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.
71
66%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/pydeseq2/SKILL.mdQuality
Discovery
82%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong, domain-specific description with excellent specificity and trigger term coverage for bioinformatics users. Its main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know precisely when to select this skill. The technical terms are appropriate for the target audience and create a highly distinctive skill profile.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about differential gene expression, DESeq2 analysis, bulk RNA-seq count data, or generating volcano/MA plots from RNA-seq experiments.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots'. These are precise, domain-specific analytical steps. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific actions and methods, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied by the domain terms. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords a bioinformatician would use: 'differential gene expression', 'DESeq2', 'RNA-seq', 'bulk RNA-seq counts', 'Wald tests', 'FDR correction', 'volcano plots', 'MA plots', 'DE genes'. Good coverage of domain-specific terms users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche: DESeq2-based differential gene expression from bulk RNA-seq is very specific and unlikely to conflict with other skills. The combination of Python, DESeq2, and RNA-seq creates a clear, narrow domain. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is highly actionable with excellent, executable code examples covering the full PyDESeq2 workflow. However, it is significantly too verbose—it reads more like comprehensive documentation than a concise skill file, with redundant sections (Quick Start vs Core Workflow), unnecessary explanations of concepts Claude already knows, and inline content that should be in reference files. Adding validation checkpoints between workflow steps would improve reliability.
Suggestions
Cut the content by 50-60%: remove 'When to Use This Skill', 'Overview' prose, 'What deseq2() does' internals, and merge Quick Start with Core Workflow into a single concise workflow section.
Move visualization code, troubleshooting, common analysis patterns, and result interpretation to reference files, keeping only the core workflow and key reminders in SKILL.md.
Add explicit validation checkpoints: verify counts shape/dtype after loading, confirm sample index alignment before DeseqDataSet creation, and check results_df is non-empty before filtering.
Remove explanations of basic concepts (what padj means, what volcano plots show, what Benjamini-Hochberg does) since Claude already knows these.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~400+ lines. It explains concepts Claude already knows (what design formulas are, what p-values mean, what volcano plots show), includes redundant sections (Quick Start duplicates Core Workflow), and has unnecessary commentary like 'What deseq2() does' listing internal steps. The 'When to Use This Skill' section is entirely unnecessary filler. | 1 / 3 |
Actionability | The skill provides fully executable, copy-paste ready Python code throughout, including data loading, filtering, model fitting, statistical testing, visualization, and troubleshooting. Code examples are complete with proper imports and realistic patterns. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but there are no explicit validation checkpoints between steps. For a multi-step pipeline involving data transformation and statistical analysis, there should be validation steps (e.g., verify counts are non-negative integers, confirm sample alignment after filtering, validate design matrix before fitting). The troubleshooting section is reactive rather than preventive. | 2 / 3 |
Progressive Disclosure | References to external files (api_reference.md, workflow_guide.md, scripts/run_deseq2_analysis.py) are present and clearly signaled, but the main SKILL.md contains far too much inline content that should be in reference files—visualization code, troubleshooting, multiple analysis patterns, and result interpretation could all be offloaded to keep the main file lean. | 2 / 3 |
Total | 8 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (558 lines); consider splitting into references/ and linking | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 9 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
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