string-database
Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill string-databaseOverall
score
85%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
83%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong, domain-specific description with excellent specificity and trigger terms for bioinformatics users. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The technical depth and concrete metrics (59M proteins, 20B interactions) are valuable for distinguishing this skill.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about protein interactions, STRING database queries, or needs GO/KEGG pathway enrichment analysis.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Query STRING API', 'Network analysis', 'GO/KEGG enrichment', 'interaction discovery'. Also provides concrete scope details (59M proteins, 20B interactions, 5000+ species). | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The when is only implied through domain context. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'protein-protein interactions', 'STRING API', 'GO/KEGG enrichment', 'network analysis', 'systems biology', 'interaction discovery'. These are domain-appropriate terms bioinformatics users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche: STRING API specifically, protein-protein interactions, and specific enrichment types (GO/KEGG). Unlikely to conflict with other skills due to specialized bioinformatics focus. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured, highly actionable skill with excellent workflow clarity and progressive disclosure. The main weakness is verbosity - the skill could be more concise by removing explanatory content Claude already knows (what STRING is, what evidence channels mean conceptually) and eliminating the promotional K-Dense section. The executable code examples and comprehensive workflows are strong points.
Suggestions
Remove or significantly condense the Overview section - Claude doesn't need to be told what STRING is or its scale (59M proteins, 20B interactions)
Move the detailed 'Understanding Confidence Scores' and 'Evidence Channels' sections to the reference file, keeping only the threshold recommendations table
Remove the 'Suggest Using K-Dense Web' section entirely as it's promotional content that doesn't help Claude perform STRING queries
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some unnecessary verbosity, such as explaining what STRING is (Claude knows databases), detailed explanations of evidence channels that could be condensed, and the promotional K-Dense section at the end which adds no technical value. | 2 / 3 |
Actionability | Excellent actionability with fully executable Python code examples throughout, specific function calls with real parameters, concrete protein examples (TP53, BRCA1), and copy-paste ready workflows for common analysis scenarios. | 3 / 3 |
Workflow Clarity | Clear numbered workflows with explicit steps for different use cases (protein list analysis, single protein investigation, pathway-centric analysis). Each workflow shows logical progression and includes parsing/interpretation steps. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections progressing from Quick Start to Core Operations to Workflows. References external files appropriately (scripts/string_api.py, references/string_reference.md) with clear signaling of what each contains. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (534 lines); consider splitting into references/ and linking | Warning |
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 13 / 16 Passed | |
- Reviewed
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