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bio-foundation-housekeepinglisted

Add schema-backed metadata validation, normalized Parquet tables, and a DuckDB catalog to a bioinformatics project. Use when an analysis needs LinkML/Pydantic records or a queryable data catalog.
fmschulz/omics-skills · ★ 7 · AI & Automation · score 64
Install: claude install-skill fmschulz/omics-skills
# Bio Foundation Housekeeping Add validated metadata models and a queryable catalog to an existing bioinformatics project. This is an independent entry point when the layout already exists; if it does not, complete `bioinformatics-project` as a separate setup task. ## Instructions 1. Confirm `bioinformatics-project` has established input/output boundaries, project records, and a pinned environment. Do not create a competing project layout. 2. Adapt `schemas/project-metadata.yaml` for the sample, run, file, result, and provenance records the project needs. Keep identifiers stable and declare types, required fields, enumerations, and patterns in the schema. 3. Generate Pydantic models with `scripts/generate_models.py`. The command pins LinkML and Pydantic, rejects changed outputs, imports the generated module, and can assert that expected classes exist. 4. Validate the complete metadata bundle with the generated `MetadataBundle` model. Reject unexpected fields and malformed types before checking relationships. 5. Check unique identifiers and foreign keys across record collections before writing outputs. At minimum, verify run-to-sample, file-to-run, result-to-input/output-file, and provenance-to-result links. 6. Normalize validated records into one Parquet table per record class plus bridge tables for multivalued relationships. Register the tables and their relative paths, row counts, and SHA-256 values in DuckDB. 7. Exercise the full boundary with `scripts/build_metadata_ca