serverless-modal

Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says "modal run", "modal training", "modal inference", "deploy to modal", "need a GPU", "run on modal", "serverless GPU", or needs remote GPU compute.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

65%

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

Highly actionable and well-sequenced, but verbose in places, lacking an explicit error-recovery loop, and monolithic with non-existent referenced files. Splitting pricing/patterns into referenced files and trimming intro prose would lift the weaker dimensions.

Suggestions

Move the pricing table, the six code patterns, and the CLI reference into separate files under references/ and link to them from SKILL.md to improve progressive disclosure and reduce inline length.

Trim introductory prose that restates what Modal is and consolidate the duplicate cost-protection/security warnings into a single concise block to improve conciseness.

Add an explicit verification/error-recovery feedback loop in Step 4 (e.g., 'if modal app logs shows an OOM or crash, reduce batch size / change GPU and rerun') to satisfy the validation-checkpoint requirement.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with high-value tables and code, but carries unnecessary introductory prose ("Modal is a serverless GPU cloud. Key advantages...") and a repeated cost-protection/security block that could be tightened.	2 / 3
Actionability	Provides six fully executable code patterns, concrete CLI commands, a VRAM-rules table, and a copy-paste cost-estimation template — all ready to run.	3 / 3
Workflow Clarity	Steps 1–6 are clearly sequenced and cost estimation is enforced as a checkpoint, but verification/error recovery is implicit (no feedback loop such as 'if logs show X, fix and rerun'), so validation gaps remain.	2 / 3
Progressive Disclosure	The skill is a single ~330-line monolithic file with all content inline and no bundle files; referenced paths (compute-env-contract.md, .aris/compute/modal.md) do not exist, so references are not real or clearly signaled.	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: concrete capability list, explicit 'Use when' triggers, and a well-scoped Modal niche. The only minor risk is the broad 'need a GPU' trigger overlapping other GPU skills, but the Modal-specific terms dominate.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ("training, fine-tuning, inference, batch processing") plus concrete operational properties ("no SSH, no Docker, auto scale-to-zero"), matching the score-3 anchor for multiple specific actions.	3 / 3
Completeness	Explicitly answers what (run GPU workloads on Modal for training/inference/etc.) and when (an explicit "Use when user says ..." clause with listed triggers), satisfying both required halves.	3 / 3
Trigger Term Quality	Includes a strong set of natural phrases users would say ("modal run", "modal training", "deploy to modal", "need a GPU", "serverless GPU"), giving good coverage of natural trigger terms.	3 / 3
Distinctiveness Conflict Risk	The Modal-named triggers carve a clear niche unlikely to fire for non-Modal GPU skills, despite the slightly generic "need a GPU" term; overall it is clearly distinguishable.	3 / 3
	Total	12 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: wanshuiyin/Auto-claude-code-research-in-sleep
Commit: 82076e5

Reviewed: 5 days ago

Table of Contents

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