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 aks129/HealthClawGuardrails --skill fhir-r6-guardrailsgit clone --depth 1 https://github.com/aks129/HealthClawGuardrailsWrote 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/fhir-r6-guardrails)<a href="https://agentmods.dev/skills/aks129/healthclawguardrails/fhir-r6-guardrails"><img src="https://agentmods.dev/badge/skills/aks129/healthclawguardrails/fhir-r6-guardrails/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/fhir-r6-guardrails"><img src="https://agentmods.dev/badge/skills/aks129/healthclawguardrails/fhir-r6-guardrails.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00132 | $0.01472 |
| Opus 5 | $0.00066 | $0.00736 |
| Sonnet 5 | $0.00026 | $0.00294 |
| Haiku 4.5 | $0.00013 | $0.00147 |
Grade A, and why
fhir-r6-guardrails 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 6d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HealthClaw Guardrails
A healthclaw.io open source project. Reference implementation of security and compliance patterns for AI agent access to FHIR data via MCP.
Supports FHIR R4 US Core v9 (stable) and FHIR R6 v6.0.0-ballot3 (experimental).
This is a runtime guardrail layer, not a knowledge skill. It sits between any AI agent and FHIR data (local or upstream), enforcing PHI redaction, audit trails, step-up authorization, and tenant isolation on every request.
When to Use This Skill
- You need to read, search, or write FHIR clinical resources through MCP
- You need PHI to be automatically redacted before the agent sees it
- You need an immutable audit trail of all agent access
- You need step-up authorization gates on write operations
- You need to evaluate R6 Permission resources for access control
MCP Tools Available (12)
Read Tools (no step-up required)
| Tool | Purpose |
|---|---|
context.get |
Retrieve a pre-built context envelope with patient-centric resources |
fhir.read |
Read a single FHIR resource by type and ID (auto-redacted) |
fhir.search |
Search resources with patient, code, status, date filters |
fhir.validate |
Structural validation of a proposed resource |
fhir.stats |
Observation statistics: count, min, max, mean over valueQuantity |
fhir.lastn |
Most recent N observations per code |
fhir.permission_evaluate |
Evaluate R6 Permission for permit/deny with reasoning |
fhir.subscription_topics |
List available SubscriptionTopics |
curatr.evaluate |
Evaluate a FHIR resource for data quality issues |
Write Tools (require step-up token)
| Tool | Purpose |
|---|---|
fhir.propose_write |
Validate and preview a write without committing |
fhir.commit_write |
Commit a proposed write (requires X-Step-Up-Token) |
curatr.apply_fix |
Apply patient-approved data quality fixes with Provenance |
Two-Phase Write Pattern
Writes always follow propose-then-commit:
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.
- 6d ago Changed b5c0e8e1d56f
- 12d ago First seen · 152 lines · 132 tokens per session scan A be34a24972a2
fhir-r6-guardrails is a skill published in the GitHub repository aks129/HealthClawGuardrails (30 stars, last pushed today), licensed MIT. It adds 132 tokens to every session and 1,472 once invoked, about $0.0007 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 2026-08-30.
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