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evaluating-cosmos-policy

Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.

72

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Critical

Do not install without reviewing

SKILL.md
Quality
Evals
Security

Quality

Content

77%

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 with clear, validated multi-step workflows and properly linked reference files. Its main weakness is token inefficiency from repeated full command blocks across the body and references, plus some background explanation Claude already knows.

Suggestions

Deduplicate the LIBERO command block: keep a single concise smoke command in SKILL.md and move the full/expanded matrices exclusively into references/libero-commands.md, linking to them instead of repeating.

Trim the "Core concepts" VLA/Cosmos-Policy background to only the architecture choices that affect flag selection; drop generic explanations Claude already knows.

Replace the repeated full-eval block in Workflow 1 Step 5 with a parameter delta (e.g. "same as smoke with --num_trials_per_task 50 across the four suites") pointing to the reference for the full command.

DimensionReasoningScore

Conciseness

The ~30-line LIBERO command block is duplicated verbatim in Quick start and Workflow 1 Step 3, and the full eval recurs in Step 5 and again in references; the "Core concepts" section restates VLA/Cosmos-Policy background Claude largely already knows, so it could be tightened rather than being fully lean.

2 / 3

Actionability

Provides fully executable, copy-paste-ready `uv run` commands with concrete flag values, env exports, and working Python snippets for parsing results, matching the score-3 anchor.

3 / 3

Workflow Clarity

Workflows use numbered steps with copy-paste progress checklists, a smoke-gate validation ("Run full benchmark if smoke passes"), an explicit validate-and-parse step, and a common-issues section with fix-to-retry guidance, satisfying the explicit-checkpoint anchor.

3 / 3

Progressive Disclosure

The two reference files are real, one level deep, and clearly signaled, but the full command matrices are inlined in SKILL.md and largely duplicated in those references, so content that should be separated into the bundle is still inline rather than appropriately split.

2 / 3

Total

10

/

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.

The description is specific, third-person, and clearly distinguishes the skill with concrete trigger terms. It answers both what it does and when to use it without fluff or over-claiming.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "setting up cosmos-policy", "running headless GPU evaluations with EGL rendering", and "profiling inference latency" — matching the score-3 anchor for specific concrete actions rather than just naming a domain.

3 / 3

Completeness

Explicitly answers what ("Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments") and when via an explicit "Use when setting up..." trigger clause, satisfying both halves.

3 / 3

Trigger Term Quality

Covers natural terms a user would actually say for this domain — "LIBERO and RoboCasa", "cosmos-policy", "robot manipulation evaluation", "headless GPU", "EGL rendering", and "inference latency" — giving good keyword coverage.

3 / 3

Distinctiveness Conflict Risk

Targets a narrow, named niche (NVIDIA Cosmos Policy on two specific benchmarks) with distinct triggers and explicit alternative-skill pointers, making mis-triggering unlikely.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
Orchestra-Research/AI-Research-SKILLs
Reviewed

Table of Contents

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