Content
57%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.
A well-structured, highly actionable reference for AlphaFold DB access with concrete, executable examples throughout. Its main weaknesses are padding from duplicated confidence/version sections and generic use-case lists, missing validation checkpoints for batch/bulk operations, and a progressive-disclosure failure: the advertised references/api_reference.md does not exist in the bundle while its would-be content is inlined.
Suggestions
Actually create references/api_reference.md (or remove the pointer) and move the bulk-access, BigQuery, and batch-processing detail out of SKILL.md, keeping only quick-start retrieval and confidence basics inline.
De-duplicate the pLDDT/PAE threshold tables and version notes into a single section, and cut 'Key Concepts' entries that restate common knowledge (e.g., what a UniProt accession is).
Add validation checkpoints to the workflows: check response status codes before parsing JSON, verify downloaded file size/format, and wrap batch processing in a validate-and-retry pattern instead of a bare try/except print.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient — the bulk is dense, useful code — but there is real padding: pLDDT thresholds appear twice (inline comments in section 3 and again in "Confidence Interpretation Guidelines"), version/v4 notes are duplicated ("Key Concepts" and "Version Management"), "Common Use Cases" restates "When to Use This Skill", and "Key Concepts" defines things like "UniProt Accession" that Claude already knows. Matches 'mostly efficient but includes some unnecessary explanation or could be tightened'; not 2 because the majority of tokens are executable reference material that earns their place. | 3 / 5 |
Actionability | Largely executable, copy-paste-ready guidance: concrete REST URLs, Biopython calls, gsutil commands, and a full BigQuery query. Minor gaps keep it from 5: the batch example uses `requests` and `np` without importing them, `pred['entryId']` treats Biopython prediction objects as dicts inconsistently with the earlier `get_structural_models_for` usage, and no example checks HTTP status before parsing. Matches 'mostly executable guidance; concrete code or commands with minor gaps'. | 4 / 5 |
Workflow Clarity | Content is organized by capability rather than as a sequenced workflow, and validation checkpoints are absent or implicit — downloads never verify HTTP status or file integrity, and the batch loop only catches and prints exceptions without a validate/fix/retry loop. Since the skill includes batch and bulk operations, the guideline caps workflow clarity at 3 ('steps listed but validation gaps; checkpoints missing or implicit'). Not 4 because no explicit verification step exists anywhere. | 3 / 5 |
Progressive Disclosure | Section structure is good and the single reference is clearly signaled with a description of its contents and when to consult it, but scoring against the actual bundle reveals two problems: the referenced file `references/api_reference.md` does not exist (no references/ directory at all), and ~500 lines of material that belongs in that reference file (full API details, BigQuery/gsutil bulk access, batch processing) are inlined in SKILL.md. This sits between anchor 2 ('content that clearly belongs in separate files is inlined') and anchor 3; structure and signaling are genuinely good, so 3 rather than 2. | 3 / 5 |
Total | 13 / 20 Passed |