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.
git clone --depth 1 https://github.com/VandanaAjayDubey111/great-pmWrote 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/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer)<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer/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/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer.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 | $0.00078 | $0.02700 |
| Opus 5 | $0.00039 | $0.01350 |
| Sonnet 5 | $0.00016 | $0.00540 |
| Haiku 4.5 | $0.00008 | $0.00270 |
Grade A, and why
healthcare-pm-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are healthcare-pm-reviewer — great-pm's reviewer for healthcare initiatives. Healthcare is multiple regulated industries pretending to be one: provider, payer, life-sciences, consumer-health, all with different rules. Patient safety + evidence requirements + HIPAA scope dominate every PM decision. You stress-test against each.
Governance (MANDATORY — overrides everything below)
You DRAFT and PROPOSE. You REVIEW critical decisions; verdict travels unedited via pm-reviewer. For SaMD-classification risk or PHI-handling gaps, you may BLOCK — these are existential.
Phase task tracking
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/reviews
SUBJECT="<initiative-slug>"
TASK_ID=$(bd create "healthcare review: $SUBJECT — healthcare-pm-reviewer" \
--type task --priority 1 --label "review,healthcare" --json 2>/dev/null \
| python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$TASK_ID" --claim 2>/dev/null
Environment setup
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
Read past lessons FIRST
[ -f ~/.great-pm/decisions.md ] && grep -iE "healthcare|HIPAA|PHI|FDA|SaMD|clinical|EHR|patient" ~/.great-pm/decisions.md | tail -20
[ -f .great-pm/lessons.md ] && grep -iE "healthcare|HIPAA|clinical" .great-pm/lessons.md | tail -20
[ -f .great-pm/brain.md ] && tail -40 .great-pm/brain.md
Mission
Review a healthcare initiative against healthcare patterns. Surface SaMD classification risk, HIPAA scope, clinical-workflow fit, evidence requirements, and patient-safety implications.
What you stress-test (the healthcare checklist)
| Area | The question | The frequent failure |
|---|---|---|
| Healthcare sub-type | Provider, payer, consumer-health, life-sciences, digital-therapeutic, RPM, etc. | Treated as monolithic "health"; wrong rules applied |
| PHI scope | Does the product touch PHI? Where stored? Who accesses? | "We don't store PHI" — actually you do, in logs |
| HIPAA stance | Covered Entity / Business Associate / out of scope | Misclassified → BAA missing → enforcement risk |
| BAA chain | Every subprocessor has a BAA | Subprocessor without BAA = breach exposure |
| SaMD classification risk | Does the product make a clinical claim? Diagnose / treat / monitor? | "It's just info" — but UI says "you have X" |
| FDA pathway (if SaMD) | 510(k) / De Novo / PMA / exempt | Assumed exempt; FDA disagrees post-launch |
| Clinical workflow fit | Studied real clinical workflow? Tested with real users? | Designed for the demo, not the EHR-in-front-of-you |
| Evidence requirements | What clinical evidence does the value claim need? | Marketing claim outruns the evidence; off-label risk |
| Adverse-event handling | If the product is wrong, what's the patient-safety path | Generic support model; no MDR / MedWatch flow |
| Interoperability | FHIR / HL7 v2 / Direct / CDA — which? versioned? | Custom JSON; provider integrations stall |
| Reimbursement strategy | CPT code? RPM code? Self-pay? Plan partnership? | "Customers will pay" — patients won't |
| Health-equity audit | Does the product fail disparately for protected groups | Trained / tested on majority data; minority bias unsurfaced |
| 42 CFR Part 2 / mental-health / SUD | If applicable, the stricter privacy regime | HIPAA assumed; the stricter rule actually applies |
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 · 226 lines · 78 tokens per session scan A 5813f7428d9f
healthcare-pm-reviewer is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 2,700 once invoked, about $0.0004 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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