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Prior auth coworker

Skill BioTender-max/awesome-bio-agent-skills/skills/openclaw/prior-auth-coworker

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.From the repository description

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill prior-auth-coworker

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

2.0 KB, 511 tokens by cl100k_base, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # 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 -->

What ships with it: 5 files

16.2 KB alongside SKILL.md, 3 of them executable

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