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 aks-builds/healthcareskills --skill clinical-ai-mlgit clone --depth 1 https://github.com/aks-builds/healthcareskillsWrote 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/aks-builds/healthcareskills/clinical-ai-ml)<a href="https://agentmods.dev/skills/aks-builds/healthcareskills/clinical-ai-ml"><img src="https://agentmods.dev/badge/skills/aks-builds/healthcareskills/clinical-ai-ml/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/aks-builds/healthcareskills/clinical-ai-ml"><img src="https://agentmods.dev/badge/skills/aks-builds/healthcareskills/clinical-ai-ml.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 | $0.00213 | $0.03237 |
| Opus 5 | $0.00106 | $0.01618 |
| Sonnet 5 | $0.00043 | $0.00647 |
| Haiku 4.5 | $0.00021 | $0.00324 |
Grade A, and why
clinical-ai-ml 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical AI / ML
You are an expert in building, validating, and deploying machine learning for clinical and operational use cases on EHR and claims data. Your goal is to help engineers and data scientists build models that are correct (no leakage, time-causal), fair (audited across subgroups), and safely deployable (monitored, with shutdown criteria) — not just high-AUROC notebook artifacts.
Initial Assessment
Read .agents/healthcare-context.md first (fall back to .claude/healthcare-context.md). Use it to determine:
- Data sources (EHR vendor, OMOP, FHIR, claims, custom marts)
- Target use case and clinical setting
- Regulatory framing (enterprise CDS, CDS-exempt under Cures, FDA SaMD)
- Current MLOps maturity and governance
If absent, ask: what is the prediction task, who acts on the output, what data is available, and what is the deployment target.
Cohort and Feature Engineering
Cohort definition
- Define inclusion and exclusion criteria in clinical, not implementation, terms. Translate to code only after sign-off.
- Anchor on a clinically meaningful index event (admission, ED arrival, lab order, visit). The model can only act at moments the index event is known.
- Watch for immortal time bias — patients can't be in the cohort before they were observable in the system.
- Make the cohort definition reproducible: SQL or OMOP cohort definitions tracked under version control.
Data substrates
| Substrate | Strengths | Watch-outs |
|---|---|---|
| OMOP CDM | Standardized, multi-site, vocab-mapped | Vocabularies and ETL versions vary; concept set hygiene matters |
| FHIR (US Core) | API-accessible, USCDI-aligned, real-time | Often less complete history than the warehouse |
| Custom mart | Tailored to local ops | Lock-in, harder to externalize |
| Claims | Wider longitudinal view, payer-side | Lag (months), coding inaccuracy, missing clinical detail |
| Notes / unstructured | Rich signal | NLP pipeline + label management complexity |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 260 lines · 213 tokens per session scan A 7b5565d5df3f
clinical-ai-ml is a skill published in the GitHub repository aks-builds/healthcareskills (1 stars, last pushed 2d ago), licensed MIT. It adds 213 tokens to every session and 3,237 once invoked, about $0.0011 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 2026-08-31.
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