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- hipaa readiness audit
Works with the AI tools you already use
hipaa readiness audit
Audits codebases for HIPAA Security Rule gaps, identifies PHI leaks, and maps BAA requirements.
$30
hipaa readiness audit
Example session with this skill installed
Audit our Node.js patient portal repo. We use OpenAI for summaries and Sentry for logging. Are we HIPAA ready?
- Read your context and instructions
- Compiled the hipaa readiness audit
Findings
- §164.312(b): Audit Gap. Sentry logs capture request bodies containing PHI. No evidence of read-access logging.
- §164.308(b)(1): BAA Risk. OpenAI flow lacks an executed BAA; data contains 3 of 18 HIPAA identifiers.
- §164.312(a): Addressable. Missing documentation for encryption-at-rest.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Developers often assume their stack is HIPAA compliant because they use AWS or Bitlocker. They lack a concrete map of how their specific code violates the HIPAA Security Rule or which vendors require a BAA.
What it does
- Scans codebases for ePHI-handling defects and maps them to specific HIPAA Security Rule citations.
- Identifies third-party vendors and AI providers that require a Business Associate Agreement (BAA).
- Finds missing audit controls, including failures to log record access (reads) rather than just writes.
- Detects ePHI leakage into client-side analytics, session replays, and unscrubbed error trackers.
- Generates severity-rated findings structured for OCR investigators or hospital security reviews.
Why this beats prompting it yourself
Generic LLM prompts often confuse proposed 2025 rules with current law or treat addressable requirements as optional. This skill uses a strict reference snapshot to ensure citations are accurate and distinguishes between code-level safeguards and organizational policies.
Use cases
- Preparing for a hospital CISO security review or vendor questionnaire.
- Auditing a healthtech repo to identify PHI exposure before a production launch.
- Generating evidence to feed an official 164.308(a)(1) risk analysis.
- Mapping AI usage to identify which LLM flows must stop due to missing BAAs.
Known limitations
Cannot verify physical safeguards (facility access) or administrative policies (employee training). Findings are unverified if repo access is not provided.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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- Passed all security checks, Safe to install