AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 ufy2024/AuC --skill healthcare-cdss-patternsgit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/healthcare-cdss-patterns)<a href="https://agentmods.dev/skills/ufy2024/auc/healthcare-cdss-patterns"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/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/ufy2024/auc/healthcare-cdss-patterns"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/healthcare-cdss-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 21 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.02358 |
| Opus 5 | $0.00023 | $0.01179 |
| Sonnet 5 | $0.00009 | $0.00472 |
| Haiku 4.5 | $0.00005 | $0.00236 |
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 7d 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.
Copies of this mod
7 near-identical copies found in the catalogue:
- healthcare-cdss-patterns — 91% identical, 29 lines differ
- healthcare-cdss-patterns — 91% identical, 29 lines differ
- healthcare-cdss-patterns — 91% identical, 29 lines differ
- healthcare-cdss-patterns — 89% identical, 28 lines differ
- healthcare-cdss-patterns — 89% identical, 28 lines differ
- healthcare-cdss-patterns — 89% identical, 28 lines differ
- healthcare-cdss-patterns — 88% identical, 29 lines differ
How it starts
The opening of the file, as written. The whole thing — 266 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.
- 7d ago First seen · 266 lines · 47 tokens per session scan A 579161236f2e
healthcare-cdss-patterns is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 2,358 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
i4h-catheter-navigation-e2e
End-to-end smoke for catheter navigation covering setup, digital twin, DRR, and unit tests. Use when asked to run the full catheter workflow smoke or demo the v0.7 pipeline.
paperlab_equation_dimensional_audit
Audit equations, dimensions, symbols, notation, and formula variants in PaperLab books. Use when technical chapters contain equations, units, and engineering correlations that must remain consistent across chapters.
paperlab_derivation_symbolic_checking
Check PaperLab mathematical derivations for assumptions, dimensional consistency, limiting cases, notation, and NeqSim implementation linkage.
template-formal
Strongly-typed multiagent ant-robot colony exemplar — ADTs, session-typed protocols, affine-discipline resource handles, storage-as-functor framing, Active-Inference-flavored decision loop, mypy-as-oracle negative controls.
template-code-project
Code-centric optimization research exemplar — gradient descent, convergence analysis, automated figures, dashboard, and publication-quality PDF.
healthcare-cdss-patterns
Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows. Use when building clinical decision support — drug interaction checks, dose validation, clinical scoring, or…