install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/prior-auth-coworker" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-prior-auth-coworker && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/prior-auth-coworker" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-prior-auth-coworker && rm -rf "$T"
manifest:
skills/prior-auth-coworker/SKILL.mdsource content
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
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"
trace for auditability.<thinking> - 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
(Lumbar Spine MRI)MRI-L-SPINE