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Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.

69

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

72%

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

A highly actionable, well-structured body with executable examples and excellent progressive disclosure to verified reference files. Its main weaknesses are some redundancy that hurts conciseness and the absence of explicit validation/feedback checkpoints for batch and deployment workflows.

Suggestions

Remove redundancy: drop the duplicated GPU/install lists between the Overview and GPU section, and consolidate the per-section Reference callouts with the final Reference Files index so each is stated once.

Add explicit validation/feedback loops to the batch and deployment workflows — e.g., verify `.map()` results or partial failures before declaring success, and confirm a deployed endpoint responds before reporting completion — to lift workflow clarity.

Tighten the Authentication section so the security constraint about not reading other env vars is stated once rather than repeated in both the auth flow and Security Notes.

DimensionReasoningScore

Conciseness

Mostly efficient with executable code and no concept-explaining fluff, but the ~450-line body has redundancy — authentication restated in Security Notes, GPU/install lists repeated across Overview and GPU sections, and per-section "Reference" callouts restated by a final Reference Files block — so it could be tightened rather than earning the lean score-3 anchor.

2 / 3

Actionability

Fully executable, copy-paste-ready examples throughout (pinned package versions, GPU strings, volumes, secrets, schedules, scaling params, lifecycle-hook classes, sandboxes) plus a concrete CLI command table and exact resource defaults/billing, matching the score-3 anchor.

3 / 3

Workflow Clarity

Processes are sequenced (install → prefer-existing-credentials authenticate → write → run/deploy), but validation/verification checkpoints are implicit; per the scoring notes, batch operations (`.map` over 100 chunks) and deploys lack explicit validate→fix→retry feedback loops, capping the score at 2.

2 / 3

Progressive Disclosure

Clear overview with well-signaled one-level-deep references after each section plus a consolidated Reference Files index; all 12 referenced files exist and content is appropriately split, matching the score-3 anchor.

3 / 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.

A strong description that names a concrete platform, lists multiple specific actions, and provides an explicit "Use when..." trigger clause. Third-person voice and clear distinctiveness make it unlikely to conflict with other skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "running Python on demand, including on-demand GPUs", "deploying or serving AI/ML models", "training, fine-tuning, inference", "serving web endpoints", "scheduling batch jobs", "scaling Python code to cloud containers" — matching the score-3 anchor for multiple specific actions.

3 / 3

Completeness

Explicitly states what it does ("serverless cloud platform for running Python on demand, including on-demand GPUs") and when to use it via an explicit "Use when..." clause, matching the score-3 anchor that requires both what and when with explicit triggers.

3 / 3

Trigger Term Quality

Covers natural user phrasing such as "deploying or serving AI/ML models", "training, fine-tuning, inference", "web endpoints", and "batch jobs"; common variations are present rather than missing, so it exceeds the score-2 anchor.

3 / 3

Distinctiveness Conflict Risk

Naming the Modal SDK and serverless GPU Python cloud platform carves out a clear niche with distinct triggers, unlikely to fire for an unrelated skill, matching the score-3 anchor.

3 / 3

Total

12

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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

Total

14

/

16

Passed

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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