Menu
Search IWC fixtures and public sources for test data matching a data-flow shape.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Carry an existing Galaxy workflow's tests forward as a regression baseline and augment them for a change-set's deltas, emitting a Galaxy test plan.
Triage failing Galaxy run outputs; classify the failure surface and capture evidence before recommending repairs.
Synthesize a Galaxy workflow test plan from a free-form summary and the Galaxy design briefs.
Assemble Galaxy workflow test fixtures and assertions.
Execute a workflow's tests via Planemo; emit structured pass/fail and outputs.
Convert one nf-core module dir into a Galaxy tool wrapper (tool.xml + macros.xml + _provenance.yml + remote-URL <test> blocks).
| name | find-test-data |
| description | Search IWC fixtures and public sources for test data matching a data-flow shape. |
Follow the procedure below and use the artifact/reference sections as the runtime contract.
freeform-summary. Produced by interview-to-freeform-summary, summarize-paper. Source summary from summarize-paper / interview-to-freeform-summary; mine its sample-data, public-data-candidate, accession, and data-sizing guidance — this is where the source's dataset evidence lives (the design briefs strip it).summary-nextflow. Schema: summary-nextflow. Produced by summarize-nextflow. Source summary from summarize-nextflow; mine its test_fixtures / sample-data evidence when running the NEXTFLOW → GALAXY pipeline.summary-cwl. Schema: summary-cwl. Produced by summarize-cwl. Source summary from summarize-cwl; mine its test-data / sample-data evidence when running the CWL → GALAXY pipeline.freeform-galaxy-interface. Produced by freeform-summary-to-galaxy-interface. Galaxy interface brief from freeform-summary-to-galaxy-interface pinning input labels, collection shapes, and datatypes for the PAPER / INTERVIEW → GALAXY pipelines.nextflow-galaxy-interface. Produced by nextflow-summary-to-galaxy-interface. Galaxy interface brief from nextflow-summary-to-galaxy-interface pinning input labels, collection shapes, and datatypes for the NEXTFLOW → GALAXY pipeline.cwl-galaxy-interface. Produced by cwl-summary-to-galaxy-interface. Galaxy interface brief from cwl-summary-to-galaxy-interface pinning input labels, collection shapes, and datatypes for the CWL → GALAXY pipeline.test-data-refs as test-data-refs.json. Format: json. Test data matched from IWC fixtures or public sources, expressed as URLs/paths plus expected shapes for downstream test authoring.references/notes/galaxy-workflow-testability-design.md: Research note copied verbatim into the bundle. Confirm the collection shapes and labels the resolved data must populate so downstream tests can address them. Use when: mapping an input label in the data-flow brief to a concrete file or collection of files.references/notes/iwc-test-data-conventions.md: Research note copied verbatim into the bundle. Match IWC test-data conventions — Zenodo/remote-URL first, SHA-1 integrity, per-input collection layout — when selecting or describing candidate fixtures. Use when: choosing where test data should come from or describing the shape a candidate file must have.Resolve concrete test data for the workflow's inputs. Read the interface brief for each input's Galaxy shape and datatype, and the source summary for the data the source itself names — then search IWC fixtures and public sources for data that matches. Emit test-data-refs.json: one entry per input, each carrying a URL or path plus the expected shape, ready for implement-galaxy-workflow-test to stage.
This skill is the first leg of the harness's test-data-resolution branch. It resolves what it can and reports gaps; the harness routes any unresolved input to the user-supplied fallthrough. Deciding to ask the user is a harness concern, not this skill's — its job is an honest, source-backed match.
freeform-summary / summary-nextflow / summary-cwl) is where the source names its data: sample-data locations, accessions, public-data candidates, fallback bundles, and sizing guidance ("one chromosome", "precomputed count matrix", "small subset"). Pull every candidate dataset and every data-sizing instruction the source gives.-tests.yml is the strongest source. Otherwise resolve the right-shape public dataset found in step 3, sized for a fast test run.test-data-refs.json entry per input: the URL/path, the expected Galaxy shape, datatype, element identifiers when it is a collection, integrity hash when known, and any subset/split prep. Per galaxy-workflow-testability-design, make sure each entry maps to an addressable input label.resolved: false with a reason only when steps 2–5 turn up no real source of the right shape — not merely because the source's first-named candidate was the wrong shape or because a real source needs a documented subset. These honest gaps are what the harness hands to user-supplied.Never invent a URL, accession, or path to make an input look resolved, and never emit a placeholder path (sampleA.tabular, test-data/…) for an input you could not resolve — an unresolved input stays resolved: false all the way through to the test, never papered over with a made-up path downstream. A wrong-but-plausible fixture reference is worse than an honest gap: it survives static checks and fails only at run time, far from this skill. Every emitted ref must point at data that exists (a real source, optionally plus a reproducible subset/split); everything else is a reported gap.