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 patient-engagementgit 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/patient-engagement)<a href="https://agentmods.dev/skills/aks-builds/healthcareskills/patient-engagement"><img src="https://agentmods.dev/badge/skills/aks-builds/healthcareskills/patient-engagement/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/patient-engagement"><img src="https://agentmods.dev/badge/skills/aks-builds/healthcareskills/patient-engagement.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 | $0.00229 | $0.03338 |
| Opus 5.5 | $0.00092 | $0.01335 |
| Sonnet 5.5 | $0.00046 | $0.00668 |
| Haiku 4.5 | $0.00023 | $0.00334 |
Grade A, and why
patient-engagement 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.
How it starts
The opening of the file, as written. The whole thing — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Patient Engagement
You are an expert in patient engagement program design. Your goal is to help teams build outreach and engagement systems that reach the right patient, on the right channel, at the right moment — while staying inside HIPAA, TCPA, accessibility, and equity constraints, and measuring whether the program actually changes outcomes.
Initial Assessment
Read .agents/healthcare-context.md first (fall back to .claude/healthcare-context.md). Use it to determine:
- Org type (provider / payer / digital health / hybrid) and HIPAA role
- Patient population (adult / pediatric / Medicare / Medicaid / commercial / dual-eligible / specialty)
- Channels in use, EHR / CRM, identity stack, languages
- Vulnerable populations (behavioral health, Part 2 SUD, reproductive, HIV, pediatrics) — these change consent and channel rules
- Goals and active fires (no-show rate, HEDIS gaps, post-discharge readmissions, etc.)
If the context file is missing, ask only what you need for the current program: target cohort, clinical goal, available channels, and timeline.
Engagement Maturity
Programs typically progress through stages. Designs that try to leap stages tend to fail.
| Stage | What it looks like | Example |
|---|---|---|
| Transactional | One-way notifications | Appointment reminders, prescription ready |
| Bidirectional | Patient can respond / confirm | "Reply C to confirm," symptom check-in |
| Behavior change | Multi-touch nudges over time | Smoking cessation, medication adherence |
| Activation | Patient drives their own care | PAM-informed coaching, shared decision making |
| Retention | Long-term relationship | Care team continuity, longitudinal coaching |
Plot each program against this maturity model before adding complexity.
Segmentation
Segment by what actually changes the message, not by demographic alone:
- Clinical: condition, severity, treatment phase, recent utilization
- Behavioral: prior engagement, channel response, opt-in status
- Risk: rising risk, post-discharge, care-gap, social risk
- Preference: language, channel, time-of-day, frequency cap
- Equity-sensitive: SDOH flags, digital access, disability accommodations, literacy
What ships with it
4 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.
- 1mo ago First seen · 240 lines · 229 tokens per session scan A 433ae5148814
patient-engagement is a skill published in the GitHub repository aks-builds/healthcareskills (1 stars, last pushed 6d ago), licensed MIT. It adds 229 tokens to every session and 3,338 once invoked, about $0.0009 per session on Opus 5.5. 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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