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 agentlas-ai/agentlas-desktop --skill modalgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/modal)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/modal"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/modal"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.00065 | $0.04052 |
| Opus 5.5 | $0.00026 | $0.01621 |
| Sonnet 5.5 | $0.00013 | $0.00810 |
| Haiku 4.5 | $0.00006 | $0.00405 |
Grade A, and why
modal scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Subprocess / custom servers:** Some patterns here (multi-GPU training launchers, `@modal.web_server` apps) call `subprocess.run`/`subprocess.Popen` or shell commands during builds. Keep argument lists fixed and hardc This is a copy
100% identical to modal — 119 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 — 486 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
The Modal Python SDK supports Python 3.10–3.14. This skill targets the stable modal>=1.0 API (current release: 1.4.x).
Authenticate
Prefer existing credentials before creating new ones. Only the two Modal-specific
variables below are relevant — do not read, load, or expose any other environment
variables or .env file contents:
- Check whether
MODAL_TOKEN_IDandMODAL_TOKEN_SECRETare already set in the current environment. - If not, look up only those two keys in a local
.envfile (ignore all other entries) and load them if appropriate for the workflow. - Only fall back to interactive
modal setupor generating fresh tokens if neither source already provides those two values.
modal setup
This opens a browser for authentication. For CI/CD or headless environments, use environment variables:
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 6.7 KB
- references/examples.md 7.3 KB
- references/functions.md 5.7 KB
- references/getting-started.md 4.0 KB
- references/gpu.md 4.9 KB
- references/images.md 6.5 KB
- references/resources.md 2.5 KB
- references/scaling.md 4.4 KB
- references/scheduled-jobs.md 3.7 KB
- references/secrets.md 2.8 KB
- references/volumes.md 5.7 KB
- references/web-endpoints.md 5.7 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.
- 1mo ago First seen · 486 lines · 65 tokens per session scan A 81fd4eaea47e
modal is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 65 tokens to every session and 4,052 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to modal, differing in 119 lines, and is treated as a copy.
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