geniml

Use Geniml for audited local genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.

Quality

83%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

92%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.

The body is a strong, version-pinned procedural skill: executable code, explicit validation gates and feedback loops for destructive/batch operations, and a clean one-level-deep reference structure. The only minor weaknesses are occasional verbosity in migration notes and placeholder CLI syntax in the consensus section.

Dimension	Reasoning	Score
Conciseness	Dense and information-rich, assuming Claude's competence without explaining basics (BED, AnnData, tokenizers), though a few migration/snapshot sections could be tightened slightly.	4 / 5
Actionability	Provides copy-paste-ready bash and Python covering common cases (install, validate, tokenize, train Region2Vec/scEmbed, audit, inspect), with concrete module paths and CLI flags; only the consensus CLI uses placeholder brace syntax.	5 / 5
Workflow Clarity	Sequences risky operations with explicit validation gates: a numbered 7-step safety-gate checklist, BED validation before analysis, checksum-before-load rules for checkpoints, and approve-before-download gates with feedback loops.	5 / 5
Progressive Disclosure	Clear overview body with a dedicated '## References' section linking five real, one-level-deep reference files, each topic appropriately split with inline essentials pointing to details.	5 / 5
	Total	19 / 20 Passed

Description

75%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.

The description is specific, distinct, and densely packed with concrete capabilities, but it omits an explicit 'Use when...' trigger clause, so the 'when' dimension is only weakly implied. Adding concrete trigger phrases would raise completeness and trigger-term quality.

Suggestions

Add an explicit 'Use when...' clause naming natural trigger phrases (e.g., 'Use when working with BED files, building consensus peaks, or training/validating Region2Vec or scEmbed models on genomic intervals').

Include file extensions or common synonyms users might say (e.g., '.bed', 'universe BED', 'peak sets') to broaden natural keyword coverage.

Consider a second sentence that separates the 'what' (capabilities) from the 'when' (trigger contexts) more clearly.

Dimension	Reasoning	Score
Specificity	Lists four concrete, domain-specific actions ('validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes') with comprehensive coverage of the skill's scope.	5 / 5
Completeness	Clearly answers 'what' the skill does, but provides no explicit 'when'/'Use when...' trigger guidance, capping completeness at 3 per the rubric guideline.	3 / 5
Trigger Term Quality	Uses the canonical domain keywords users would say (BED, Region2Vec, scEmbed, consensus universes, tokenizer), but lacks natural-language synonyms, file extensions, or varied phrasings a non-expert might utter.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear, narrow niche (Geniml genomic-interval ML workflows) with distinct named-tool triggers, making conflict with other skills minimal.	5 / 5
	Total	17 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/geniml/SKILL.md
Commit: 49c6e97

Reviewed: 9 days ago

Table of Contents

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