domino-distributed-computing

Work with distributed computing frameworks in Domino including Apache Spark, Ray, and Dask clusters. Covers cluster configuration, on-demand clusters, choosing between frameworks, PySpark usage, and scaling workloads. Use when processing large datasets, parallel ML training, or running distributed compute jobs.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Quality

Content

71%

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

The content is highly actionable with extensive executable code, but it is over-long for a single SKILL.md: it restates framework basics Claude already knows and keeps all per-framework reference material inline with no bundle files. Workflow clarity is hampered by missing validation checkpoints for cluster lifecycle operations.

Suggestions

Move the per-framework deep-dives (Spark/Ray/Dask/GPU sections) into separate reference files under ./references/ and keep SKILL.md as a concise overview with one-level-deep links, improving progressive disclosure and conciseness.

Remove the 'Supported Frameworks' Best-For table and 'When to Use Each Framework' bullets that restate common knowledge about Spark/Ray/Dask; instead retain only Domino-specific decision guidance.

Add explicit validation checkpoints to the cluster workflow (e.g., verify cluster is running before submitting jobs, confirm worker count via dashboard before scaling) to raise workflow clarity for batch/distributed operations.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient code-focused guidance, but it restates basic concepts Claude already knows (e.g., framework 'Best For' table, 'When to Use Each Framework' bullets that restate what Spark/Ray/Dask are) and repeats cluster-launch detail in multiple places, so it could be tightened.	2 / 3
Actionability	Provides fully executable, copy-paste-ready code across Spark, Ray, Dask, GPU clusters, and autoscaling (e.g., SparkSession, ray.remote, dask.dataframe, cluster_config dicts), with specific Domino paths and SDK calls rather than pseudocode.	3 / 3
Workflow Clarity	The UI launch steps are sequenced, but the cluster lifecycle (start, monitor, validate, scale down) lacks explicit validation checkpoints or error-recovery feedback loops; the autoscaling and troubleshooting sections mention monitoring but offer no verify-then-proceed checkpoints, capping clarity at 2.	2 / 3
Progressive Disclosure	Content is well-sectioned by topic, but the SKILL.md is a monolithic ~380-line body with no bundle files (references/scripts/assets absent) to offload the per-framework detail; everything lives inline rather than being split into one-level-deep referenced files.	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.

The description is well-crafted: it names concrete capabilities, lists natural trigger terms, answers both what and when explicitly, and occupies a distinct niche (Domino distributed computing). It uses proper third-person voice throughout.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions: 'cluster configuration, on-demand clusters, choosing between frameworks, PySpark usage, and scaling workloads' alongside named frameworks (Spark, Ray, Dask), matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Clearly answers what ('Work with distributed computing frameworks in Domino including Apache Spark, Ray, and Dask clusters...') and when ('Use when processing large datasets, parallel ML training, or running distributed compute jobs'), satisfying both what and when with explicit triggers.	3 / 3
Trigger Term Quality	Includes natural user-facing terms like 'processing large datasets, parallel ML training, or running distributed compute jobs' plus framework names users would say, giving good coverage of natural phrasing.	3 / 3
Distinctiveness Conflict Risk	Scoped to Domino-specific distributed computing with three named frameworks and distinct trigger terms ('processing large datasets, parallel ML training, distributed compute jobs'), forming a clear niche unlikely to conflict with other skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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: dominodatalab/domino-claude-plugin
Commit: 64f4433

Reviewed: 1 day ago

Table of Contents

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