galaxy-automation（银河自动化）

Galaxy Workflow Automation with BioBlend and Planemo

Purpose

This skill provides expert knowledge for automating Galaxy workflows using BioBlend (Python Galaxy API library) and Planemo (Galaxy workflow testing and execution tool).

When to Use This Skill

Use this skill when:

• Automating Galaxy workflow execution via API
• Building batch processing systems for Galaxy
• Using BioBlend to interact with Galaxy
• Testing workflows with Planemo
• Managing Galaxy histories, datasets, and collections programmatically
• Polling workflow invocation status
• Implementing error handling and retry logic for Galaxy operations
• Creating Galaxy automation pipelines
• Integrating Galaxy into larger bioinformatics workflows

This skill is NOT project-specific - it's useful for ANY Galaxy automation project.

Supporting Files

Detailed reference material is split into separate files:

• bioblend-reference.md -- BioBlend API patterns: connection, history management, workflow invocation, status checking, error handling, rerun API, dataset operations, and collections
• planemo-reference.md -- Planemo command structure, job YAML format, programmatic command generation, output parsing, and Galaxy API curl/authentication patterns
• automation-patterns.md -- Thread-safe operations, batch processing, resume capability, and debugging (history inspection, invocation step analysis)

Security Best Practices

1. API Key Management

Store in environment variables:

import os

api_key = os.environ.get('GALAXY_API_KEY')
if not api_key:
    raise ValueError("GALAXY_API_KEY environment variable not set")

gi = GalaxyInstance(url, api_key)

Mask in logs:

def mask_api_key(key):
    """Mask API key for display"""
    if len(key) <= 8:
        return '*' * len(key)
    return f"{key[:4]}{'*' * (len(key) - 8)}{key[-4:]}"

masked_key = mask_api_key(api_key)
print(f"Using API key: {masked_key}")

2. Path Handling

Always quote paths in shell commands:

# Good - handles spaces
command = f'planemo run "{workflow_path}" "{job_yaml}"'

# Bad - breaks with spaces
command = f'planemo run {workflow_path} {job_yaml}'

Common Pitfalls

1. Planemo failures vs Galaxy failures

- Planemo return code!= 0: Workflow was NOT launched, no invocation exists - Invocation state = 'failed': Workflow was launched but Galaxy job failed - Don't confuse these two failure modes

1. Concurrent uploads

- Too many simultaneous uploads can overwhelm Galaxy - Use max_concurrent limits (typically 3-5) - Consider --simultaneous_uploads vs sequential

1. Dataset state checking

- Don't invoke workflows before uploads complete - Always wait for dataset state = 'ok'

1. History name conflicts

- Use unique history names (add timestamps or suffixes) - Check for existing histories before creating

1. Return code interpretation

- os.system() shifts exit codes (exit 1 -> return 256) - Use return_code >> 8 to get actual exit code

1. Invocation ID recovery

- Terminal disconnection loses invocation ID - Always save invocation IDs to file immediately - Use --test_output_json with planemo

Best Practices Summary

1. Use environment variables for API keys
2. Mask API keys in logs and output
3. Quote all file paths in shell commands
4. Implement thread-safety for concurrent operations
5. Save state frequently for resume capability
6. Wait for dataset uploads before invoking workflows
7. Poll invocation status with reasonable intervals (30-60s)
8. Distinguish planemo failures from Galaxy failures
9. Implement proper error handling and retry logic
10. Use unique history names to avoid conflicts

Galaxy MCP Connection

When using the Galaxy MCP tools (mcp__Galaxy__*), connect at the start of each session.

Connection Pattern

MCP tools cannot read shell environment variables directly. Resolve them via Bash first:

# Resolve env vars
echo "$GXYVGP"   # Galaxy instance URL
echo "$MAINKEY"   # API key

Then pass the resolved values:

mcp__Galaxy__connect(url="<resolved_url>", api_key="<resolved_key>")

Known Instances

Related Skills

• galaxy-tool-wrapping: For creating Galaxy tool wrappers
• galaxy-workflow-development: For creating Galaxy workflows
• vgp-pipeline: VGP-specific orchestration (uses this skill as dependency)

Resources

• BioBlend Documentation: https://bioblend.readthedocs.io/
• Planemo Documentation: https://planemo.readthedocs.io/
• Galaxy API: https://docs.galaxyproject.org/en/master/api/
• Galaxy Training: https://training.galaxyproject.org/