modal
Cloud computing platform for running Python on GPUs and serverless infrastructure. Use when deploying AI/ML models, running GPU-accelerated workloads, serving web endpoints, scheduling batch jobs, or scaling Python code to the cloud. Use this skill whenever the user mentions Modal, serverless GPU compute, deploying ML models to the cloud, serving inference endpoints, running batch processing in the cloud, or needs to scale Python workloads beyond their local machine. Also use when the user wants to run code on H100s, A100s, or other cloud GPUs, or needs to create a web API for a model.
88
86%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
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.
This is a strong skill description that clearly identifies the platform (Modal), lists concrete actions, and provides extensive trigger guidance with two explicit 'Use when' clauses. The description covers both general use cases and specific hardware mentions (H100s, A100s), making it highly discoverable. It uses proper third-person voice throughout and is well-differentiated from generic cloud or Python skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: deploying AI/ML models, running GPU-accelerated workloads, serving web endpoints, scheduling batch jobs, scaling Python code to the cloud, creating web APIs for models. | 3 / 3 |
Completeness | Clearly answers both 'what' (cloud computing platform for running Python on GPUs and serverless infrastructure) and 'when' with explicit 'Use when...' and 'Use this skill whenever...' clauses listing detailed trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: Modal, serverless GPU, H100s, A100s, cloud GPUs, ML models, inference endpoints, batch processing, web API, Python workloads. These are terms users would naturally use when needing this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Clearly targets a specific niche — Modal as a cloud computing platform — with distinct triggers like specific GPU types (H100s, A100s), serverless GPU compute, and the Modal platform name. Unlikely to conflict with general Python or generic cloud skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong skill file that excels at actionability and progressive disclosure, providing executable code examples for every major concept while cleanly pointing to detailed reference files. The main weaknesses are moderate verbosity (the 'When to Use' section and some introductory text could be trimmed) and the absence of validation/verification steps in workflows — there's no guidance on checking deployment status, reading logs, or handling common errors.
Suggestions
Remove or significantly trim the 'When to Use This Skill' section since it largely duplicates the skill description metadata and consumes tokens without adding actionable guidance.
Add validation checkpoints to workflow patterns — e.g., how to verify a deployment succeeded (`modal app list`), check function logs, or handle common errors like GPU unavailability or image build failures.
Trim the Overview bullet list and introductory sentences that describe Modal's value proposition rather than instructing Claude on how to use it (e.g., 'Everything in Modal is defined as code — no YAML, no Dockerfiles required').
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally well-structured but includes some unnecessary framing (e.g., 'When to Use This Skill' section largely repeats the description, and some explanatory text like 'Everything in Modal is defined as code' is filler). The overview bullet points and some section intros could be tighter, but most content earns its place with concrete code and configuration details. | 2 / 3 |
Actionability | Excellent actionability throughout — nearly every concept is accompanied by executable, copy-paste-ready Python code examples. CLI commands are concrete, GPU configuration shows exact syntax, and the common workflow patterns section provides complete, runnable applications covering inference, batch processing, and scheduled pipelines. | 3 / 3 |
Workflow Clarity | While individual concepts are clearly explained with code, the skill lacks explicit validation checkpoints and feedback loops. For example, there's no guidance on verifying deployments succeeded, checking logs for errors, or handling common failure modes. The workflow patterns show what to do but not how to verify correctness or recover from issues. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure — the main file serves as a clear overview with concise examples for each concept, and every section includes a well-signaled one-level-deep reference to detailed documentation. The reference files section at the end provides a clean navigation index. Content is appropriately split between overview and detail. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- Reviewed
Table of Contents
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