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-emr-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-emr-patterns)<a href="https://agentmods.dev/skills/ufy2024/auc/healthcare-emr-patterns"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/healthcare-emr-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-emr-patterns"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/healthcare-emr-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 | $0.00040 | $0.01526 |
| Opus 5 | $0.00020 | $0.00763 |
| Sonnet 5 | $0.00008 | $0.00305 |
| Haiku 4.5 | $0.00004 | $0.00153 |
Grade A, and why
healthcare-emr-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 8d 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-emr-patterns — 91% identical, 28 lines differ
- healthcare-emr-patterns — 91% identical, 28 lines differ
- healthcare-emr-patterns — 91% identical, 28 lines differ
- healthcare-emr-patterns — 88% identical, 29 lines differ
- healthcare-emr-patterns — 88% identical, 29 lines differ
- healthcare-emr-patterns — 88% identical, 29 lines differ
- healthcare-emr-patterns — 88% identical, 29 lines differ
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Healthcare EMR Development Patterns
Patterns for building Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems. Prioritizes patient safety, clinical accuracy, and practitioner efficiency.
When to Use
- Building patient encounter workflows (complaint, exam, diagnosis, prescription)
- Implementing clinical note-taking (structured + free text + voice-to-text)
- Designing prescription/medication modules with drug interaction checking
- Integrating Clinical Decision Support Systems (CDSS)
- Building lab result displays with reference range highlighting
- Implementing audit trails for clinical data
- Designing healthcare-accessible UIs for clinical data entry
How It Works
Patient Safety First
Every design decision must be evaluated against: "Could this harm a patient?"
- Drug interactions MUST alert, not silently pass
- Abnormal lab values MUST be visually flagged
- Critical vitals MUST trigger escalation workflows
- No clinical data modification without audit trail
Single-Page Encounter Flow
Clinical encounters should flow vertically on a single page — no tab switching:
Patient Header (sticky — always visible)
├── Demographics, allergies, active medications
│
Encounter Flow (vertical scroll)
├── 1. Chief Complaint (structured templates + free text)
├── 2. History of Present Illness
├── 3. Physical Examination (system-wise)
├── 4. Vitals (auto-trigger clinical scoring)
├── 5. Diagnosis (ICD-10/SNOMED search)
├── 6. Medications (drug DB + interaction check)
├── 7. Investigations (lab/radiology orders)
├── 8. Plan & Follow-up
└── 9. Sign / Lock / Print
Smart Template System
interface ClinicalTemplate {
id: string;
name: string; // e.g., "Chest Pain"
chips: string[]; // clickable symptom chips
requiredFields: string[]; // mandatory data points
redFlags: string[]; // triggers non-dismissable alert
icdSuggestions: string[]; // pre-mapped diagnosis codes
}
Red flags in any template must trigger a visible, non-dismissable alert — NOT a toast notification.
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.
- 8d ago First seen · 180 lines · 40 tokens per session scan A d2d7bfb9b076
healthcare-emr-patterns is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 1,526 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…