Workflow Clinic

Workflow Clinic is a GSoC 2026 project focused on improving the portability, reproducibility, and cloud-readiness of scientific workflows.

The project aims to analyze workflow languages such as Nextflow and Snakemake, convert them into a common intermediate representation called WorkflowBundle, and identify workflow portability issues through automated validation and analysis.

By using a common workflow model inspired by the DAW (Data Analysis Workflow) metamodel, Workflow Clinic can reason about workflows independently of their original language and provide consistent diagnostics, recommendations, and future repair capabilities.

Why Workflow Clinic?

Scientific workflows are often tightly coupled to specific execution environments, storage systems, schedulers, or local infrastructure.

This can make workflows difficult to:

• Share
• Reproduce
• Port across platforms
• Execute in cloud environments
• Integrate with GA4GH-compliant services

Workflow Clinic aims to help workflow authors identify and resolve these issues before deployment.

Planned Features

Workflow Parsing

• Nextflow support
• Snakemake support
• Common WorkflowBundle representation

Workflow Analysis

• Portability diagnostics
• Storage validation
• Resource validation
• Metadata validation
• Workflow structure validation

AI-Assisted Review

• Rule-based workflow checks
• AI-assisted diagnostics
• Confidence-based recommendations

Workflow Repair

• Suggested fixes
• Automated transformations
• Validation of generated fixes

Installation

Clone the Repository

git clone https://github.com/revaarathore11/ga4gh_workflow_clinic_gsoc_2026-.git
cd ga4gh_workflow_clinic_gsoc_2026-

Create a Virtual Environment

python -m venv .venv
source .venv/bin/activate

Install Dependencies

pip install -e ".[dev]"

Development

Run Tests

pytest

Run Linting

ruff check .

Run Formatting

ruff format .

AI Critic & Remediation Guidance (AI-Assisted Review)

Workflow Clinic includes an AI Critic Agent to enrich diagnostic findings with AI-powered, cloud-readiness remediation advice.

Enabling LLM Remediation

To generate AI remediation advice, run the examine command with the --enhance (-e) flag:

workflow-clinic examine main.nf --enhance

🔑 Bring Your Own Key & Model (BYOK & BYOM)

You can configure any supported LiteLLM model (e.g., OpenAI, Gemini, Anthropic, Groq, Mistral) by setting the respective environment variable.

Configuration via .env file (Recommended)

Create a .env file in your working directory to permanently save configuration details:

OPENAI_API_KEY="sk-proj-..."
# CLINIC_MODEL="gpt-4o"  # Optional: Overrides the auto-detected default model

Model Auto-Detection: The AI Critic automatically resolves the appropriate default model based on which API key is present in your environment (e.g., OPENAI_API_KEY defaults to gpt-4o-mini). To view all supported providers and their default models, run:

workflow-clinic list-models

CLI Options

You can temporarily override the default model directly on the command line:

workflow-clinic examine main.nf --enhance --model gpt-4o --api-key sk-proj-...

[!WARNING] Security Notice: Avoid passing explicit --api-key arguments in shared or public environments as they can leak into your shell history (history) or process listings (ps aux). Using environment variables or a .env file is the highly recommended security practice.

Supported Workflow Languages

Current target languages:

• Nextflow
• Snakemake

Potential future support:

• CWL
• WDL

Architecture Overview

Workflow Files
    ↓
  Parser
    ↓
WorkflowBundle
    ↓
Rule Engine
    ↓
 AI Critic
    ↓
  Doctor

Standards Alignment

Workflow Clinic is being designed with future compatibility in mind for:

• GA4GH TES
• GA4GH WES
• GA4GH TRS
• Workflow Run RO-Crate

License

This project is licensed under the Apache License 2.0.