Content
71%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 body is well-structured with a genuinely progressive-disclosure design — a lean overview pointing to six large, real reference files, a runnable script, and a report template, all cited with accurate paths and grep-based lookup instructions. Weaknesses are moderate: redundant capability/best-practices sections and comment-style examples cost tokens without adding executable value, and validation checkpoints live in Best Practices rather than in the workflow itself.
Suggestions
Cut the 'Key Capabilities' bullet list and the 'Report Generation' best practices ('Be comprehensive / Be specific / Be actionable') — they restate the description and generic advice Claude already knows.
Convert at least one Example (e.g., the FASTQ one) from comment-style outline to executable code that computes and prints read count, GC content, and quality stats.
Fold the metadata validation check ('Cross-check metadata consistency (e.g., stated dimensions vs actual data)') into Step 3 of the workflow as an explicit checkpoint rather than leaving it in Best Practices.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient — it assumes Claude's competence (never explains what FASTQ or pandas is) and gives library names without library tutorials — but several sections pad: the 'Key Capabilities' bullet list restates the description, 'Report Generation' best practices ('Be comprehensive', 'Be specific', 'Be actionable') are generic advice Claude already knows, and the three Examples are comment-style walkthroughs that largely repeat the workflow. It is noticeably tighter than a 2 but has enough unnecessary material to fall short of 4. | 3 / 5 |
Actionability | Provides mostly executable guidance: the analyzer invocation 'python scripts/eda_analyzer.py <filepath> [output.md]', a concrete regex ('### \.pdb[^#]*?(?=###|\Z)') for reference lookup, an ImportError fallback with an install command, and per-datatype analysis checklists. Below 5 because the Examples are pseudocode outlines ('# Calculate: read count, length distribution...') rather than copy-paste runnable code, and the per-format analysis steps are named but not shown. | 4 / 5 |
Workflow Clarity | The five-step workflow (detect → load reference → analyze → present → save-only-if-requested) is clearly sequenced with explicit decision points ('For a narrow request, return the result inline and stop'), plus a Troubleshooting section covering import errors, unknown extensions, and large files. It is a read-only analysis skill, so the destructive/batch validation cap does not apply; it misses 5 only because validation checkpoints (e.g., cross-checking stated vs actual dimensions) appear in Best Practices rather than being wired into the workflow as explicit checkpoints. | 4 / 5 |
Progressive Disclosure | Scored against the actual bundle: six large reference files, one script, and one asset all exist at the paths cited, and the body signals each with its exact path and purpose ('Reference file: references/chemistry_molecular_formats.md', 'assets/report_template.md', 'scripts/eda_analyzer.py'), plus explicit instructions to grep sections rather than load whole files. References are one level deep (spot-check confirms a flat '### .extension' structure, no nested pointers), and the Resources section indexes every bundle file — matching the 'clear overview with well-signaled one-level-deep references' anchor. | 5 / 5 |
Total | 16 / 20 Passed |