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ncats-arax

Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.

68

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%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 well-structured, highly actionable skill body that leads with executable commands and delegates detail to real one-level references. Weakest in explicit error-recovery feedback loops and minor verbosity in the interpretive lists.

Suggestions

Add an explicit feedback loop for empty or partial results (e.g., on partial exit code 7, inspect warnings then decide whether to rerun with adjusted providers).

Tighten the 'Interpret results' and 'Deliberate exclusions' bullet lists to trim residual verbosity.

DimensionReasoningScore

Conciseness

Lean and assumes competence with no concept tutorials; each section is a short lead plus an executable command, with minor over-explanation in the 'Interpret results' and 'Deliberate exclusions' lists.

4 / 5

Actionability

Fully executable copy-paste commands for every subcommand (preflight, normalize, one-hop, two-hop, federated, summarize) with real flags and argument values covering the common cases.

5 / 5

Workflow Clarity

A clear 7-step numbered workflow with explicit guardrails ('Do not silently change provider selection', 'Verify outside ARAX'); checkpoints are present but a validate→fix→retry feedback loop is not made explicit.

4 / 5

Progressive Disclosure

Good structure with SKILL.md as an overview and bulk detail delegated one level deep to the real query-contract.md and output-schema.md via explicit 'Read X before...' navigation; minor organization gaps.

4 / 5

Total

17

/

20

Passed

Description

80%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, third-person description that concretely defines the skill's capabilities and explicit use/non-use triggers with low conflict risk. Its main weakness is trigger-term naturalness, which leans on technical jargon over user-spoken phrasing.

Suggestions

Add user-spoken synonyms alongside jargon (e.g., 'biomedical relationship lookup' or 'find connections between genes, drugs, and diseases') to improve trigger naturalness.

Soften abstract qualifiers like 'provenance-rich' by naming the concrete provenance fields inspected.

DimensionReasoningScore

Specificity

Lists several concrete actions (one-hop/two-hop KG relationships, Biolink-constrained RTX-KG2 lookup, selected-provider federation, entity normalization, TRAPI edge/provenance inspection), with minor gaps where phrasing like 'provenance-rich' stays abstract.

4 / 5

Completeness

Explicitly answers both what (queries ARAX for bounded typed KG relationships plus normalization and provenance inspection) and when via a concrete 'Use for...' trigger list, supplemented by a 'Do not use for...' boundary.

5 / 5

Trigger Term Quality

Contains some relevant natural terms ('knowledge-graph relationships', 'entity normalization', 'federation') but leans heavily on domain jargon (TRAPI, Biolink, RTX-KG2, qualifier-aware) and lacks common synonyms users would actually say.

3 / 5

Distinctiveness Conflict Risk

A clear niche (NCATS Translator ARAX biomedical KG lookup) with an explicit negative-boundary clause, yielding minimal overlap risk with other skills.

5 / 5

Total

17

/

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
Reviewed

Table of Contents

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