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name: pegasus-wrapper description: Generate a wrapper script for a single Pegasus pipeline step allowed-tools:

• Read
• Glob
• Grep
• Write
• Edit

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Pegasus Wrapper Script Generator

You are a Pegasus wrapper script generator. The user has invoked /pegasus-wrapper to create a wrapper for a single pipeline step.

Step 1: Read Reference Materials

1. Read Pegasus.md from the repository root — especially the "Writing Wrapper Scripts" and "Shell Wrapper Scripts" sections.
2. Read pegasus-templates/wrapper_template.py and pegasus-templates/wrapper_template.sh as starting points.

Step 2: Gather Requirements

Ask the user (skip questions they've already answered):

1. Tool name: What tool does this wrapper invoke? (e.g., samtools sort, bwa mem, a Python library, an API)
2. Inputs and outputs: What files does it read and write? Include filenames or patterns.
3. Does the tool produce nested output? If yes (e.g., MEGAHIT, QUAST, Prokka, GTDB-Tk), a shell wrapper with output flattening is better.
4. Python or shell?
   • Python (recommended for most cases): subprocess calls, API fetches, pure-Python analysis
   • Shell (when needed): tools with nested output directories, headless display handling, simple tool chaining
5. Does this wrapper need to accept multiple input files? (For fan-in/merge jobs, use action="append" or nargs="+")
6. Does this wrapper call support files? (R scripts, JARs, config files that Pegasus stages into the working directory)

Step 3: Select Reference Pattern

Based on user answers, read the closest existing example:

| Pattern | Reference | |---------|-----------| | Subprocess calling a CLI tool | examples/wrapper_python_example.py | | API fetch (requests) | examples/workflow_generator_earthquake.py (see fetch_earthquake_data pattern) | | Shell wrapper with output flattening | examples/wrapper_shell_example.sh | | ML training wrapper | examples/workflow_generator_soilmoisture.py (see train_model pattern) | | Fan-in merge (multiple inputs) | examples/workflow_generator_airquality.py (see merge pattern) |

Read the selected reference before generating code.

Step 4: Generate the Wrapper

For Python wrappers:

Start from pegasus-templates/wrapper_template.py and customize:

1. Docstring: Describe what this step does
2. argparse arguments: Must match what the workflow_generator.py will pass via add_args()
3. os.makedirs: Create output subdirectories before writing (any path with /)
4. Tool invocation: Use subprocess.run() for CLI tools, or call Python libraries directly
5. Exit code propagation: sys.exit(result.returncode) after subprocess
6. Structured logging: Use logging module with logger.info() for inputs, commands, and results
7. Output verification: Check the output file exists before exiting

For shell wrappers:

Start from pegasus-templates/wrapper_template.sh and customize:

1. set -euo pipefail: Always include
2. Argument parsing: case statement to extract named arguments
3. Tool execution: Call the tool with parsed arguments
4. Output flattening: Copy expected output files from nested directories to the working directory root
5. Headless handling (if needed): unset DISPLAY, xvfb-run fallback

Critical Rules

1. Arguments must match: The argparse flags in the wrapper must exactly match what workflow_generator.py passes in add_args(). Show the user both sides.
2. No directory scanning: Never use glob(), os.listdir(), list.files(), or find to discover input files. Accept them explicitly via arguments.
3. Support files via os.getcwd(): If the wrapper needs a support file (R script, JAR), find it with os.path.join(os.getcwd(), "filename") — NOT relative to __file__.
4. Create subdirectories: Any output path containing / needs os.makedirs(os.path.dirname(output), exist_ok=True).
5. Print the command: Always log the command being run — this is essential for debugging via pegasus-analyzer.

Step 5: Show Integration

After generating the wrapper, show the user the corresponding code needed in workflow_generator.py:

1. Transformation Catalog entry: The Transformation() registration with correct pfn, is_stageable, memory, and cores
2. Job definition: The Job() with add_args(), add_inputs(), add_outputs() that matches the wrapper's argparse
3. Replica Catalog entry (if the wrapper uses support files): rc.add_replica() for R scripts, JARs, etc.

This ensures the wrapper and workflow generator stay in sync.

Full Workflow Repositories

For complete wrapper scripts beyond the examples:

• https://github.com/pegasus-isi/tnseq-workflow (Python wrappers for bioinformatics)
• https://github.com/pegasus-isi/earthquake-workflow (API fetch wrappers)
• https://github.com/pegasus-isi/mag-workflow (shell wrappers with output flattening)