OpenClaw-Medical-Skills prior-auth-coworker

<!--

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.md
source 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

  1. Policy Matching: Checks against specific criteria (e.g., "Pain > 6 weeks").
  2. Trace Generation: Produces an "Anthropic-style"
    <thinking>
    trace for auditability.
  3. Structured Output: Returns a JSON object with decision, reasoning, and timestamps.

Workflow

  1. Extract Data: Parse the clinical note and procedure code from the user's input.
  2. Execute Review: Run the coworker script.
  3. 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)
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->