Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add crazymsn/academic-skills --skill modalgit clone --depth 1 https://github.com/crazymsn/academic-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/crazymsn/academic-skills/modal)<a href="https://agentmods.dev/skills/crazymsn/academic-skills/modal"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-skills/modal/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/crazymsn/academic-skills/modal"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-skills/modal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00123 | $0.03022 |
| Opus 5 | $0.00062 | $0.01511 |
| Sonnet 5 | $0.00025 | $0.00604 |
| Haiku 4.5 | $0.00012 | $0.00302 |
Grade A, and why
modal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
81% identical to modal — 99 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 (T4, L4, A10, L40S, A100, H100, H200, B200)
- 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
- Sub-second cold starts for low-latency inference
Everything in Modal is defined as code — no YAML, no Dockerfiles required (though both are 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
Installation and Authentication
Install
uv pip install modal
Authenticate
Prefer existing credentials before creating new ones:
- Check whether
MODAL_TOKEN_IDandMODAL_TOKEN_SECRETare already present in the current environment. - If not, check for those values in a local
.envfile and load them if appropriate for the workflow. - Only fall back to interactive
modal setupor generating fresh tokens if neither source already provides credentials.
modal setup
This opens a browser for authentication. For CI/CD or headless environments, use environment variables:
export MODAL_TOKEN_ID=<your-token-id>
export MODAL_TOKEN_SECRET=<your-token-secret>
If tokens are not already available in the environment or .env, generate them at https://modal.com/settings
Modal offers a free tier with $30/month in credits.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/api_reference.md 5.0 KB
- references/examples.md 6.9 KB
- references/functions.md 5.6 KB
- references/getting-started.md 4.0 KB
- references/gpu.md 4.4 KB
- references/images.md 6.0 KB
- references/resources.md 2.5 KB
- references/scaling.md 4.4 KB
- references/scheduled-jobs.md 3.5 KB
- references/secrets.md 2.8 KB
- references/volumes.md 5.7 KB
- references/web-endpoints.md 5.4 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 405 lines · 123 tokens per session scan A 822a9ea1f456
modal is a skill published in the GitHub repository crazymsn/academic-skills (22 stars, last pushed 3mo ago), licensed MIT. It adds 123 tokens to every session and 3,022 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 81% identical to modal, differing in 99 lines, and is treated as a copy.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
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Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…
modal
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.