mgnify-skill

Submit compact MGnify API requests for microbiome studies, samples, and biome metadata. Use when a user wants concise MGnify summaries

Quality

95%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

100%Weight 40%Scale 1-3

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

The body is a tight, actionable, well-organized skill for a single read-only API tool: executable examples, clear input/output contracts, and an appropriate (minimal) reference structure. It fully meets the rubric across all dimensions.

Dimension	Reasoning	Score
Conciseness	The body is lean, gives operational one-liners, and avoids explaining concepts Claude already knows (no "what is MGnify" filler); the only minor inefficiency is that the Execution block repeats the Input example, which is not enough to drop it below anchor 3.	3 / 3
Actionability	It provides a complete, copy-paste-ready bash command piping full JSON into the real script, plus required/optional field lists and concrete example payloads, matching anchor 3.	3 / 3
Workflow Clarity	As a simple read-only skill, Input → Execution → Output is unambiguously sequenced and failure output is documented (ok=false with error.code/message); no destructive or batch operations require validation feedback loops, so it qualifies for anchor 3 under the simple-skills note.	3 / 3
Progressive Disclosure	At ~33 lines with well-organized sections and a single one-level-deep reference (scripts/rest_request.py, verified to exist) that is explicitly stated as the only runtime file, it meets the anchor-3 simple-skills criterion.	3 / 3
	Total	12 / 12 Passed

Description

90%Weight 40%Scale 1-3

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

The description is clear, niche-specific, and provides an explicit trigger clause answering both what and when. Its only weakness is specificity, as it describes essentially one action applied to several objects rather than a range of distinct actions.

Dimension	Reasoning	Score
Specificity	Names the domain and concrete objects ("microbiome studies, samples, and biome metadata") but relies on a single action verb ("Submit compact MGnify API requests"), so it is not a comprehensive list of distinct actions like the anchor-3 example.	2 / 3
Completeness	Explicitly answers what ("Submit compact MGnify API requests...") and when via an explicit "Use when a user wants concise MGnify summaries" clause, matching anchor 3 rather than the when-implied anchor 2.	3 / 3
Trigger Term Quality	Natural domain terms a user would actually say ("MGnify", "microbiome studies", "samples", "biome metadata", "concise MGnify summaries") give good coverage, matching the anchor-3 example.	3 / 3
Distinctiveness Conflict Risk	A narrow MGnify/microbiome niche with distinct triggers makes it unlikely to fire for unrelated skills, matching anchor 3.	3 / 3
	Total	11 / 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/mgnify-skill/SKILL.md
Commit: 11c74d6

Reviewed: 17 days ago

Table of Contents

Discovery Implementation Validation

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