pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Quality

72%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./bundled/skills/pydeseq2/SKILL.md

Quality

Content

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

The content is highly actionable with executable code throughout and a clear, well-sequenced workflow supported by real reference files. The main weakness is conciseness — there is notable redundancy across sections and recap material that inflates the body.

Suggestions

Reduce redundancy by collapsing the repeated load/fit/test patterns in "Common Analysis Patterns" into references to the Quick Start, or move those variants into the workflow guide.

Trim the "Key Reminders" section to only points not already established inline, or remove it in favor of the workflow steps.

Add explicit validation checkpoints (e.g. assert counts and metadata indices match before fitting) to elevate workflow clarity for this batch/statistical pipeline.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and code-driven, but there is noticeable redundancy — repeated loading/fitting patterns across sections, an 8-point recap of what deseqq2() does, a "Key Reminders" recap of points already covered, and prose troubleshooting that could be trimmed.	3 / 5
Actionability	Fully executable, copy-paste-ready code throughout Quick Start, all six workflow steps, common patterns, result interpretation, and troubleshooting, covering the common cases comprehensively.	5 / 5
Workflow Clarity	A clear six-step sequence with an enumerated breakdown of the fitting pipeline, but explicit validation checkpoints or feedback loops (e.g. verifying counts/metadata alignment before fitting) are mostly implicit rather than called out.	4 / 5
Progressive Disclosure	Good structure with a concise overview and well-signaled, one-level-deep references to real files (api_reference.md, workflow_guide.md) plus a bundled script, though some inlined content (visualization, troubleshooting) could arguably live in the references.	4 / 5
	Total	16 / 20 Passed

Description

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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 specific and occupies a distinct niche, with concrete actions and good trigger keywords. Its main weakness is the absence of an explicit "Use when..." clause, leaving the trigger context only weakly implied.

Suggestions

Add an explicit "Use when..." trigger clause, e.g. "Use when analyzing bulk RNA-seq count data for differential expression, or when the user mentions DESeq2, PyDESeq2, or differential expression analysis."

Include natural synonyms and common phrasings users would say, such as "DE analysis", "differential expression", and "count matrix".

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ("Identify DE genes", "Wald tests", "FDR correction", "volcano/MA plots") with only minor coverage gaps, matching the "several specific actions; minor gaps" anchor.	4 / 5
Completeness	Clearly states what the skill does but the "when" is only weakly implied by the trailing "for RNA-seq analysis" clause with no explicit "Use when..." trigger guidance, which caps completeness at 3 per the rubric.	3 / 5
Trigger Term Quality	Good keyword coverage including "DESeq2", "differential gene expression", "RNA-seq", and "bulk RNA-seq counts", but missing a few natural synonyms (e.g. "DE analysis", "differential expression") and any file extensions.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (Python DESeq2 / bulk RNA-seq differential expression) with distinct triggers and minimal overlap with other skills.	5 / 5
	Total	16 / 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
skill_md_line_count	SKILL.md is long (554 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/pydeseq2/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

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