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veomni-new-model

Use this skill when adding support for a new model to VeOmni. Owns the lifecycle around the modeling itself: analyzing the HuggingFace model, choosing the category, the training config, trainer and data-pipeline integration, tests and docs. The modeling patch itself is delegated to /veomni-patchgen-model. Trigger: 'add model', 'support new model', 'integrate a model', 'new model support'.

70

Quality

88%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

88%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 well-structured, highly actionable five-phase workflow tailored to the VeOmni codebase, with concrete paths, commands, and validation checkpoints throughout. The main improvements are trimming the repeated handoff messaging and moving the longest inline checklists into reference files.

DimensionReasoningScore

Conciseness

Dense, project-specific guidance with no explanations of concepts Claude already knows, but the patchgen handoff is restated three times (header quote, Phase 2, Common Pitfalls) and the Phase 2 parallel_plan.py note is somewhat discursive, keeping it just below the 'every token earns its place' anchor.

4 / 5

Actionability

Gives exact repo paths (configs/text/<model_name>.yaml, tests/models/test_model_registry.py, veomni/data/data_collator.py), runnable commands (make quality, pytest tests/models/), and a concrete decision rule for checkpoint key conflicts — fully actionable instruction-only guidance with no vague steps.

5 / 5

Workflow Clarity

Five explicitly sequenced phases with a todo-tracking plan, an explicit Phase 2 return gate ('once the model loads and its registry / patch tests pass'), a user-decision checkpoint for key conflicts with strict-loading round-trip validation, and Phase 5 verification commands — clear sequence with explicit validation and feedback loops.

5 / 5

Progressive Disclosure

Clear sectioned structure with modeling detail delegated to /veomni-patchgen-model and one-level-deep, purpose-described references to .agents/knowledge files, but the body inlines fairly detailed material (the VLM metadata checklist, the parallel_plan.py explainer) that could live in reference files, and no references/ bundle is provided.

4 / 5

Total

18

/

20

Passed

Description

83%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 that explicitly covers what the skill does, when to use it, and how it boundaries against its sibling modeling skill, with natural trigger phrases. It falls just short of top marks on specificity and distinctiveness due to slightly abstract framing and generic trigger terms that could collide with the sibling patchgen skill.

DimensionReasoningScore

Specificity

Lists several concrete actions ('analyzing the HuggingFace model, choosing the category, the training config, trainer and data-pipeline integration, tests and docs') with only minor gaps — the abstract framing 'Owns the lifecycle around the modeling itself' and noun-phrase items keep it below the comprehensive anchor 5.

4 / 5

Completeness

Clearly answers both 'what' (the enumerated lifecycle steps and explicit delegation of the modeling patch) and 'when' ('Use this skill when adding support for a new model to VeOmni' plus concrete trigger phrases), matching the top anchor exactly.

5 / 5

Trigger Term Quality

Explicit triggers ('add model', 'support new model', 'integrate a model', 'new model support') are natural phrases a user would say, giving good coverage; a few natural synonyms (e.g. 'onboard a model') are missing, so it does not reach the comprehensive anchor 5.

4 / 5

Distinctiveness Conflict Risk

Scoped to a named repo and explicitly delimited against /veomni-patchgen-model, but generic trigger phrases like 'add model' carry minor overlap risk with that closely related sibling skill — matching 'mostly distinct; minor overlap risk' rather than the minimal-conflict anchor 5.

4 / 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
ByteDance-Seed/VeOmni
Reviewed

Table of Contents

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