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 body is well-structured with executable examples and clean progressive disclosure to three real reference files. Its weaknesses are verbosity in enumerative sections and missing validation/retry feedback loops in batch API workflows.
Suggestions
Trim exhaustive lists (publication types, MeSH subheadings, export formats) to representative examples and point to a reference file for the rest.
Add explicit validation/retry checkpoints to the programmatic workflows (e.g., check HTTP status, handle 429 rate-limit with exponential backoff, verify PMID count matches retmax).
Remove the reversed-email support artifact or replace it with a normal contact pointer to avoid confusion.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with concrete examples, but padded enumerations (publication types, MeSH subheadings, export formats) and over-explained concepts like the PICO framework and search-history mechanics could be trimmed. | 3 / 5 |
Actionability | Provides executable ESearch/EFetch Python code, concrete field-tag query examples, and grep patterns, with only minor placeholder gaps in some batch examples. | 4 / 5 |
Workflow Clarity | Five numbered workflows are sequenced clearly, but batch/bulk operations lack explicit validation or rate-limit retry checkpoints, which caps this dimension per the rubric. | 3 / 5 |
Progressive Disclosure | SKILL.md is a clear overview with three well-signaled, one-level-deep references (api_reference.md, search_syntax.md, common_queries.md), each with explicit 'When to consult' guidance; the inline capabilities section is somewhat long. | 4 / 5 |
Total | 14 / 20 Passed |