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string-database

Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.

71

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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-organized, token-efficient body that uses progressive disclosure effectively and includes a mandatory validation gate. Adding one or two inline worked command examples and an error-recovery loop would push it to full marks.

Suggestions

Add 1-2 concrete worked command examples for the most common operations (e.g. a 'map' and a 'interactions' call with real flags) so the body is copy-paste ready without opening a reference file.

Include a brief error-recovery feedback loop (e.g. what to do on API errors, rate limits, or empty result sets) to strengthen workflow validation for batch/remote operations.

DimensionReasoningScore

Conciseness

The body is lean and directive throughout, assumes Claude's competence, and never pads with explanations of what STRING/PPIs/libraries are; every section earns its place.

5 / 5

Actionability

Provides a concrete runnable invocation ('uv run scripts/string_cli.py <command> [options] --output /tmp/out.tsv'), but only a generic template rather than specific copy-paste worked examples for common commands, which live in the reference files.

4 / 5

Workflow Clarity

Clear ordered workflow with an explicit validation checkpoint (MANDATORY species confirmation before proceeding) and a map-identifiers-first step, but no error-recovery feedback loops for API failures or retries.

4 / 5

Progressive Disclosure

A concise overview with four well-signaled, one-level-deep reference links, each annotated with a clear one-line purpose; all referenced files exist in references/ and content is appropriately split by feature domain.

5 / 5

Total

18

/

20

Passed

Description

87%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, well-structured description that clearly states capabilities and provides explicit 'Use when' trigger guidance. Minor trigger-term synonyms (GO/KEGG, protein network) could be added for completeness.

DimensionReasoningScore

Specificity

Names the STRING domain plus three concrete capability areas (PPIs, functional enrichment, homology), but coverage is not exhaustive at the description level (e.g. network topology and values/ranks enrichment appear only in the body).

4 / 5

Completeness

Explicitly answers both what ('Query the STRING database for ... PPIs, functional enrichment, and homology') and when ('Use when the user asks about ...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural trigger coverage ('interactions between specific proteins', 'interaction evidence', 'confidence scores', 'protein interaction partners', 'pathway enrichments'), but a few natural terms are missing (GO/KEGG/Pfam, 'protein network').

4 / 5

Distinctiveness Conflict Risk

The STRING-database / PPI niche is specific with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

18

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
google-deepmind/science-skills
Reviewed

Table of Contents

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