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ray-data

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

68

Quality

82%

Does it follow best practices?

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Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

65%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A code-rich, highly actionable reference with clear 'when to use' guidance and real, well-signaled reference files. It loses points for marketing/benchmark padding and time-sensitive version metadata, missing validation checkpoints on batch write workflows, and inline duplication of material that already lives in the reference files.

Suggestions

Remove the 'Use cases' company name-drops, the 'Performance benchmarks' tables, and version/star metadata ('Ray 2.40.0+', '⭐ 36,000+') — they are non-essential padding and time-sensitive, hurting conciseness.

Add a validation/retry checkpoint to the batch inference and write pipelines (e.g., verify output row counts or sample outputs before declaring success) to lift workflow clarity above 2.

De-duplicate the transformations and integration sections: keep a one-line pointer in the body and move the full code into references/transformations.md and references/integration.md to avoid content that should be separate appearing inline.

DimensionReasoningScore

Conciseness

Mostly efficient with brief comments and code-first sections, but padded with marketing/non-essential content ('Pinterest', 'ByteDance', 'Spotify' name-dropping), benchmark tables, and time-sensitive info ('Ray 2.40.0+', '⭐ 36,000+') that the guidelines say should penalize conciseness. Not the level above (3), which is lean with every token earning its place; not the level below (1), which explains concepts Claude already knows.

2 / 3

Actionability

Fully executable, copy-paste-ready code throughout — read_parquet, map_batches, map, filter, groupby, GPU transforms, to_torch/to_tf, write_* — with specific parameters (batch_size, num_gpus, ScalingConfig). Not the level below (2), which relies on pseudocode or omits key details.

3 / 3

Workflow Clarity

Sequences are present (batch inference load→infer→write; preprocessing map→tokenize→augment→write) but lack validation/checkpoint steps for batch write operations, capping it per the destructive/batch-operation guideline. Not the level above (3), which requires explicit validation steps and feedback loops; not the level below (1), where steps are unclear or missing.

2 / 3

Progressive Disclosure

Has a clear References section with one-level-deep, real, well-signaled links (references/transformations.md, references/integration.md — both verified to exist), but substantial content is duplicated inline (full transformation and integration code appears in both the body and the reference files), so content that should be separate is inline. Not the level above (3), which appropriately splits content; not the level below (1), which has nested/deep references.

2 / 3

Total

9

/

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.

A strong description: third-person voice, specific concrete capabilities, natural trigger terms, and an explicit 'Use for...' clause covering both what and when. It is clearly niched to Ray Data and unlikely to conflict with other skills.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions and capabilities — 'Streaming execution across CPU/GPU', 'supports Parquet/CSV/JSON/images', 'Integrates with Ray Train, PyTorch, TensorFlow', plus concrete use cases (batch inference, ETL). Not the level below (2), which only names a domain and some actions without comprehensive coverage.

3 / 3

Completeness

Explicitly answers both 'what' (scalable data processing, streaming execution, format/framework support) and 'when' via the explicit trigger clause 'Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.' Not the level below (2), where the 'when' is missing or only implied.

3 / 3

Trigger Term Quality

Good coverage of natural terms users would say: 'batch inference', 'data preprocessing', 'multi-modal data loading', 'distributed ETL pipelines', 'Parquet/CSV/JSON/images', 'ML workloads'. Not the level below (2), which has relevant keywords but misses common variations.

3 / 3

Distinctiveness Conflict Risk

Clear niche (distributed ML data processing on Ray) with distinct triggers; unlikely to trigger for the wrong skill. Not the level below (2), which is somewhat specific but could still overlap with similar skills.

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
OpenLAIR/dr-claw
Reviewed

Table of Contents

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