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evo2

Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring regulatory or coding regions across species.

76

Quality

96%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

92%

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 and token-efficient, with executable code, concrete tables, a routing decision tree, and a sequenced remote-compute workflow backed by a troubleshooting feedback loop. Its main weakness is structure rather than content: everything lives in one file with no progressive disclosure of the detailed remote-compute and troubleshooting material into separate references.

Suggestions

Move the remote-compute orchestration (submit/wait/attach flow and HF_HOME/HF_HUB_OFFLINE notes) into a references/ file such as REMOTE_COMPUTE.md, leaving a one-line pointer in SKILL.md so the main file reads as an overview.

Extract the troubleshooting and typical-performance tables into a references/ file (e.g. TROUBLESHOOTING.md) referenced from the body, reducing inline tokens while keeping error-recovery guidance one level deep.

If keeping the skill single-file, add an explicit validation checkpoint for batch scoring (e.g. 'confirm ll values are finite and in expected range before computing Δll') to strengthen the workflow feedback loop.

DimensionReasoningScore

Conciseness

The body is lean — prerequisites/install/scoring/generation are given as compact tables and executable snippets, and prose only adds non-obvious domain guidance (e.g. 'More negative ⇒ less likely', HF_HOME/HF_HUB_OFFLINE rationale). It assumes Claude's competence and avoids explaining what DNA or libraries are, matching the 'every token earns its place' anchor.

3 / 3

Actionability

Provides copy-paste-ready executable code ('from evo2 import Evo2', 'model.score_sequences(seqs)', 'model.generate(...)') plus concrete install, model-selection, and remote-compute submission snippets with specific VRAM/timing numbers. Fully instructs rather than describes; matches the level-3 anchor.

3 / 3

Workflow Clarity

Sequences are explicit: a decision tree routes to the right model, and the remote-compute flow is step-sequenced (submit_job → wait_for_notification → save_artifacts → attach_job). The troubleshooting table supplies a symptom→cause→fix error-recovery feedback loop. Not level 2 because checkpoints (notification, error recovery) are present rather than implicit; operations are non-destructive inference so strict validate-before-proceed is less critical.

3 / 3

Progressive Disclosure

Well-organized into clear headered sections, but it is a monolithic ~125-line file with no bundle references (references/scripts/assets absent); detailed material such as the remote-compute orchestration and the troubleshooting/performance tables is inline where separate reference files would let SKILL.md act as an overview. Fits the level-2 anchor ('content that should be separate is inline') rather than level 3, which expects well-signaled one-level-deep references and appropriately split content.

2 / 3

Total

11

/

12

Passed

Description

100%

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 strong description: it states concrete capabilities in third person and provides an explicit, enumerated 'Use this skill when' trigger covering four distinct scenarios. It is specific, complete, and clearly scoped to a DNA foundation-model niche with low conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Score, embed, and generate DNA sequences' — elaborated across four enumerated capabilities (likelihoods, embedding, generation, cross-species scoring). Not vague; matches the 'lists multiple specific concrete actions' anchor rather than the domain-only level 2.

3 / 3

Completeness

Explicitly answers both what ('Score, embed, and generate DNA sequences with Evo 2') and when ('Use this skill when: (1)…(4)'). Matches the level-3 anchor with explicit triggers; not level 2 since the 'when' is stated, not merely implied.

3 / 3

Trigger Term Quality

Uses natural domain terms a genomics user would say — 'variant effect scoring', 'embedding genomic windows', 'generating DNA', 'scoring regulatory or coding regions' — with good coverage. Not reduced to 2 because it avoids pure jargon and captures the common phrasings users would actually request.

3 / 3

Distinctiveness Conflict Risk

Clear DNA language-model niche with triggers unlikely to fire for non-DNA skills; the body's decision tree even distinguishes it from borzoi and esm2. Not level 2 because the scope is tightly scoped to DNA likelihood/embedding/generation rather than overlapping 'document/code' generics.

3 / 3

Total

12

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository
UnicomAI/wanwu
Reviewed

Table of Contents

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