cancer-genomics-analysis

Computational cancer genomics workflows. Somatic mutation detection and annotation, structural variation characterization, copy number analysis, tumor purity/ploidy estimation, NMF metagene extraction, and DNA damage response network analysis. For cancer mutation databases use cosmic-database; for variant clinical significance use clinvar-database.

Quality

73%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./backend/cli/skills/biology/cancer-genomics-analysis/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 for every capability, but it is a monolithic ~530-line document that ignores its own scripts/ bundle and lacks validation feedback loops in its multi-step workflows.

Suggestions

Replace inlined code with one-level-deep references to the existing scripts/ files (e.g. 'See scripts/parse_vcf.py') to enforce progressive disclosure and avoid duplication.

Add explicit validate-then-retry checkpoints to each Typical Workflow (e.g. re-run FilterMutectCalls if FilterRec records remain, or assert PASS-only counts before annotation).

Trim or move the duplicated Quick Start / Core Capabilities code now that scripts/ holds the canonical implementations.

Dimension	Reasoning	Score
Conciseness	The ~530-line body is mostly efficient executable code, but it duplicates logic that already exists in the scripts/ bundle and could be tightened by pointing to those files rather than inlining everything.	2 / 3
Actionability	Provides complete, executable, copy-paste-ready code throughout — e.g. parse_vcf, run_mutect2, cnvkit_pipeline, extract_metagenes, calculate_tmb — with concrete function signatures and real library calls.	3 / 3
Workflow Clarity	The 'Typical Workflows' are numbered and sequenced, but batch/bioinformatics operations lack explicit validation checkpoints or fix-and-retry feedback loops, so per the rubric workflow clarity is capped at 2.	2 / 3
Progressive Disclosure	Sections are organized, but code that should live in the provided scripts/ bundle (parse_vcf.py, calculate_tmb.py, ddr_network.py, nmf_metagenes.py) is inlined instead and those bundle files are never referenced from the body.	2 / 3
	Total	9 / 12 Passed

Description

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.

The description is specific, well-scoped, and explicitly distinguishes itself from sibling database skills. Its main gap is the absence of an explicit 'Use when...' trigger clause, which leaves the activation condition implicit.

Suggestions

Add an explicit 'Use when...' clause naming common user phrasing (e.g. 'Use when processing somatic VCFs, running CNVkit, or estimating TMB') to lift completeness.

Include a few broader natural trigger terms like 'variants', 'VCF', or 'sequencing' that users may say alongside the specialist vocabulary.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete capabilities — 'Somatic mutation detection and annotation, structural variation characterization, copy number analysis, tumor purity/ploidy estimation, NMF metagene extraction, and DNA damage response network analysis' — matching the multiple-specific-actions anchor.	3 / 3
Completeness	Clearly answers 'what' via the capability list, but there is no explicit 'Use when...' trigger clause; only database routing ('For cancer mutation databases use cosmic-database'), so per the guideline completeness is capped at 2.	2 / 3
Trigger Term Quality	Uses natural domain terms a cancer-genomics user would say — 'somatic mutation', 'structural variation', 'copy number', 'tumor purity/ploidy', 'NMF metagene', 'DNA damage response' — giving good coverage of likely user phrasing.	3 / 3
Distinctiveness Conflict Risk	Explicit routing — 'For cancer mutation databases use cosmic-database; for variant clinical significance use clinvar-database' — carves a clear niche and reduces conflict with sibling database skills.	3 / 3
	Total	11 / 12 Passed

Validation

81%

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 — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (543 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/cancer-genomics-analysis/SKILL.md
Commit: 52845c3

Reviewed: about 10 hours ago

Table of Contents

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