Modal job orchestration with .map, .starmap, .spawn, .spawn_map, @modal.batched. Use when fanning out work, polling job IDs, capping concurrency, or batching GPU calls.
Use this skill for Modal Sandbox lifecycle and interactive isolated execution. Trigger when the user needs to run untrusted or user-supplied code in a sandbox, keep a long-lived controller process alive, stream stdout from `exec()` calls, reattach to an existing sandbox, upload files at runtime, expose a tunneled HTTP or TCP service, or checkpoint and restore sandbox state. Do not use it for regular `@app.function` deployments, LLM serving, or training workflows.
Analyze project and recommend relevant AIE-Skills. Use when installing, setting up ML skills, bootstrapping AI/ML workflow, or sharing skills across repos.
Read and analyze arXiv papers via HTML. Use when user mentions arxiv, paper, research paper, academic paper, arxiv ID, or asks to read/summarize/analyze a scientific paper.
Diagnose and clean disk space on Linux servers, especially ML/LLM servers with Docker. Use when disk full, df shows high usage, du mismatch, Docker eating space, deleted files still consuming space, or need to free up disk on EC2/GPU server.
Build reproducible GPU-enabled Docker containers for ML/CUDA workloads. Use when writing GPU Dockerfiles, selecting NGC base images, configuring docker-compose GPU passthrough, setting up NVIDIA Container Toolkit, or installing uv inside Docker containers.
Set up selfhosted experiment tracking with MLflow or W&B. Use when deploying MLflow servers, configuring W&B selfhosted, logging metrics and artifacts, comparing experiment runs, managing model registry, or troubleshooting offline sync.
Build production-grade FastAPI at scale. Use when structuring projects, async SQLAlchemy, Alembic migrations, JWT auth, rate limiting, testing with httpx, deploying uvicorn/gunicorn/Docker.