prior-auth-coworker
SkillDev toolsThis skill acts as an automated utilization management reviewer. It takes unstructured clinical notes and a procedure code, compares them against internal policy criteria (e.g., conservative therapy failure), and renders a decision.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the prior-auth-coworker skill
About this skill
The largest open-source medical AI skills library for OpenClaw🦞.
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/prior-auth-coworker/SKILL.md and read by ahel’s review.
name: 'prior-auth-coworker' description: 'Prior Auth Review' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Prior Authorization Coworker
This skill acts as an automated utilization management reviewer. It takes unstructured clinical notes and a procedure code, compares them against internal policy criteria (e.g., conservative therapy failure), and renders a decision.
When to Use This Skill
- When a user asks to "review a prior auth request".
- When checking if a patient qualifies for a specific procedure (e.g., MRI).
- When you need to generate a structured approval/denial letter justification.
Core Capabilities
- Policy Matching: Checks against specific criteria (e.g., "Pain > 6 weeks").
- Trace Generation: Produces an "Anthropic-style"
<thinking>trace for auditability. - Structured Output: Returns a JSON object with decision, reasoning, and timestamps.
Workflow
- Extract Data: Parse the clinical note and procedure code from the user's input.
- Execute Review: Run the coworker script.
- Present Decision: Output the JSON decision and the reasoning trace.
Example Usage
User: "Check if this patient qualifies for an MRI of the Lumbar Spine: Patient has had back pain for 2 months, tried PT but it didn't work."
Agent Action:
python3 Skills/Clinical/Prior_Authorization/anthropic_coworker.py --code "MRI-L-SPINE" --note "Patient has back pain > 2 months. Failed PT."
Supported Policies
MRI-L-SPINE(Lumbar Spine MRI)
Signals
- GitHub stars
- 3k
- Forks
- 412
- Last commit
- Jul 2026
Advanced
- Item type
- skill
- Key
prior-auth-coworker- Source
- github.com/freedomintelligence/openclaw-medical-skills
github.com/freedomintelligence/openclaw-medical-skills
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