pydeseq2

Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Quality

66%

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Securityby

Passed

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

Quality

Content

65%

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

The body is highly actionable with executable code and a clear step sequence, but it is over-long with redundancy across sections and pushes little into the available references, capping conciseness, workflow clarity, and progressive disclosure at 2.

Suggestions

Deduplicate the Quick Start, Core Workflow Steps, and Common Analysis Patterns sections so the canonical workflow appears once; cross-reference variants instead of restating full code blocks.

Move detailed visualization (volcano/MA plots) and troubleshooting into references/workflow_guide.md, leaving the SKILL.md as a concise overview with well-signaled pointers.

Add explicit inline validation checkpoints in the core 6-step workflow (e.g., verify size factors and dispersion before statistical testing) rather than relying on the bundled script for validation.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean executable code with little concept padding, but at ~570 lines it repeats content — the Quick Start duplicates Core Workflow Steps, Common Analysis Patterns re-shows the two-group case, and Key Reminders restate in-step guidance — so it could be tightened.	2 / 3
Actionability	It provides fully executable Python throughout, concrete CLI invocations with flags, and copy-paste-ready examples matching the anchor for executable, specific guidance.	3 / 3
Workflow Clarity	Steps 1–6 are clearly sequenced and troubleshooting offers diagnostics, but the main in-SKILL workflow lacks explicit inline 'validate then proceed' checkpoints; validation is implicit or delegated to the bundled script rather than gated in the core steps.	2 / 3
Progressive Disclosure	References to references/api_reference.md and references/workflow_guide.md are well-signaled, one-level-deep, and the files exist, but the SKILL.md itself is a long monolithic body with visualization and troubleshooting content that overlaps the workflow_guide reference, so content that could be separate remains inline.	2 / 3
	Total	9 / 12 Passed

Description

67%

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 clearly niche-scoped, but lacks an explicit 'when to use' trigger clause and omits some common trigger-term variations, which cap its completeness and trigger-term scores at 2.

Suggestions

Add an explicit 'Use when...' clause to the description, e.g. 'Use when analyzing bulk RNA-seq counts for differential expression or when users mention DESeq2, RNA-seq, or PyDESeq2.'

Broaden trigger terms to include common variations like 'DESeq2', 'RNA-seq analysis', and 'count data' that users are likely to say.

Keep the concise capability list but ensure the trigger guidance is in the description itself, not only in the body's 'When to Use' section.

Dimension	Reasoning	Score
Specificity	The description enumerates multiple concrete capabilities — 'formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization' — matching the anchor for listing several specific concrete actions rather than just naming a domain.	3 / 3
Completeness	It clearly answers 'what' the skill does, but contains no explicit 'Use when...' trigger clause; per the guidelines a missing explicit trigger guidance caps completeness at 2.	2 / 3
Trigger Term Quality	It includes relevant natural terms ('differential gene expression', 'bulk RNA-seq', 'PyDESeq2') but misses common variations a user might say such as 'DESeq2', 'RNA-seq analysis', or 'count data' that appear only in the body, so coverage is solid but not comprehensive.	2 / 3
Distinctiveness Conflict Risk	'Differential gene expression analysis for bulk RNA-seq with PyDESeq2' carves out a clear niche with distinct triggers unlikely to conflict with other skills.	3 / 3
	Total	10 / 12 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 (577 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/pydeseq2/SKILL.md
Commit: 757b63b

Reviewed: about 11 hours ago

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