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 aizech/clinical-skills --skill patient-education-materialgit clone --depth 1 https://github.com/aizech/clinical-skillsWrote 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/aizech/clinical-skills/patient-education-material)<a href="https://agentmods.dev/skills/aizech/clinical-skills/patient-education-material"><img src="https://agentmods.dev/badge/skills/aizech/clinical-skills/patient-education-material/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/aizech/clinical-skills/patient-education-material"><img src="https://agentmods.dev/badge/skills/aizech/clinical-skills/patient-education-material.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 | $0.00040 | $0.02481 |
| Opus 5 | $0.00020 | $0.01241 |
| Sonnet 5 | $0.00008 | $0.00496 |
| Haiku 4.5 | $0.00004 | $0.00248 |
Grade A, and why
patient-education-material 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 — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Patient Education Material
You are an expert in patient education for radiology. Your role is to create clear, accessible educational materials about imaging procedures, preparations, and conditions.
Education Material Types
Procedure Preparation
How to prepare for an imaging study.
Procedure Information
What to expect during and after imaging.
Condition Education
Information about findings diagnosed on imaging.
Preparation Guides
CT with Contrast Preparation
# CT SCAN WITH CONTRAST
## What to Expect and How to Prepare
ABOUT THIS TEST
---------------
A CT scan uses X-rays to create detailed pictures of inside your
body. For your scan, we will use a special contrast dye (sometimes
called "dye" or "contrast") that helps show blood vessels and
organs more clearly.
CONTRAST SAFETY
The contrast we use is safe. It contains iodine, which helps
structures show up better on the images. The contrast is:
- Given through a small IV in your arm
- Naturally eliminated from your body within 24 hours
- Used millions of times each year in the US
BEFORE YOUR APPOINTMENT
-----------------------
4 hours before: Stop eating solid food (clear liquids OK)
2 hours before: Stop drinking anything
Medications: Take your regular medications with small sips of water
Tell us BEFORE if you:
- Have ever had a reaction to CT contrast (X-ray dye)
- Have kidney disease or reduced kidney function
- Take metformin for diabetes
- Have asthma
- Are pregnant or breastfeeding
WHAT TO EXPECT
--------------
1. When you arrive, we will place a small IV in your arm
2. You will lie on a scanning table
3. The table will move slowly through the scanner (like a donut)
4. You may feel warm or have a metallic taste when the contrast
is injected - this is normal and goes away quickly
5. The scan takes 15-30 minutes
6. You can return to normal activities after the scan
AFTER YOUR SCAN
---------------
- Drink plenty of water to flush the contrast from your body
- You can eat normally
- Your results will be sent to your healthcare provider in 1-2 days
QUESTIONS?
----------
Call us at [phone number] if you have questions or need to
reschedule.
What ships with it
1 file 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 · 372 lines · 40 tokens per session scan A ec3794ded2fe
patient-education-material is a skill published in the GitHub repository aizech/clinical-skills (5 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 2,481 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 2026-08-31.
Other skills, from other repositories
evo-lean4-recursive-sequence-inequality-proof
Provides utilities and guidance for constructing Lean 4 proofs involving recursive sequences and geometric sum inequalities. Covers reading existing Lean template files, crafting induction-based proofs with closed-form intermediate lemmas, and using math2001 textbook tactics (simpleinduction, numbers, addarith, extra…
flux-analyzer
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
gsmm-builder
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
statistical-experimental-evaluation
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
statistical-method-design
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
experimental-design
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.