galaxy-automation

name	galaxy-automation
description	BioBlend and Planemo expertise for Galaxy workflow automation. Galaxy API usage, workflow invocation, status checking, error handling, batch processing, and dataset management. Essential for any Galaxy automation project.
version	1.0.0
dependencies	bioblend, planemo

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

2. Concurrent uploads

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

3. Dataset state checking

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

4. History name conflicts

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

5. Return code interpretation

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

6. Invocation ID recovery

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

7. CRITICAL: Admin API key sees ALL users' data

   • get_invocations() without filters returns EVERY user's invocations, not just yours
   • NEVER cancel/delete/modify invocations based on broad queries (e.g., "recent and still running")
   • Always use specific invocation IDs from your own tool output (planemo prints Invocation <hex_id>)
   • Before any destructive action, verify the history/invocation owner matches your user
   • If you must query broadly, filter by a history ID you own
   • Cancelled invocations are IRRECOVERABLE — there is no undo

8. Planemo test invocation tracking

   • Planemo prints invocation IDs during test runs: Invocation <52bc9f6134abd589>
   • When cancelling orphaned invocations from killed planemo tests, use ONLY these IDs
   • Do NOT scan all server invocations and guess which are yours based on timing

---

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 "$GXYVGP"   # Galaxy instance URL
echo "$TESTKEY"   # API key for testing
echo "$MAINKEY"   # Admin API key (only for admin tasks, NEVER for testing)

Then pass the resolved values:

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

IMPORTANT: Use $TESTKEY for all testing and workflow runs. $MAINKEY is an admin key — it can see and modify ALL users' data. Only use $MAINKEY when admin access is specifically needed.

Known Instances

Env Var	Instance	Notes
$GXYVGP	https://vgp.usegalaxy.org	VGP production, user: delphinel (admin)
$TESTKEY	Testing API key for VGP	Use this for all planemo tests and workflow runs
$MAINKEY	Admin API key for VGP	Admin tasks only — sees ALL users' data, NEVER use for testing

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Browser Automation with browser-use

When using browser-use to automate Galaxy UI interactions:

LLM Setup

• browser-use 0.12+ has its own ChatAnthropic wrapper — use browser_use.llm.anthropic.chat.ChatAnthropic, NOT langchain_anthropic.ChatAnthropic. The langchain version lacks a provider property that browser-use requires.
• The API key must be passed explicitly: ChatAnthropic(model="claude-sonnet-4-20250514", api_key=os.environ["ANTHROPIC_API_KEY"])

Browser Connection

• Use CDP connection to a user-managed browser: BrowserSession(cdp_url="http://localhost:9222")
• Launch Chrome with: chrome --remote-debugging-port=9222
• Auto-login via cookie injection does NOT work reliably with Galaxy's session handling — let the user log in manually before connecting
• Playwright's record_video_dir is NOT available since browser-use uses CDP directly, not Playwright contexts. Use ffmpeg screen capture instead.

Galaxy UI Agent Tips

• Galaxy has social media icons near toolbar buttons — agent may misclick LinkedIn/Twitter instead of Upload
• Add hover-to-verify instructions: "Before clicking any element, hover over it first and wait 2 seconds for the tooltip to appear"
• Add deliberate pacing instructions for video-quality recordings
• "Create history" steps should be handled via bioblend API before the agent starts, then skipped in the browser
• File uploads via the browser Upload dialog work better than bioblend's upload_file() for Zenodo URLs

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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)

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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/