CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

mgnify-skill

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

72

Quality

91%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

100%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.

The body is an exemplary compact skill: lean, fully executable, and unambiguous, with a verified script reference and a clearly organized section structure that fits its size. No dimension shows a weakness worth trading tokens against.

DimensionReasoningScore

Conciseness

Every line is operational: operating rules ("Use scripts/rest_request.py for all MGnify calls", "Prefer record_path=data"), the base URL, a compact input/output schema, and one runnable command. There is no explanation of concepts Claude already knows and no padding, matching the 5 anchor (lean, assumes competence, every token earns its place).

5 / 5

Actionability

The body provides a copy-paste-ready command ("echo '{...}' | python scripts/rest_request.py") plus two concrete JSON request patterns covering the common studies and biomes cases, and documents required/optional stdin fields and the full output/error contract ("ok", "status_code", "error.code", "error.message"). This is fully executable guidance with specific examples covering the common cases, matching the 5 anchor.

5 / 5

Workflow Clarity

This is a simple single-task skill (run one script with a JSON object on stdin) and the single action is unambiguous: the exact script path, exact command, exact input format, and both success and failure output shapes are specified. Per the simple-skill exception, workflow clarity scores 5; there are no destructive or batch operations that would cap it.

5 / 5

Progressive Disclosure

The skill is well under 50 lines with no need for external references: the only bundle file, scripts/rest_request.py, is referenced and verified to exist, and the body explicitly states "No additional runtime references are required; keep the import package limited to this file and scripts/rest_request.py". Sections (Operating rules, Execution behavior, Input, Output, Execution, References) are clearly organized, matching the simple-skill case for a 5.

5 / 5

Total

20

/

20

Passed

Description

78%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.

The description is concise, third-person, and anchored to a uniquely named service, which gives it excellent distinctiveness. Its weaknesses are the narrow single-scenario "Use when" clause and missing natural synonyms such as "metagenomics" or "EBI". Broadening the trigger coverage would raise completeness and trigger term quality to the 5-anchor level.

Suggestions

Broaden the "Use when" clause to cover more trigger scenarios, e.g. "Use when the user asks about MGnify, wants microbiome or metagenomics data, or wants concise summaries of studies, samples, or biomes."

Add natural synonyms and identifiers users would actually say, such as "metagenomics", "EBI", or study accession formats like MGYS, to improve trigger term coverage.

Consider naming a second concrete action (e.g. "fetch and summarize" or "look up") alongside "Submit compact MGnify API requests" to make the capability list more comprehensive.

DimensionReasoningScore

Specificity

"Submit compact MGnify API requests for microbiome studies, samples, and biome metadata" names the domain and three concrete objects in third person, but lists only one action type (submitting requests), leaving minor gaps in coverage. It is more concrete than the 3 anchor ("1-2 concrete actions") yet not the comprehensive multi-action coverage of the 5 anchor.

4 / 5

Completeness

Both parts are explicit: the "what" is submitting compact MGnify API requests, and the "when" is "Use when a user wants concise MGnify summaries". The when-clause is present but narrow, covering only the summary scenario and not triggers like fetching a study or looking up a sample, matching the 4 anchor (when could be more explicit or specific) rather than the 5 anchor's concrete multi-phrase triggers.

4 / 5

Trigger Term Quality

Terms like "MGnify", "microbiome studies", "samples", "biome metadata", and "concise MGnify summaries" are natural phrases a user would say, but common variations are missing ("metagenomics", "EBI", study accessions such as MGYS). Good keyword coverage with a few natural terms missing matches the 4 anchor rather than the comprehensive synonym coverage of 5.

4 / 5

Distinctiveness Conflict Risk

"MGnify API" names a unique, specific service with distinct domain triggers (microbiome studies, samples, biome metadata), giving it a clear niche with minimal risk of firing for unrelated skills. This matches the 5 anchor (clear niche with distinct triggers); it is clearly more distinct than the 4 anchor's overlap risk.

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

openai/plugins

Path

plugins/life-science-research/skills/mgnify-skill/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.