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primekg

Queries a pinned Precision Medicine Knowledge Graph (PrimeKG) CSV for typed gene, drug, disease, and phenotype nodes, direct associations, disease context, and one- or two-hop paths. Use for PrimeKG reproducibility, biological association lookup, and hypothesis generation with relation and data provenance preserved.

72

Quality

91%

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SKILL.md
Quality
Evals
Security

Quality

Content

85%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 high-quality, domain-expert body: every section carries non-obvious PrimeKG-specific caveats, the workflow is sequenced with explicit validation and error-recovery behavior, and detail is correctly split into an existing one-level-deep reference. The residual weaknesses are a commented-out get_neighbors example and a few review-hedging sentences that cost tokens without aiding execution.

Suggestions

Add one complete, runnable get_neighbors and find_paths example (with the node_type/node_source kwargs filled in from the search result) so the most common traversal calls are copy-paste ready.

Trim the review-process asides (e.g. "validation used small HTTP byte ranges and synthetic data, not the 982 MB file" and the stubbed-loader caveats) into the single Validation section or the data-contract reference, keeping the inline guidance instructional.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious domain caveats (reverse-row symmetrization, relation semantics, namespace collisions, MONDO_grouped IDs) that Claude would not know, with no library tutorials or filler. Minor trimmable instances remain — review-hedging asides like "validation used small HTTP byte ranges and synthetic data, not the 982 MB file" and inline review dates — placing it at the efficient-with-minor-trimming anchor rather than the every-token-earns-its-place anchor.

4 / 5

Actionability

Provides a copy-paste curl download, an executable search_nodes example, and exact API signatures (e.g. `get_neighbors(node_id, relation_type=None, *, node_type=None, node_source=None)`) backed by real functions in scripts/query_primekg.py. The gap to a 5 is that the get_neighbors call is commented out and find_paths lacks a full invocation example, so coverage of common cases is mostly — not fully — executable.

4 / 5

Workflow Clarity

A clear numbered sequence — pin and verify the artifact, resolve a typed entity before traversal, retrieve associations, then query context/paths — with explicit validation checkpoints and feedback loops: checksum/size verification before use, reviewing candidates before calling get_neighbors, "an ambiguous name returns an error and candidates; it never silently selects the first hit", and explicit failure on unsupported depths or max_paths overflow. Queries are read-only, so the destructive-operation cap does not apply.

5 / 5

Progressive Disclosure

The body is a well-organized overview that delegates data/API provenance (Dataverse metadata, checksum verification, PyTDC contracts) to a clearly signaled, one-level-deep references/data-contract.md (linked twice, file exists), and references the importable scripts.query_primekg helper (verified present). Core usage guidance (relation table, API semantics) is appropriately inline, matching the clear-overview/well-signaled-references anchor.

5 / 5

Total

18

/

20

Passed

Description

92%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: third-person, concrete, and comprehensive over the skill's capabilities, with an explicit "Use for" trigger clause naming the scenarios it covers. The only gap is a handful of missing natural synonyms a user might say when reaching for it.

DimensionReasoningScore

Specificity

"Queries a pinned Precision Medicine Knowledge Graph (PrimeKG) CSV for typed gene, drug, disease, and phenotype nodes, direct associations, disease context, and one- or two-hop paths" lists multiple concrete actions covering the skill's full query surface (node resolution, associations, disease context, path enumeration) in third person, matching the comprehensive-coverage anchor rather than the minor-gaps anchor at 4.

5 / 5

Completeness

It explicitly answers both "what" (queries for typed nodes, direct associations, disease context, one- or two-hop paths) and "when" ("Use for PrimeKG reproducibility, biological association lookup, and hypothesis generation") with concrete trigger phrases, matching the top anchor exactly.

5 / 5

Trigger Term Quality

"PrimeKG", "Precision Medicine Knowledge Graph", "biological association lookup", and "hypothesis generation" are natural user phrasings, but a few common variations (e.g. "drug repurposing", "protein-protein", "gene-disease association") are absent — good coverage with a few natural terms missing, not the synonym-complete coverage of a 5.

4 / 5

Distinctiveness Conflict Risk

"pinned PrimeKG CSV" names a specific published artifact and dataset, giving the skill a clear niche with distinct triggers and minimal risk of firing for unrelated knowledge-graph or bioinformatics skills.

5 / 5

Total

19

/

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

K-Dense-AI/scientific-agent-skills

Path

skills/primekg/SKILL.md

Reviewed

Table of Contents

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