Workflow Engine Mapper

Goal

Choose the smallest workflow structure that preserves reproducibility, restartability, and provenance for a materials simulation task.

Requirements

• Python 3.10+
• No external dependencies
• Works on Linux, macOS, and Windows

Inputs to Gather

Decision Guidance

• Use one-off scripts for fewer than 5 local exploratory runs.
• Use jobflow/atomate2 when the workflow is Python-native and Materials Project style input sets are useful.
• Use AiiDA when provenance, database-backed records, and remote execution are central.
• Use pyiron when interactive atomistic workflows, notebooks, and job management are the primary user surface.

Script Outputs

scripts/workflow_engine_mapper.py emits:

• recommended_engine
• dag_pattern
• provenance_requirements
• restart_strategy
• storage_layout
• migration_triggers

Workflow

python3 skills/simulation-workflow/workflow-engine-mapper/scripts/workflow_engine_mapper.py \
  --task "relax static dos for 200 oxides" \
  --code vasp \
  --runs 200 \
  --needs-provenance \
  --needs-restart \
  --hpc \
  --json

Use the output to scaffold the workflow before writing engine-specific code.

Error Handling

If the task has too few details, choose the conservative pattern and ask for engine, run count, and restart needs before implementation.

Limitations

The skill does not replace the official APIs of atomate2, jobflow, AiiDA, or pyiron; it selects and explains the workflow shape.

Security

• The script accepts only scalar CLI inputs and booleans.
• It does not connect to remote services or submit jobs.
• The skill uses Bash only to run the bundled script.

References

• See references/workflow_engines.md for engine selection heuristics.

Version History

• 1.0.0: Initial workflow engine mapping skill.