cnv-caller-plotter

Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, tumor-normal comparison, and BED format export for cancer genomics and rare disease analysis.

Quality

73%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/Data analysis/cnv-caller-plotter/SKILL.md

Quality

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 well-crafted description with excellent specificity and domain-appropriate trigger terms for bioinformatics users. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The technical depth and distinct niche make it highly distinguishable from other skills.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user mentions CNV analysis, copy number calling, WGS data analysis, or needs genome-wide CNV visualization.'

Consider adding file format triggers like '.bam', '.vcf', or 'sequencing reads' to capture more natural user queries.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'Detect copy number variations', 'generate publication-quality genome-wide CNV plots', 'CNV calling', 'segmentation', 'tumor-normal comparison', and 'BED format export'. These are precise, actionable capabilities.	3 / 3
Completeness	Clearly answers 'what does this do' with specific capabilities and use cases (cancer genomics, rare disease analysis), but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied through domain context.	2 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'copy number variations', 'CNV', 'whole genome sequencing', 'tumor-normal', 'BED format', 'cancer genomics', 'rare disease'. Good coverage of domain-specific terms bioinformaticians would use.	3 / 3
Distinctiveness Conflict Risk	Highly specialized niche in CNV analysis from WGS data. The specific combination of CNV detection, genome-wide plots, tumor-normal comparison, and BED export creates a distinct profile unlikely to conflict with other bioinformatics skills.	3 / 3
	Total	11 / 12 Passed

Implementation

64%

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

This skill provides solid actionable guidance with executable code examples and comprehensive CLI documentation. However, it's somewhat verbose with explanatory content Claude doesn't need, and the workflow lacks explicit validation checkpoints critical for genomics pipelines handling patient data. The prominent placeholder warnings are appropriate and well-placed.

Suggestions

Add explicit validation checkpoints in the workflow (e.g., 'Verify BAM index exists before proceeding', 'Confirm coverage meets minimum threshold before CNV calling')

Remove or condense the 'Key Capabilities' bullets and 'Common Pitfalls' section - Claude knows these genomics concepts

Consider splitting detailed content (tumor-normal comparison, parameter reference) into separate files with clear links from the main skill

Dimension	Reasoning	Score
Conciseness	The skill is reasonably efficient but includes some unnecessary explanatory content (e.g., the 'Key Capabilities' bullet list largely restates what's obvious from the title). The tables and structured content are helpful, but some sections like 'Common Pitfalls' explain concepts Claude would know.	2 / 3
Actionability	Provides fully executable Python code examples and CLI commands that are copy-paste ready. The parameter table is complete with types and requirements, and the code snippets show actual usage patterns with realistic file paths and method calls.	3 / 3
Workflow Clarity	The workflow section lists steps but lacks explicit validation checkpoints between steps. For a genomics tool processing potentially sensitive data with destructive operations, there should be clearer verify-then-proceed gates. The fallback handling is good but validation loops are implicit.	2 / 3
Progressive Disclosure	Content is well-organized with clear sections, but everything is in a single monolithic file. The References section links to external databases but there's no indication of separate detailed documentation files for advanced features like tumor-normal comparison or the API reference.	2 / 3
	Total	9 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

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

	Total	10 / 11 Passed

Repository: aipoch/medical-research-skills
Commit: ca9aaa4

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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