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veomni-new-model

Use this skill when adding support for a new model to VeOmni. Covers the full lifecycle: analyzing the HuggingFace model, creating model patches, defining parallel plans, writing configs, integrating with the trainer, and testing. Trigger: 'add model', 'support new model', 'integrate a model', 'new model support'.

74

Quality

91%

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SKILL.md
Quality
Evals
Security

Quality

Content

90%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 high-quality, actionable workflow document with lean prose and concrete commands, paths, and patterns. The main gaps are the absence of an explicit validation feedback loop and some detail that could be externalized into reference files.

Suggestions

Add an explicit validate→fix→retry loop in Phase 6 (e.g., 'if make quality or pytest fails, review errors, fix the patch_gen_config, re-run make patchgen, then re-test').

Move the detailed VLM multimodal-metadata checklist and the patch-pattern reference into separate files under references/, keeping SKILL.md as an overview with one-level-deep links.

Include one minimal worked example of a *_patch_gen_config.py snippet so the central artifact is copy-paste ready.

DimensionReasoningScore

Conciseness

Lean and dense: phase headers, numbered steps, concrete paths and commands, with no padding explaining concepts Claude already knows (FSDP, MoE, patchgen). Every token earns its place.

5 / 5

Actionability

Gives concrete commands ('make patchgen', 'make quality', 'pytest tests/models/'), exact file paths and registry names, specific patch-pattern shapes, and explicit reference models to copy, making it fully actionable for an instruction-heavy skill.

5 / 5

Workflow Clarity

A clearly sequenced six-phase workflow with TodoWrite status tracking and a dedicated Test phase running 'make quality' and 'pytest', but it lacks an explicit validate→fix→retry feedback loop.

4 / 5

Progressive Disclosure

Well-organized into phases with one-level-deep signaled references ('.agents/knowledge/multimodal_metadata.md'), but some inline detail such as the VLM metadata checklist and patch patterns could be split into reference files; no bundle files exist.

4 / 5

Total

18

/

20

Passed

Description

92%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, specific description that clearly states both the full lifecycle of work and explicit trigger phrases. The only minor gap is trigger-term synonym breadth, which keeps trigger quality at 4 rather than 5.

DimensionReasoningScore

Specificity

Lists six concrete actions ('analyzing the HuggingFace model, creating model patches, defining parallel plans, writing configs, integrating with the trainer, and testing'), giving comprehensive coverage of the lifecycle rather than vague language.

5 / 5

Completeness

Explicitly answers what ('Covers the full lifecycle...') and when ('Use this skill when...' plus a 'Trigger:' line with concrete phrases), matching the anchor for clearly answering both.

5 / 5

Trigger Term Quality

Provides four natural trigger phrases ('add model', 'support new model', 'integrate a model', 'new model support'), but they are close variations on one concept and omit synonyms like 'port'/'implement a model' or file extensions.

4 / 5

Distinctiveness Conflict Risk

Scoped to a clear niche ('adding support for a new model to VeOmni') with distinct triggers, giving minimal overlap risk with other skills.

5 / 5

Total

19

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
ByteDance-Seed/VeOmni
Reviewed

Table of Contents

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