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biopython

A comprehensive toolbox for computational molecular biology; use it when you need programmatic sequence/structure parsing, batch bioinformatics pipelines, or automated NCBI/BLAST workflows.

64

Quality

77%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/biopython/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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 delivers an executable, well-structured overview with real bundle references, but it leans slightly verbose in Implementation Details and lacks an explicit validation feedback loop for its batch/remote operations, which the rubric expects.

Suggestions

Trim Implementation Details that restate the example (Entrez config, sleep, streaming) to reduce token redundancy.

Add an explicit validate/retry feedback loop for BLAST and Entrez calls (e.g. 'on transient HTTP failure, back off and retry up to N times') to satisfy the batch-operation workflow expectation.

Render the reference list as markdown links (e.g. [blast.md](references/blast.md)) with one-line teasers so navigation is unambiguous.

DimensionReasoningScore

Conciseness

The body is mostly efficient and code-forward, but several Implementation Details repeat guidance already shown in the runnable example (Entrez email/api_key, time.sleep, SeqIO.parse streaming) and some prose explains concepts Claude already knows.

3 / 5

Actionability

A complete, copy-paste runnable pipeline with real imports, env-var configuration, and concrete run commands is provided; only minor gaps (e.g. input.fasta handling, BLAST error cases) keep it from a 5.

4 / 5

Workflow Clarity

The example sequences steps clearly (parse -> GC -> BLAST -> fetch -> print) and Implementation Details add retry/delay guidance, but there is no explicit validate-then-fix feedback loop for the batch network operations.

4 / 5

Progressive Disclosure

SKILL.md is a concise overview with a clearly signaled one-level-deep reference list (sequence_io.md, blast.md, etc.), all of which exist as real files; minor gaps are that links are plain paths rather than markdown links and not all bodies are teased.

4 / 5

Total

15

/

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 well-formed: it names the domain and concrete capabilities and gives an explicit 'use it when' trigger. It is slightly below a perfect specificity/trigger score only because it omits common synonyms and file extensions that users might say.

Suggestions

Add concrete file extensions and synonyms to the description (e.g. 'FASTA/FASTQ/GenBank/PDB parsing') to strengthen trigger-term coverage.

Mention phylogenetics/tree-building in the description so the 'what' coverage matches the body.

DimensionReasoningScore

Specificity

Lists several concrete actions ("sequence/structure parsing", "batch bioinformatics pipelines", "automated NCBI/BLAST workflows") with only minor coverage gaps (e.g., phylogenetics is in the body but not the description).

4 / 5

Completeness

It states both what ("A comprehensive toolbox for computational molecular biology") and an explicit when ("use it when you need programmatic sequence/structure parsing, batch bioinformatics pipelines, or automated NCBI/BLAST workflows").

5 / 5

Trigger Term Quality

Natural user-facing terms like "sequence/structure parsing", "bioinformatics pipelines", "NCBI/BLAST workflows" are present, but common synonyms and concrete file extensions (FASTA/FASTQ/PDB) are omitted from the description.

4 / 5

Distinctiveness Conflict Risk

The niche (Biopython / computational molecular biology with NCBI/BLAST triggers) is specific and unlikely to overlap with unrelated skills; conflict risk is minimal.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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