Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aks129/HealthClawGuardrailsnpx agentmods add skills/aks129/healthclawguardrails/personal-health-recordsWrote 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/aks129/healthclawguardrails/personal-health-records)<a href="https://agentmods.dev/skills/aks129/healthclawguardrails/personal-health-records"><img src="https://agentmods.dev/badge/skills/aks129/healthclawguardrails/personal-health-records/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/aks129/healthclawguardrails/personal-health-records"><img src="https://agentmods.dev/badge/skills/aks129/healthclawguardrails/personal-health-records.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 72 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 433 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Tool Misuse · line 475 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Data Exfiltration · line 158 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00111 | $0.04172 |
| Opus 5 | $0.00056 | $0.02086 |
| Sonnet 5 | $0.00022 | $0.00834 |
| Haiku 4.5 | $0.00011 | $0.00417 |
Grade A, and why
personal-health-records scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:5000/r6/fhir/health How it starts
The opening of the file, as written. The whole thing — 517 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Personal health records — HealthEx + HealthClaw
Connect your health records from any US health system, pull your complete clinical history, analyze it with Claude, and optionally store it in a personal FHIR data store with data quality curation.
Standards: All resources use FHIR R4 with US Core v6.1 profiles — the production standard used by US health systems. The /r6/fhir URL path in HealthClaw is a legacy route prefix from the project's experimental R6 ballot resource support; the actual clinical data (Conditions, Observations, Immunizations, etc.) is R4 and validated against US Core required fields.
Setup (do this once)
1. Connect HealthEx in Claude.ai
- Go to claude.ai → Settings → Integrations
- Find HealthEx and click Connect
- Authorize with your HealthEx account (create one free at healthex.io if needed)
- In HealthEx, connect your health systems: Epic, Cerner, CommonWell, Carequality, and most major US health networks are supported
- Return to Claude — the HealthEx tools are now active in this session
To verify: ask Claude "Check when my health records were last updated" — it will call HealthEx:update_and_check_recent_records and confirm your connection.
2. Optional: Connect HealthClaw for local FHIR store
If you want to store, curate, and own a local de-identified copy of your records:
# Clone the HealthClaw Guardrails repo
git clone https://github.com/aks129/HealthClawGuardrails
cd HealthClawGuardrails
# Start the local stack (Flask guardrail proxy + MCP server + Redis)
cp .env.example .env # edit with your STEP_UP_SECRET and ANTHROPIC_API_KEY
docker-compose up -d --build
# Confirm healthy
curl http://localhost:5000/r6/fhir/health
Add to .mcp.json in the repo root so Claude Code picks it up:
{
"mcpServers": {
"healthclaw-local": {
"type": "http",
"url": "http://localhost:3001/mcp",
"headers": { "X-Tenant-ID": "my-health" }
}
}
}
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 · 517 lines · 111 tokens per session scan A c17580128c50
personal-health-records is a skill published in the GitHub repository aks129/HealthClawGuardrails (30 stars, last pushed today), licensed MIT. It adds 111 tokens to every session and 4,172 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
healthcare-fhir
Design RESTful clinical data exchanges using HL7 FHIR standards.
lab-report-walkthrough
Walk a person through their lab report — read the original document, organize results by panel, flag out-of-range values against the printed reference ranges, compare with their history, and explain in plain language. Use when the user uploads a lab report (PDF/image) or asks what their blood test results mean.
healthcare-expert
Expert-level healthcare systems, medical informatics, HIPAA compliance, and health data standards. Use when the user mentions medical, HIPAA, HL7, FHIR, or EHR, or when the task involves Healthcare IT, Standards and Protocols, Regulatory Compliance, or Security and Compliance.
hl7gen
Use when generating, validating, or converting HL7 v2 healthcare messages, or converting HL7 v2 to FHIR. Covers synthetic test-message generation for any of 185 HL7 v2 message types across versions 2.1–2.8.2, message validation, FHIR R5/R4B conversion, and structure exploration (required/optional/repeating fields).…
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
deploy-docker-compose
Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…