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omniverse-usd-performance-tuning

Top-level workflow skill for USD performance diagnosis and optimization. Use for slow loading, high memory, low FPS, or 'optimize my scene' requests; delegates auth/runtime setup to Phase 0 owners.

56

Quality

71%

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tessl review fix ./plugins/nvidia/skills/omniverse-usd-performance-tuning/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 dense, well-structured orchestrator body with genuinely actionable routing policy, explicit validation and approval checkpoints, and a real, well-organized bundle behind it. The main costs are duplication (the when-to-use paragraph and labeling rules stated multiple times) and inconsistent, deeply nested reference paths that would trip an agent resolving them from the bundle root.

Suggestions

Delete the duplicated 'Use this workflow for broad performance asks...' paragraph (it appears in both 'When to Use' and after 'Output Format') and consolidate the repeated profile-stage:baseline/after labeling rule into a single stated-once contract.

Normalize all reference paths to one resolvable base (e.g., references/...) — the 'skills/omniverse-usd-performance-tuning/references/...' form does not resolve from the bundle root and risks the agent failing to load the runtime-context-header gate.

Break the 10-line run-on paragraph in 'Output Format' into a short bullet contract (entry skill, decision, milestone chain, report schema) so the milestone subsequence is scannable rather than buried mid-paragraph.

DimensionReasoningScore

Conciseness

Mostly efficient routing policy rather than concept teaching, but with real redundancy: the 'Use this workflow for broad performance asks such as slow loading, low FPS, high memory, GPU crashes...' paragraph appears twice near-verbatim (When to Use, and again after Output Format), and the profile-stage:baseline/after labeling rule is repeated across three sections. Anchor 2 would require pervasive padding, which is not the case; anchor 4 requires only minor trimmable instances, while these are whole duplicated passages.

3 / 5

Actionability

For an instruction-only orchestrator skill the guidance is highly concrete: exact milestone subsequence tokens, exact decision values (ready_to_plan, approval_required, blocked_missing_scene_optimizer), exact reference file paths, and specific op-curation rules (prefer meshCleanup over mergeVertices, deduplicateGeometry over findCoincidingGeometry). It falls short of anchor 5 because several directives remain abstract ('gather minimum context', 'keep raw runtime artifacts on disk') with mechanics deferred elsewhere.

4 / 5

Workflow Clarity

The sequence is explicit with validation checkpoints ('Always validate before and after processor execution'), a mandatory runtime gate before any output, approval gates for destructive operations, and a Troubleshooting section with recovery paths — so the destructive/batch validation cap does not apply. It is not anchor 5 because the flow is scattered across five separate sections (Instructions, Output Format, Entry skill rule, Triage order, Required execution flow) instead of one coherent ordered procedure, with the authoritative order deferred to references/workflow.md.

4 / 5

Progressive Disclosure

Verified against the actual bundle: the 130-file references/ tree is well organized, cited files exist, and detailed material (workflow, op curation, runtime token budget, report templates) is correctly split out of SKILL.md. Not anchor 5 because the reference paths are inconsistent — some use 'references/...' while others use 'skills/omniverse-usd-performance-tuning/references/...', which do not resolve from the bundle root — and some chains nest three 'references/' levels deep (e.g., usd-structure-assessment/references/instancing-readiness/references/instancing-tradeoffs.md), making navigation error-prone.

4 / 5

Total

15

/

20

Passed

Description

70%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 solid orchestrator-style description: it cleanly separates what from when, uses natural trigger phrases users would say, and explicitly marks boundaries against sibling skills. The main weakness is an abstract 'what' clause that never states the concrete actions or deliverables of the workflow.

Suggestions

State 2-3 concrete capabilities in the 'what' clause, e.g., 'Profiles USD stages, runs validation and Scene Optimizer operations, and produces a structured optimization report.'

Add USD-qualifying synonyms to the trigger list (e.g., 'slow USD loading', 'scene won't load', '.usd/.usda/.usdc performance') to reduce overlap with generic performance skills.

Replace internal jargon like 'Phase 0 owners' with the actual skill name (setup-usd-performance-tuning) so the boundary is actionable to the agent reading only the description.

DimensionReasoningScore

Specificity

The description names the domain ("USD performance diagnosis and optimization") and only 1-2 generic actions (diagnosis, optimization, delegation of setup), matching the anchor for '1-2 concrete actions, but not comprehensive'. It never lists the concrete capabilities of the workflow (profiling, restructuring, validator runs, report generation), so it does not reach anchor 4.

3 / 5

Completeness

Both 'what' ("Top-level workflow skill for USD performance diagnosis and optimization") and 'when' (explicit "Use for" clause with concrete trigger phrases) are present, with the when-clause being specific and quoted. It is not anchor 5 because the 'what' is abstract — it names no concrete deliverables (e.g., optimized stage, structured report) or specific workflow actions.

4 / 5

Trigger Term Quality

"slow loading, high memory, low FPS, or 'optimize my scene' requests" are natural phrases users would actually say, giving good keyword coverage. It falls short of anchor 5 because common variations and synonyms (e.g., 'speed up', 'frame rate', 'GPU memory', '.usd') are missing.

4 / 5

Distinctiveness Conflict Risk

"USD performance diagnosis and optimization" is a clear niche with distinct triggers, and the explicit boundary ("delegates auth/runtime setup to Phase 0 owners") marks the sibling-skill edge. Minor overlap risk remains because the trigger phrases themselves ('slow loading', 'low FPS') are not USD-specific and could fire on generic performance requests.

4 / 5

Total

15

/

20

Passed

Validation

75%

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

Validation — 12 / 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

referenced_paths_exist

Referenced path issues: 2 missing, 5 deeper-than-1-level

Warning

Total

12

/

16

Passed

Repository

openai/plugins

Path

plugins/nvidia/skills/omniverse-usd-performance-tuning/SKILL.md

Reviewed

Table of Contents

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