team-galaxy 0.2.1

Galaxy Project extensions for the team multi-agent LLM framework.

team-galaxy

Galaxy Project extensions for the team multi-agent LLM framework.

team-galaxy adds Galaxy-specific skills, personas, and ready-to-run scenario configs on top of team-core. Installing team-galaxy is all it takes — no environment variables or hardcoded paths required. It integrates with team-core ≥ 0.15.5 via Python entry points:

Entry-point group	What it contributes
team.skills	Short skill names (bioblend, planemo, …) usable anywhere in a YAML
team.persona_dirs	Galaxy personas auto-discoverable with the @name shorthand
team.commands	team galaxy subcommand injected into the team CLI

[!NOTE]

A significant portion of this code and documentation was written with the assistance of GitHub Copilot. All contributions have been reviewed and tested by the maintainers.

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Scenarios

Scenario	Description	Workflow type
tool-wrapper-factory	Generate an IUC-compliant Galaxy tool XML wrapper from a --help text	review_loop
iwc-compliance	Bring a Galaxy workflow to full IWC quality-gate compliance	sequential_chain
gtn-tutorial-generator	Convert a .ga workflow into a complete GTN tutorial	review_loop
bioblend-analysis	Run a multi-step bioinformatics analysis via the Galaxy API	manager
job-failure-investigator	Diagnose failed Galaxy jobs and produce actionable reports	sequential_chain

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Installation

Basic (planemo scenarios, no Galaxy API)

pip install team-galaxy

Planemo must be installed separately and available on PATH:

pip install planemo

With BioBlend (Galaxy API scenarios)

pip install "team-galaxy[bioblend]"

Set your Galaxy credentials:

export GALAXY_URL=https://usegalaxy.org
export GALAXY_API_KEY=<your_api_key>

From source

git clone https://github.com/cumbof/team-galaxy
cd team-galaxy
pip install -e ".[dev,bioblend]"

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Quick Start

1. Initialise a scenario

team galaxy init --scenario tool-wrapper-factory

This copies a ready-to-edit YAML config to the current directory. Skill references use short registered names — no paths to edit.

2. Prepare your workspace

Each scenario expects one or two input files in the workspace directory. The generated YAML contains a # Before running: comment describing exactly what to provide.

For the tool-wrapper-factory scenario, create tool_spec.md in the workspace:

# Tool: fastp

conda package: fastp
version: 0.23.4

## --help output

fastp: an ultra-fast all-in-one FASTQ preprocessor
...

## Intended Galaxy datatypes
- Input: fastqsanger, fastqsanger.gz (paired or single)
- Output: fastqsanger.gz (trimmed reads), html (QC report), json (QC metrics)

3. Run

team run tool-wrapper-factory.yaml

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Skills

After pip install team-galaxy, use skill names directly in any team YAML:

defaults:
  skills:
    - bioblend        # Galaxy API tools (requires bioblend extra)
    - planemo         # planemo lint / test / autoupdate tools
    - toolshed        # Tool Shed search and metadata tools
    - iuc_standards   # IUC authoring standards (context injection)
    - iwc_checklist   # IWC workflow quality checklist (context injection)
    - gtn_format      # GTN tutorial format specification (context injection)
    - bioconda_guide  # Bioconda packaging guide (context injection)

Available skills

Name	Type	Provides
bioblend	Python tools	galaxy_upload, galaxy_run_tool, galaxy_invoke_workflow, galaxy_job_status, galaxy_wait_for_job, galaxy_download, galaxy_search_tools, galaxy_create_history, galaxy_get_histories, galaxy_show_dataset
planemo	Python tools	planemo_lint, planemo_test, planemo_workflow_lint, planemo_workflow_test, planemo_autoupdate, planemo_shed_lint
toolshed	Python tools	toolshed_search, toolshed_tool_info, toolshed_categories, toolshed_owner_repos
iuc_standards	Markdown context	IUC tool XML authoring standards injected into system prompt
iwc_checklist	Markdown context	IWC workflow quality checklist injected into system prompt
gtn_format	Markdown context	GTN tutorial Markdown format specification
bioconda_guide	Markdown context	Bioconda recipe packaging guide

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Personas

Galaxy-specific personas are automatically discovered when team-galaxy is installed. No TEAM_PERSONA_DIR needed.

Key	Role	Description
@iuc_reviewer	IUC Tool Reviewer	Expert in Galaxy tool XML, planemo, and IUC contribution standards
@iwc_curator	IWC Workflow Curator	Expert in IWC workflow quality standards and compliance validation
@gtn_author	GTN Tutorial Author	Expert in writing GTN tutorials in the GTN Markdown format
@galaxy_admin	Galaxy System Administrator	Manages Galaxy instance infrastructure and job routing
@tool_wrapper_author	Galaxy Tool Wrapper Author	Expert in writing IUC-compliant Galaxy tool XML wrappers

Reference them in any team YAML:

members:
  - name: reviewer
    persona: "@iuc_reviewer"

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How it extends team-core

team-galaxy uses the three entry-point groups added in team-core ≥ 0.15.5:

team-core plugin API
├── team.skills          ← bioblend, planemo, toolshed, iuc_standards, …
├── team.persona_dirs    ← team_galaxy/personas/ (auto-merged)
└── team.commands        ← team galaxy <subcommand>

This means:

• Skill names work in any team YAML, not just team-galaxy ones.
• Personas are available via @name without any env var.
• team galaxy --help is always accessible once team-galaxy is installed.

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CLI reference

team galaxy --help

Commands:
  init       Copy a scenario template to a directory, ready to run.
  scenarios  List available scenario templates.
  skills     List available Galaxy skill names and descriptions.
  personas   List available Galaxy personas with descriptions.

# List scenarios
team galaxy scenarios

# Initialise a scenario in the current directory
team galaxy init --scenario bioblend-analysis

# Initialise in a specific directory
team galaxy init --scenario tool-wrapper-factory --output-dir ~/my-project/

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Development

pip install -e ".[dev,bioblend]"
pytest -q

Integration tests (require a live Galaxy server and planemo):

pytest -m integration

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Contributing

Pull requests are welcome. Please disclose AI assistance in the PR description (e.g. "co-authored with GitHub Copilot"). Before submitting:

• Run pytest -q and ensure all tests pass.
• Add or update tests for any behaviour changes.
• Follow the conventions described in AGENTS.md.