bgee-skill

Submit compact Bgee SPARQL requests for healthy wild-type expression metadata and ontology-aware lookup patterns. Use when a user wants concise Bgee summaries; save raw results only on request.

Quality

87%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Low

Low-risk findings worth noting

Quality

Content

100%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A compact, well-organized body that gives executable guidance and clear I/O contracts without padding. It is an exemplar of a lean single-purpose skill body.

Dimension	Reasoning	Score
Conciseness	The body is lean and sectioned into Operating rules, Execution behavior, Input, Output, Execution, and References with no concept over-explanation; every token earns its place.	3 / 3
Actionability	It provides an executable command, concrete input/output field specifications, and example JSON payloads that are copy-paste ready.	3 / 3
Workflow Clarity	This is a simple single-purpose skill whose single action (run sparql_request.py with JSON stdin) is unambiguous, and the operating rules serve as a clear checklist with no destructive or batch operations requiring validation loops.	3 / 3
Progressive Disclosure	At under 50 lines with well-organized sections and a single real referenced script (scripts/sparql_request.py), the content is appropriately structured with no nested references.	3 / 3
	Total	12 / 12 Passed

Description

75%

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 well-scoped, third-person description with an explicit trigger clause and strong distinctiveness. Its main weakness is jargon-heavy trigger terms and a narrow action set rather than a broad menu of concrete capabilities.

Suggestions

Add a few natural-language trigger variations users might actually say (e.g. 'gene expression', 'where is a gene expressed', 'Bgee lookup') alongside the technical terms.

List one or two more concrete actions (e.g. 'look up gene expression by organ', 'fetch ontology terms') to broaden the specificity beyond a single submit action.

Dimension	Reasoning	Score
Specificity	It names a concrete action and domain ('Submit compact Bgee SPARQL requests for healthy wild-type expression metadata') but the action set is narrow rather than a comprehensive list of multiple distinct actions.	2 / 3
Completeness	It clearly answers what the skill does and includes an explicit 'Use when a user wants concise Bgee summaries' trigger clause.	3 / 3
Trigger Term Quality	Relevant terms are present (Bgee, SPARQL, expression metadata, summaries) but it leans on jargon like 'ontology-aware lookup patterns' and misses common user phrasings.	2 / 3
Distinctiveness Conflict Risk	The highly specific Bgee healthy wild-type expression niche with distinct triggers makes it unlikely to fire for the wrong skill.	3 / 3
	Total	10 / 12 Passed

Validation

100%

Warnings & errors only

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: openai/plugins
Path: plugins/life-science-research/skills/bgee-skill/SKILL.md
Commit: 11c74d6

Reviewed: about 10 hours ago

Table of Contents

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