Artifact of the paper "Prompt Engineering for Scientific Workflow Development"

This replication package accompanies the paper Prompt Engineering for Scientific Workflow Development.

Large language models (LLMs) offer new opportunities to support scientific workflow development by translating natural-language descriptions into structured computational artefacts. However, workflow generation requires an LLM to interpret scientific objectives, identify analytical stages, select suitable tools, configure parameters, establish valid data dependencies, and follow the conventions of the target workflow system. Underspecified prompts may therefore produce incomplete analyses, inappropriate assumptions, invalid connections, or platform-incompatible representations. This paper presents a workflow-oriented structured prompt specification that explicitly represents scientific intent, data context, analytical requirements, tools and parameters, dataflow constraints, expected outputs, platform requirements, validation criteria, and unresolved information. We first identify six categories of information that workflow prompts should communicate and organise relevant prompting techniques into four groups: specification, decomposition and reasoning, grounding, and output control and refinement. We then apply the specification to ten expert-curated bioinformatics tasks spanning Galaxy and Nextflow. Independent assessment by two authors indicates that the generated artefacts satisfy requirements coverage, analytical completeness, tool or module suitability, parameter completeness, and uncertainty handling. Dataflow correctness and platform conformity are partially satisfied because two workflows require platform-specific revisions. These findings provide preliminary evidence that the specification can support the generation of coherent, implementation-oriented workflow artefacts while making missing assumptions visible. However, live execution testing and external workflow and domain-expert assessment remain necessary before deployment.