This lets Claude deploy and run Python code on Modal's serverless platform, with first-class support for GPU workloads. You can spin up containers with H100s or A100s on demand, serve ML models as web endpoints, schedule batch jobs, and manage persistent storage for model weights. Everything is defined in Python code instead of YAML or Dockerfiles, which keeps the setup simple. The skill covers the full workflow from authentication through deployment, including volume mounts, secret management, and autoscaling configuration. Modal offers sub-second cold starts and scales from zero to thousands of containers automatically. If you're deploying AI models or running GPU-accelerated workloads without managing infrastructure, this is the straightforward path.
- skills.sh installs
- 2K
- GitHub stars
- 56.6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add k-dense-ai/scientific-agent-skills --skill modal --agent claude-codeInstalls into .claude/skills of the current project.
Files
Modal
Overview
Modal is a cloud platform for running Python code serverlessly, with a focus on AI/ML workloads. Key capabilities:
- GPU compute on demand (including H100, H200, B200 and B300)
- Serverless functions with autoscaling from zero to thousands of containers
- Custom container images built entirely in Python code
- Persistent storage via Volumes for model weights and datasets
- Web endpoints for serving models and APIs
- Scheduled jobs via cron or fixed intervals
- Container reuse and warm pools to reduce cold starts (model loading still takes time)
Modal Apps and Images are defined in Python; existing Dockerfiles are also supported.
When to Use This Skill
Use this skill when:
- Deploy or serve AI/ML models in the cloud
- Run GPU-accelerated computations (training, inference, fine-tuning)
- Create serverless web APIs or endpoints
- Scale batch processing jobs in parallel
- Schedule recurring tasks (data pipelines, retraining, scraping)
- Need persistent cloud storage for model weights or datasets
- Want to run code in custom container environments
- Build job queues or async task processing systems
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- Repository
- github.com/k-dense-ai/scientific-agent-skills
- Categories
- AI & Agent Building, Data Science & ML, Automation & Workflows, Python, Cloud & Infrastructure
- First seen
- Jun 3, 2026
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