scikit-bio

A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).

Quality

83%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

72%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 dense, actionable reference body with an excellent executable example and concrete API guidance, but it functions as a catalogue rather than a workflow and fails to surface its existing deeper reference file from the overview.

Suggestions

Add a pointer to the bundled reference, e.g. a '## API Reference' section linking 'See [api_reference.md](references/api_reference.md) for full method signatures, advanced examples, and troubleshooting' so the one-level-deep material is discoverable.

For common end-to-end analyses (e.g. a diversity/ordination workflow), add a short sequenced section with an explicit validation/checkpoint (e.g. verify the DistanceMatrix is symmetric and IDs align before PERMANOVA) to lift workflow clarity above a catalogue.

Trim the overlap between 'Key Features' and 'Implementation Details' so method names appear once with their concrete guidance, reducing token cost without losing actionability.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with a dense, useful API catalogue and a complete executable example; minor over-explanation comes from Key Features and Implementation Details restating overlapping method lists, but each section adds value Claude would not infer (exact signatures, return types, the integer-counts caveat).	4 / 5
Actionability	Provides a fully executable, copy-paste-ready example spanning sequences, alpha/beta diversity, PCoA, PERMANOVA, and tree I/O, plus concrete signatures and gotchas (non-negative integer counts, permutations=999+ for stable p-values) covering the common cases.	5 / 5
Workflow Clarity	This is a reference/catalogue skill rather than a multi-step pipeline; the example uses numbered comments (1-5) but there are no validation checkpoints or feedback loops, so it sits at the 'sequence present but checkpoints missing' anchor.	3 / 5
Progressive Disclosure	The body is well-sectioned and a 748-line references/api_reference.md bundle exists, but the body never links to or signals it, leaving navigation to the deeper material missing — matching the 'references present but not clearly signaled' anchor rather than the well-signaled anchor-4.	3 / 5
	Total	15 / 20 Passed

Description

95%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, third-person description that clearly states the toolkit's purpose and an explicit trigger clause with concrete natural keywords and format extensions. Minor room to enumerate individual actions, but it comprehensively answers what and when.

Dimension	Reasoning	Score
Specificity	Names several concrete domains and actions ('sequence, phylogeny, and microbiome/community-ecology analysis', 'compute diversity/ordination/statistics') plus standard formats, but is slightly less action-explicit than the anchor-5 example which enumerates individual operations.	4 / 5
Completeness	Explicitly answers both 'what' (a Python bioinformatics toolkit for sequence/phylogeny/microbiome analysis) and 'when' ('use it when you need to compute diversity/ordination/statistics from biological data and standard formats'), matching the anchor-5 example.	5 / 5
Trigger Term Quality	Comprehensive natural terms including synonyms and format extensions ('diversity', 'ordination', 'statistics', 'FASTA/FASTQ/Newick/BIOM', 'microbiome/community-ecology', 'biological data') that users would naturally say.	5 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (scikit-bio bioinformatics) with distinct, domain-specific triggers and format names, giving minimal conflict risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Data Analysis/scikit-bio/SKILL.md
Commit: f5ef65b

Reviewed: ~7 hours ago

Table of Contents

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