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bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

62

Quality

74%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./backend/cli/skills/biology/bioservices/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%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 well-structured and highly actionable with real, executable examples and a clean reference/script split. Its main weaknesses are redundancy across sections and missing validation checkpoints for batch and multi-step workflows.

Suggestions

Consolidate identifier-mapping examples into section 5 and reference it from sections 1 and 3 to remove the triplicated mapping code.

Add explicit validation checkpoints to the batch and multi-service workflows (e.g., verify converted IDs exist before use, check BLAST job status in a retry loop) to lift workflow clarity above the batch cap.

Move the per-service "Key methods" summary lists into references/services_reference.md so SKILL.md stays a lean overview.

DimensionReasoningScore

Conciseness

Mostly efficient and code-heavy without explaining concepts Claude already knows, but identifier mapping is demonstrated three times (sections 1, 3, and 5) and "Key methods" lists restate methods already shown in the code above them.

3 / 5

Actionability

Numerous concrete, mostly copy-paste-ready code examples with real method names and parameters, plus script invocations; minor gaps such as the BLAST example referencing an undefined `protein_sequence` variable.

4 / 5

Workflow Clarity

Sequenced steps and numbered workflows are present, but validation/verification checkpoints are absent for the batch_id_converter and multi-service pipelines, which the rubric caps at 3 for batch operations lacking feedback loops.

3 / 5

Progressive Disclosure

Good structure with clearly signaled one-level-deep references (services_reference.md, workflow_patterns.md, identifier_mapping.md) and scripts that all exist in the bundle; inline "Key methods" lists could live in the reference file, leaving a minor organization gap.

4 / 5

Total

14

/

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.

The description is strong: it states the package's purpose, gives concrete trigger conditions with named databases, and provides explicit boundary guidance against neighboring tools. The only mild weakness is that the listed actions are broad categories rather than many specific concrete operations.

DimensionReasoningScore

Specificity

Lists several concrete actions ("querying multiple databases", "cross-database analysis", "ID mapping across services") plus named services (UniProt, KEGG, ChEMBL, Reactome), but the actions remain somewhat high-level rather than a comprehensive granular enumeration.

4 / 5

Completeness

Explicitly answers both what ("Unified Python interface to 40+ bioinformatics services") and when ("Use when querying multiple databases ... in a single workflow"), with concrete trigger phrases plus disambiguating guidance to gget and biopython.

5 / 5

Trigger Term Quality

Strong natural-term coverage via named databases ("UniProt, KEGG, ChEMBL, Reactome"), "cross-database analysis", "ID mapping", "single-database lookups"; a few common phrasings like "protein" or "pathway" are absent.

4 / 5

Distinctiveness Conflict Risk

Clear niche as a unified multi-database bioinformatics interface, and it explicitly differentiates from gget (single-database lookups) and biopython (sequence/file manipulation), giving minimal conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 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

14

/

16

Passed

Repository
synthetic-sciences/openscience
Reviewed

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