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pysam

Provides Python/HTSlib workflows for genomic files. Used when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.

75

Quality

94%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

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

A strong reference-style skill body: fully executable code, high-signal pysam-specific knowledge, verified one-level-deep progressive disclosure, and a decision checklist with real validation checkpoints. The only deductions are minor: inline version/date stamps outside a migration section and the absence of an explicit validate-and-retry loop for write workflows.

Suggestions

Consolidate version-sensitive details (the 0.24.1 release date and behavior-change list) into the existing 'references/migration_to_0_24.md' and keep only the pinned version number inline, so time-sensitive information lives in a dedicated section per the conciseness guideline.

Add a short validate -> fix -> retry loop for the write/filter workflow (e.g., after writing, run quickcheck and reopen the output; on failure, fix header/reference issues and rewrite) to raise workflow clarity to the anchor-5 pattern.

Trim the quickcheck writing-rule bullet to one line plus a pointer to the samtools contract, moving the full readability verification recipe into a reference file.

DimensionReasoningScore

Conciseness

The body is dense and assumes Claude's competence — nearly every sentence carries pysam-specific knowledge (coordinate conventions, defaults, 0.24 behavior changes) rather than general explanations. However, time-sensitive version/date stamps ('pysam 0.24.1 (7 September 2026)') appear inline in both Overview and Installation rather than being confined to a migration/deprecated section, and the quickcheck writing-rule bullet and citation block could be trimmed or pushed to a reference, matching anchor 4's 'minor instances that could be trimmed' rather than anchor 5's 'every token earns its place'.

4 / 5

Actionability

All guidance is fully executable and copy-paste ready: pinned install command, script invocations with flags, context-manager code for every format family, concrete fetch/pileup/tabix/dispatcher examples, and an exception-handling pattern for SamtoolsError. Specific examples cover the common cases, matching anchor 5; it is above anchor 4 because there are no pseudocode or missing-detail gaps.

5 / 5

Workflow Clarity

The 'First Decide' numbered checklist, Coordinate Contract, and Writing Rules give a clear sequenced decision process with real checkpoints (quickcheck preflight, reopen outputs before downstream use, scripts refuse to overwrite or accept stale indexes). It falls short of anchor 5 because there is no explicit validate -> fix -> retry feedback loop for write/filter workflows — validation is stated as precautions rather than an error-recovery cycle.

4 / 5

Progressive Disclosure

The body is a clean overview: concise per-topic sections each signaling their one-level-deep reference, a Bundled Scripts table, and a Reference Map table mapping needs to files. All nine referenced markdown files and four scripts exist, and references point only back to scripts (no nested reference chains), matching anchor 5's 'well-signaled one-level-deep references' exactly.

5 / 5

Total

18

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20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An excellent description: concrete actions, comprehensive natural trigger terms spanning every supported format, explicit 'Used when...' trigger guidance, third-person voice, and a clearly distinct niche. No weaknesses identified.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions ('reading, querying, filtering, or writing', 'pileup, coverage, indexing, and CRAM references') with comprehensive coverage across all supported format families, matching the anchor-5 example's breadth. It uses third-person voice ('Provides', 'Used when') with no vague filler, so it is above anchor 4 which allows minor coverage gaps.

5 / 5

Completeness

It explicitly answers both questions: what ('Provides Python/HTSlib workflows for genomic files') and when ('Used when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam'), with concrete trigger phrases. This matches the anchor-5 exemplar structure exactly; anchor 4 would require the 'when' clause to be less explicit or specific.

5 / 5

Trigger Term Quality

Natural user vocabulary is comprehensively present: 'SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, tabix' (format names that double as file extensions), 'pysam', 'pileup', 'coverage', 'indexing'. This exceeds the anchor-4 example ('PDF files, forms, document extraction') because the format acronyms cover the extension-space users would naturally say.

5 / 5

Distinctiveness Conflict Risk

The description is pinned to a clear niche — pysam/HTSlib access to genomic file formats — with format-specific triggers unlikely to fire for unrelated skills. Anchor 4 ('minor overlap risk with closely related skills') does not apply since the tool name plus format list disambiguates it from generic file-processing skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

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