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pubmed-database

Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.

60

Quality

76%

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./backend/cli/skills/databases/pubmed-database/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%Weight 40%Scale 1-5

Reviews 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 and domain-dense with mostly executable examples and genuine progressive disclosure to three real reference files. Its weaknesses are redundancy across sections (rate limits, expiry limits, reference pointers each repeated), the absence of validation/feedback checkpoints in the batch API workflow, and inlining of web-UI material that belongs in a reference file.

Suggestions

State rate limits, history/clipboard expiry, and reference-file consult cues once each, and cut the duplicated 'Working with Reference Files' summary to a short pointer list — this would tighten conciseness toward anchor 4.

Add explicit validation to Workflow 3 (e.g., check the esearchresult error field, handle empty idlist, back off on HTTP 429) so the batch workflow has real checkpoints and can clear the workflow-clarity cap of 3.

Move web-interface features (My NCBI, saved searches, RSS, clipboard, export formats) into a reference file or fold them into common_queries.md so SKILL.md stays an overview, lifting progressive_disclosure toward 5.

DimensionReasoningScore

Conciseness

Mostly efficient domain-specific content (field tags, MeSH syntax, E-utility endpoints, rate limits) that Claude would not reliably know, but it could be tightened: rate limits are stated three times (sections 4, Tips, Limitations), search-history/clipboard expiry is stated twice, the 'Working with Reference Files' section repeats the per-section 'When to consult' lists, and the opening explains what PubMed is. Fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the 'minor instances' of anchor 4.

3 / 5

Actionability

Mostly executable guidance: a runnable requests-based ESearch/EFetch snippet, concrete query examples with field tags, exact URL patterns, and grep patterns for the reference files. Minor gaps keep it below 5 — the EFetch example omits error handling, the ECitMatch and 'Batch Export' blocks are bare format strings rather than runnable code, and no workflow shows checking the JSON response for errors.

4 / 5

Workflow Clarity

Five workflows are clearly sequenced, but validation is implicit or absent: the batch API workflow says 'Implement rate limiting and error handling' with no checkpoint (no response status check, no empty-idlist or error-field handling, no retry guidance). Per the rubric, missing validation steps in batch operations caps workflow clarity at 3, which takes precedence over the otherwise clean sequencing.

3 / 5

Progressive Disclosure

Good structure with three real, one-level-deep reference files (verified present in references/), each signaled inline with 'When to consult' lists plus grep patterns and a summary section. Below 5 because some content that belongs in references is inlined in a ~450-line body (web-UI features like My NCBI, RSS feeds, search history, clipboard, export formats, support resources) and the reference pointers are duplicated between per-section cues and the 'Working with Reference Files' section.

4 / 5

Total

14

/

20

Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: specific, third-person, concise, and with both a what and a when clause plus explicit boundary guidance against biopython. The main gap is trigger-term breadth — it lacks the natural user phrasings (literature search, abstracts, MEDLINE, PMID) that would guarantee invocation for common biomedical-literature requests.

Suggestions

Add natural trigger phrases users actually say, e.g., 'Use when the user asks to search PubMed, find biomedical articles, retrieve abstracts, or look up PMIDs', to lift trigger_term_quality and completeness toward 5.

Include common synonyms/extensions such as MEDLINE, PMID, DOI, and 'systematic review' in the description so literature-search requests match reliably.

Keep the existing biopython boundary note — it is effective distinctiveness guidance and worth preserving verbatim.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — "Direct REST API access to PubMed", "Advanced Boolean/MeSH queries", "E-utilities API, batch processing, citation management" — covering the skill's core capabilities comprehensively rather than in generic terms.

5 / 5

Completeness

Both 'what' (REST access, Boolean/MeSH queries, E-utilities, batch processing, citation management) and 'when' ("Use this for direct HTTP/REST work or custom API implementations") are present, with bonus boundary guidance ("For Python workflows, prefer biopython"). Not 5 because the when-clause is narrow and conditional rather than a set of concrete user trigger phrases (e.g., 'when the user asks to search PubMed or fetch abstracts').

4 / 5

Trigger Term Quality

Good keyword coverage ("PubMed", "REST API", "E-utilities", "MeSH", "citation management", "biopython") but natural phrases a user would say such as "literature search", "biomedical articles", "abstracts", "MEDLINE", or "PMID" are missing. Not 5 because synonym coverage is incomplete; not 3 because the terms present are natural and relevant rather than purely technical jargon.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche — direct HTTP/REST access to PubMed — and explicitly disambiguates against the nearest competing skill ("For Python workflows, prefer biopython (Bio.Entrez)"), minimizing wrong-skill triggering. Anti-drift check: fits the 'clear niche with distinct triggers' anchor better than the 'minor overlap risk' anchor 4.

5 / 5

Total

18

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/databases/pubmed-database/SKILL.md

Reviewed

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