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geniml

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

Quality

84%

Does it follow best practices?

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SecuritybySnyk

Passed

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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 highly actionable, well-structured body: executable pinned commands and code, an explicit safety-gate sequence with validation checkpoints, and clean one-level-deep progressive disclosure to real bundle files. The only deductions are minor trimmable over-explanations and the lack of an explicit fix-and-revalidate feedback loop.

Suggestions

Trim explanations of facts Claude already knows, e.g. compress 'start is included, end is excluded, and length is end - start' to just 'BED is 0-based, half-open [start, end)'.

Add an explicit error-recovery loop to the validation steps, e.g. 'If bed_validator.py reports proposed fixes, apply them to the source BED and re-run until it reports no violations before proceeding to tokenization or consensus planning.'

DimensionReasoningScore

Conciseness

The body is dense and fluff-free — nearly every sentence carries geniml-specific contracts, version pins, or pitfalls — but minor over-explanations could be trimmed, e.g. restating 'start is included, end is excluded, and length is end - start' for BED's 0-based half-open semantics, and dated version detail (2026-01-14, 2026-09-05) sits in the main flow rather than wholly in the snapshot/migration sections. This fits the 4 anchor (efficient, minor trimmable instances) rather than the 5 anchor's every-token-earns-its-place.

4 / 5

Actionability

Guidance is fully executable throughout: pinned 'uv pip install "geniml==0.8.4"' commands, copy-paste invocations like 'python skills/geniml/scripts/bed_validator.py --input data/peaks.bed --assembly GRCh38 --chrom-sizes refs/GRCh38.chrom.sizes', and complete Python snippets with imports, parameters, and concrete module paths covering the common cases. This matches the 5 anchor; the only non-literal command ('geniml build-universe {cc,ccf,ml,hmm} ...') is deliberately deferred to a clearly signaled reference.

5 / 5

Workflow Clarity

Sequencing and checkpoints are strong: a numbered 7-item safety gate runs first, 'Run a bounded validation and normalization plan before analysis' precedes analysis, and gates like 'Do not concatenate or generate coverage until all BED files pass the same assembly contract' block invalid progress. It falls short of the 5 anchor because there is no explicit error-recovery feedback loop (e.g., 'if the validator reports errors, fix the BED and re-run') — checkpoints exist but the fix-and-revalidate retry path is only implicit.

4 / 5

Progressive Disclosure

The body is a clear overview (install, safety gate, contracts, API map) that defers detail to five one-level-deep references, each well signaled inline ('See [references/region2vec.md] for export, encoding, legacy CLI, and evaluation details') and in an annotated References section; all five referenced files and all six referenced scripts exist in the bundle, matching the 5 anchor for a well-signaled, appropriately split structure.

5 / 5

Total

18

/

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.

A specific, third-person, low-conflict description with concrete verbs covering the skill's main workflows. Its main weakness is the absence of any 'Use when...' trigger clause, leaving the 'when to use' question unanswered and capping completeness at 3.

Suggestions

Append an explicit trigger clause such as 'Use when the user mentions Geniml, Region2Vec, scEmbed, genomic intervals or regions, BED/universe files, tokenizers, or building/assessing consensus peaks.'

Add natural synonyms and file formats users say — 'tokenize', 'peaks', 'genomic regions', '.bed', 'consensus peaks' — to improve trigger-term coverage from good to comprehensive.

DimensionReasoningScore

Specificity

The description lists four concrete actions — 'validate BED and universe contracts', 'plan Region2Vec or scEmbed runs', 'inspect model/tokenizer compatibility', and 'assess consensus universes' — which comprehensively cover the skill's primary modern workflows, matching the 5 anchor for multiple specific concrete actions.

5 / 5

Completeness

The 'what' is explicit via the colon-listed actions, but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines. It is not 2 because the 'what' is concrete and specific rather than vague.

3 / 5

Trigger Term Quality

Natural domain terms are present (Geniml, BED, Region2Vec, scEmbed, consensus universes, model/tokenizer), but common variations a user might say — 'tokenize', 'peaks', 'genomic regions', '.bed', 'build universe' — are missing, fitting the 4 anchor of good coverage with a few natural terms missing rather than the 5 anchor's synonym/extension completeness.

4 / 5

Distinctiveness Conflict Risk

The description names a clear niche (Geniml genomic-interval ML) with tool-specific triggers (Region2Vec, scEmbed, BED universes) that no generic skill would match, satisfying the 5 anchor for minimal conflict risk.

5 / 5

Total

17

/

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

Path

skills/geniml/SKILL.md

Reviewed

Table of Contents

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