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borzoi

Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.

68

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

82%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 tight, competent body: efficient tables and code, a clearly sequenced remote-compute workflow with an explicit notification checkpoint, and no bundle-file sprawl. The main gaps are a missing minimal `borzoi_run.py` / input-construction example and absent output validation, which hold actionability and workflow clarity just below top marks.

Suggestions

Include a minimal `borzoi_run.py` (or the one-hot input construction) so the remote-job example is fully copy-paste executable.

Add an explicit output validation step, e.g. check `tracks.npz` shape and track count against the expected (B, T, L) format, after `compute_done`.

Show a two-line ref/alt variant-scoring code sketch to make the primary use case (1) in the description concrete.

DimensionReasoningScore

Conciseness

The body is lean throughout — dense prerequisite and troubleshooting tables, minimal code, and gotcha notes like "the base `Borzoi` model has no `targets` attribute" that add non-obvious value while assuming Claude's competence; no section explains concepts Claude already knows.

5 / 5

Actionability

Mostly executable guidance: concrete `from_pretrained` loading, a full `submit_job` call with real arguments, and `attach_job(job_id).result()` for re-entry. However `borzoi_run.py` is passed as a job input without its contents ever being shown, input one-hot construction is only specified as a shape comment, and variant scoring is described ("run ref/alt windows centred on the variant") rather than coded — minor gaps, not fully copy-paste ready.

4 / 5

Workflow Clarity

The remote-compute sequence is clear (read compute_details → submit → "call the `wait_for_notification` brain-tool" → "when the `compute_done` notification arrives, act on its payload" → save_artifacts → attach_job), with the non-blocking checkpoint made explicit. No validation of the output artifact (e.g., checking `tracks.npz` shape or track count) is included, which keeps it below the explicit-validation anchor.

4 / 5

Progressive Disclosure

No bundle files exist and the ~77-line body is cleanly sectioned (Prerequisites, How to run, Output format, Remote compute, Troubleshooting), with orchestration detail appropriately deferred to well-signaled skills (`remote-compute-ssh` / `remote-compute-modal`). It exceeds the under-50-lines simple-skill case and carries the full remote-compute protocol inline, so minor organization gaps remain rather than an ideal split.

4 / 5

Total

17

/

20

Passed

Description

88%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 strong description: third-person, concise, with an explicit what and a three-item when clause covering concrete use cases. Keyword coverage is good but could add common synonyms, and the non-coding prioritisation trigger has some overlap risk with other variant-scoring skills.

Suggestions

Add natural synonyms users would say, e.g. "variant effect" or "chromatin accessibility", to broaden trigger-term coverage.

Sharpen the non-coding prioritisation trigger to tie it to predicted-track evidence specifically, reducing overlap with general variant-effect predictors.

DimensionReasoningScore

Specificity

"Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence" names the domain, concrete action, and specific assays, and the three enumerated use cases ("Scoring the regulatory effect of a variant", "Generating predicted coverage tracks for a locus", "Prioritising non-coding variants by predicted track delta") give comprehensive concrete coverage — matching the multiple-specific-actions anchor, not the minor-gaps anchor below it.

5 / 5

Completeness

It explicitly answers both questions: the first sentence states what the skill does, and "Use this skill when: (1)... (2)... (3)..." gives three concrete trigger phrases — a direct match for the anchor requiring clear what AND when with concrete triggers.

5 / 5

Trigger Term Quality

Good natural keyword coverage ("regulatory effect", "expression/accessibility", "coverage tracks", "non-coding variants") but common variations like "variant effect", "chromatin accessibility", or "predicted RNA-seq" phrasing are missing, so it falls short of the comprehensive-synonyms anchor at 5.

4 / 5

Distinctiveness Conflict Risk

Naming Borzoi and track types (RNA-seq, CAGE, DNase, ChIP) carves a mostly distinct niche, but "Prioritising non-coding variants by predicted track delta" overlaps with other variant-effect predictors (the body itself suggests combining with evo2), so minor overlap risk with closely related skills remains.

4 / 5

Total

18

/

20

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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