differing in 28 lines, and is treated as a copy.
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