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mining-pubmed-literature

Searches and fetches PubMed and PMC via NCBI E-utilities (ESearch then EFetch/ESummary) to gather biomedical evidence and build text corpora. Use when the user wants citations for a condition or drug, abstracts to summarize, MeSH-based searches, or a corpus of literature to run NER over. Trigger keywords: PubMed, PMC, NCBI, E-utilities, ESearch, EFetch, ESummary, MeSH, PMID, literature search, abstracts, evidence. Pairs adjacent to OpenMed: fetched abstracts feed openmed.analyze_text for biomedical NER, and OpenMed-extracted diagnoses/drugs/genes become the search terms. E-utilities are public; an optional free API key raises rate limits from 3 to 10 requests/second.

72

Quality

90%

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Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

76%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.

A strong, highly actionable body with real executable code and genuinely non-obvious gotchas (no EFetch JSON for PubMed, History-server expiry, MeSH lag, PMC licensing). The main weaknesses are the missing in-sequence validation checkpoints for the batch-fetch workflow and mild repetition of the OpenMed NER hand-off across three sections.

Suggestions

Add explicit validation checkpoints to the Workflow sequence: after ESearch, verify the returned count and WebEnv/query_key before fetching; after each batch fetch, check for NCBI error blocks or truncated output before paging further.

Move the 429-retry/backoff feedback loop from the Edge cases section into Workflow step 3 so the batch-fetch step includes a validate-and-retry loop.

Consolidate the OpenMed NER guidance, which currently appears in When-to-use, Workflow step 5, and the Hand-off section, into the single Hand-off section.

DimensionReasoningScore

Conciseness

The body is dense and non-padded with no explanations of concepts Claude already knows, and every section (rate limits, EFetch-has-no-JSON, history expiry, MeSH lag) adds non-obvious domain knowledge. It falls short of 5 because the OpenMed NER hand-off is repeated across "When to use", Workflow step 5, and the "Hand-off to / from OpenMed" section — trimmable redundancy.

4 / 5

Actionability

Fully executable, copy-paste-ready Python (esearch with usehistory=y, efetch_abstracts, esummary with timeouts and raise_for_status), equivalent cURL commands, and concrete query syntax ("[MeSH]", "[tiab]", "[pdat]", 2023:2025[pdat]) covering the common cases. Not 4 — there are no gaps; even error paths (HTTP 429) are addressed.

5 / 5

Workflow Clarity

The 5-step workflow (build query → ESearch → batch-fetch → parse → NER) is clearly sequenced, but this is a batch operation and the sequence itself contains no validation checkpoints — no verify-count-before-fetching, no response-error check, no fetch-completeness confirmation. The 429-retry feedback loop exists only in the separate "Edge cases" section, outside the sequence, so per the batch-operations cap it cannot exceed 3; it is above 2 because the steps are well-defined and rate-limit handling is embedded in step 3.

3 / 5

Progressive Disclosure

Well-organized sections (When to use, Quick start, ESummary, Workflow, Hand-off, Edge cases, Standards) with a concise overview and an external standards link list; no false references to nonexistent bundle files. Not 5 because at ~130 lines everything is inline — bulk detail such as full E-utilities parameter syntax or PMC JATS-XML parsing details could live one level deep in a reference file; not 3 because the structure and placement are otherwise appropriate.

4 / 5

Total

16

/

20

Passed

Description

100%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.

An exemplary description: concrete named actions, an explicit "Use when" clause with real user scenarios, a comprehensive natural-keyword trigger list, and a clearly demarcated PubMed/PMC niche with honest boundary signaling toward OpenMed and rate-limit facts. No changes needed.

DimensionReasoningScore

Specificity

"Searches and fetches PubMed and PMC via NCBI E-utilities (ESearch then EFetch/ESummary) to gather biomedical evidence and build text corpora" names multiple concrete actions with specific tool names (ESearch, EFetch, ESummary) and comprehensively covers the domain including NER hand-off. Not 4 — there are no minor coverage gaps; the actions span search, fetch, metadata retrieval, and corpus building.

5 / 5

Completeness

The first sentence explicitly answers "what" (searches/fetches PubMed and PMC via ESearch then EFetch/ESummary) and "Use when the user wants citations for a condition or drug, abstracts to summarize, MeSH-based searches, or a corpus of literature to run NER over" explicitly answers "when" with concrete triggers. Both anchors of the 5 example are satisfied; not 4 because neither half could be more explicit.

5 / 5

Trigger Term Quality

"Trigger keywords: PubMed, PMC, NCBI, E-utilities, ESearch, EFetch, ESummary, MeSH, PMID, literature search, abstracts, evidence" covers natural user phrasings ("literature search", "abstracts", "evidence") plus technical synonyms. Not 4 — the keyword set is comprehensive rather than missing a few natural terms.

5 / 5

Distinctiveness Conflict Risk

The PubMed/PMC/NCBI E-utilities niche is unmistakable, and the OpenMed text ("Pairs adjacent to OpenMed") signals complementary adjacency rather than overlap. Not 4 — trigger terms like PMID/MeSH/E-utilities are unique to this skill and carry minimal conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

maziyarpanahi/openmed

Path

skills/mining-pubmed-literature/SKILL.md

Reviewed

Table of Contents

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