Content
61%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is highly actionable with broad executable examples and a genuine one-level-deep reference, but it is verbose and its batch/FTP workflows lack the validation checkpoints the rubric requires. Tightening duplicated code and adding explicit validation steps would raise both conciseness and workflow clarity.
Suggestions
Add explicit validation/verification checkpoints to batch and FTP workflows (e.g. verify downloaded files exist and are non-empty before processing; confirm rate-limit sleeps succeed).
Trim duplicated install snippets and repeated GEOparse.get_GEO boilerplate, and move the full DE-analysis/clustering/meta-analysis code into references/geo_reference.md.
Cut the 'Key Concepts' explanations of MIAME/SOFT/MINiML to one-line definitions, since Claude already knows these formats.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The ~800-line body is mostly actionable code rather than prose padding, but it is noticeably verbose: repeated GEOparse.get_GEO calls, duplicated install snippets, and 'Key Concepts' blocks explaining MIAME/SOFT/MINiML that Claude largely already knows. | 3 / 5 |
Actionability | Extensive copy-paste-ready Python covers search, retrieval, FTP download, QC, differential expression, clustering, and batch processing, with only minor gaps (placeholder GSM IDs, unverified pivot_samples/VALUE assumptions). | 4 / 5 |
Workflow Clarity | Capabilities are loosely sequenced but lack explicit validation checkpoints, and batch/bulk operations (batch_download_geo, batch_fetch_geo_metadata, FTP bulk wget -r) run without validate-then-proceed feedback loops, which caps workflow_clarity at 3 per the rubric. | 3 / 5 |
Progressive Disclosure | Clear overview with a real, well-signaled one-level-deep reference (references/geo_reference.md) whose stated topics match the file's actual sections, though large analysis code blocks (DE analysis, clustering, meta-analysis) are inlined rather than split into the reference. | 4 / 5 |
Total | 14 / 20 Passed |