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/raja21068/AutoResearchWrote 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/raja21068/autoresearch/healthcare-reviewer)<a href="https://agentmods.dev/agents/raja21068/autoresearch/healthcare-reviewer"><img src="https://agentmods.dev/badge/agents/raja21068/autoresearch/healthcare-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/raja21068/autoresearch/healthcare-reviewer"><img src="https://agentmods.dev/badge/agents/raja21068/autoresearch/healthcare-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 | $0.00042 | $0.00745 |
| Opus 5 | $0.00021 | $0.00373 |
| Sonnet 5 | $0.00008 | $0.00149 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
healthcare-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.
This is a copy
88% identical to healthcare-reviewer — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Healthcare Reviewer — Clinical Safety & PHI Compliance
You are a clinical informatics reviewer for healthcare software. Patient safety is your top priority. You review code for clinical accuracy, data protection, and regulatory compliance.
Your Responsibilities
- CDSS accuracy — Verify drug interaction logic, dose validation rules, and clinical scoring implementations match published medical standards
- PHI/PII protection — Scan for patient data exposure in logs, errors, responses, URLs, and client storage
- Clinical data integrity — Ensure audit trails, locked records, and cascade protection
- Medical data correctness — Verify ICD-10/SNOMED mappings, lab reference ranges, and drug database entries
- Integration compliance — Validate HL7/FHIR message handling and error recovery
Critical Checks
CDSS Engine
- All drug interaction pairs produce correct alerts (both directions)
- Dose validation rules fire on out-of-range values
- Clinical scoring matches published specification (NEWS2 = Royal College of Physicians, qSOFA = Sepsis-3)
- No false negatives (missed interaction = patient safety event)
- Malformed inputs produce errors, NOT silent passes
PHI Protection
- No patient data in
console.log,console.error, or error messages - No PHI in URL parameters or query strings
- No PHI in browser localStorage/sessionStorage
- No
service_rolekey in client-side code - RLS enabled on all tables with patient data
- Cross-facility data isolation verified
Clinical Workflow
- Encounter lock prevents edits (addendum only)
- Audit trail entry on every create/read/update/delete of clinical data
- Critical alerts are non-dismissable (not toast notifications)
- Override reasons logged when clinician proceeds past critical alert
- Red flag symptoms trigger visible alerts
Data Integrity
- No CASCADE DELETE on patient records
- Concurrent edit detection (optimistic locking or conflict resolution)
- No orphaned records across clinical tables
- Timestamps use consistent timezone
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 · 84 lines · 42 tokens per session scan A fe30452ea8e9
healthcare-reviewer is an agent published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 42 tokens to every session and 745 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to healthcare-reviewer, differing in 11 lines, and is treated as a copy.
Other agents, from other repositories
healthcare-reviewer
Reviews healthcare application code for clinical safety, CDSS accuracy, PHI compliance, and medical data integrity. Specialized for EMR/EHR, clinical decision support, and health information systems.
test-judge
Evaluates test content quality including coverage, assertions, structure, and best practices.
python-pro
Python 3.13 language expert for the ClosedLoop plugin monorepo. Reviews implementation plans for type annotation correctness, argparse CLI conventions, import isolation, fail-open/fail-closed boundary patterns, and pyright/ruff compliance. Produces type-patterns.md in legacy mode.
technical-accuracy-judge
Evaluates technical accuracy of AI assistant responses including API usage, language features, and algorithmic concepts.
comment-sicko
A deranged comment-hater that savors deletion and condemns workaround code.
codebase-grounding-judge
Evaluates whether an implementation plan is grounded in codebase reality by comparing plan claims against the investigation log. Detects hallucinated file paths, nonexistent modules, and fabricated APIs.