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uniprot-database

Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.

52

Quality

60%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/uniprot-database/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

42%Scale 1-5

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

This skill provides a reasonable reference for UniProt REST API access but suffers from verbosity (explaining what UniProt is, restating capabilities multiple times) and incomplete actionability (query strings shown but no complete HTTP request examples). The workflow for ID mapping is outlined but lacks error handling/validation steps, and the referenced helper script and supporting files are not provided in the bundle.

Suggestions

Remove the overview paragraph explaining what UniProt is and the 'When to Use This Skill' section — Claude already knows these things. Start directly with Core Capabilities.

Add a complete, executable Python example using the requests library that performs a search and parses the JSON response, rather than just showing URL patterns and query strings.

Add error handling and validation steps to the ID mapping workflow (e.g., check for failed jobs, validate input IDs, handle rate limiting responses with retry logic).

Move the detailed query syntax examples to the referenced query_syntax.md file and keep only 1-2 key examples inline to reduce the main file length.

DimensionReasoningScore

Conciseness

The overview section explains what UniProt is (Claude already knows this), the 'When to Use This Skill' section is an 8-item list that largely restates the overview, and there's significant redundancy throughout. The best practices section includes obvious advice like 'choose the most appropriate format' and 'cache results.' Much of this could be cut in half.

2 / 5

Actionability

The skill provides URL patterns and query syntax examples which are useful, but the Python implementation section references a helper script (`scripts/uniprot_client.py`) without showing its actual code. The query examples are illustrative but not fully executable end-to-end — there's no complete working example showing how to make an HTTP request and parse the response. The code blocks show query strings but not the actual requests library calls.

3 / 5

Workflow Clarity

The ID mapping workflow has a clear 3-step sequence (submit, check status, retrieve results), but lacks validation/error handling steps — what if the job fails? What if IDs are invalid? There's no feedback loop for error recovery. The general search workflow is implicit rather than explicitly sequenced. For batch operations with up to 100,000 IDs, the absence of validation checkpoints is notable.

3 / 5

Progressive Disclosure

The skill references several supporting files (api_fields.md, id_mapping_databases.md, query_syntax.md, api_examples.md, uniprot_client.py) which is good structure, but no bundle files were provided, so these references are unverifiable. The main file itself is quite long (~180 lines) and inlines content that could be in reference files (e.g., the full query syntax examples section could live in the referenced query_syntax.md). The references are reasonably well-signaled though.

3 / 5

Total

11

/

20

Passed

Description

78%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.

This is a strong skill description that clearly identifies its domain (UniProt REST API), lists concrete capabilities, and notably includes explicit differentiation from a related skill (bioservices). The main weakness is that the 'when' clause focuses on technical architecture decisions rather than user-facing trigger phrases, and a few natural synonyms are missing from the trigger terms.

DimensionReasoningScore

Specificity

Lists several specific actions: 'Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL' — these are concrete capabilities. Minor gaps exist (e.g., doesn't mention batch retrieval, annotation extraction, or specific endpoint details), but coverage is good.

4 / 5

Completeness

The 'what' is clear (REST API access to UniProt with specific capabilities). The 'when' is present ('Use this for direct HTTP/REST work or UniProt-specific control') and includes differentiation from bioservices, but the 'when' clause could be more explicit about user-facing trigger scenarios rather than focusing on technical architecture choices.

4 / 5

Trigger Term Quality

Includes strong natural keywords like 'UniProt', 'FASTA', 'protein searches', 'ID mapping', 'Swiss-Prot', 'TrEMBL', 'REST API'. Missing some user-natural phrases like 'protein sequence', 'accession number', 'protein database', or 'uniprot.org'.

4 / 5

Distinctiveness Conflict Risk

Excellent distinctiveness — explicitly differentiates itself from the bioservices skill by specifying when to use each. The niche (direct REST API to UniProt) is very clear and unlikely to conflict with other skills, and the boundary guidance ('For Python workflows with multiple databases, prefer bioservices') actively reduces conflict risk.

5 / 5

Total

17

/

20

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.

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

9

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

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