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 regen-coordination/org-os-template --skill modal-computegit clone --depth 1 https://github.com/regen-coordination/org-os-templateWrote 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/regen-coordination/org-os-template/modal-compute)<a href="https://agentmods.dev/skills/regen-coordination/org-os-template/modal-compute"><img src="https://agentmods.dev/badge/skills/regen-coordination/org-os-template/modal-compute.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.00376 |
| Opus 5 | $0.00019 | $0.00188 |
| Sonnet 5 | $0.00008 | $0.00075 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
modal-compute 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 8d 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.
What it actually says
Modal Compute
Use the modal CLI for serverless GPU workloads. No pod lifecycle to manage — write a decorated Python script and run it.
Setup
pip install modal
modal setup
Commands
| Command | Description |
|---|---|
modal run script.py |
Run a script on Modal (ephemeral) |
modal run --detach script.py |
Run detached (background) |
modal deploy script.py |
Deploy persistently |
modal serve script.py |
Serve with hot-reload (dev) |
modal shell --gpu a100 |
Interactive shell with GPU |
modal app list |
List deployed apps |
GPU types
T4, L4, A10G, L40S, A100, A100-80GB, H100, H200, B200
Multi-GPU: "H100:4" for 4x H100s.
Script pattern
import modal
app = modal.App("experiment")
image = modal.Image.debian_slim(python_version="3.11").pip_install("torch==2.8.0")
@app.function(gpu="A100", image=image, timeout=600)
def train():
import torch
# training code here
@app.local_entrypoint()
def main():
train.remote()
When to use
- Stateless burst GPU jobs (training, inference, benchmarks)
- No persistent state needed between runs
- Check availability:
command -v modal
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.
- 8d ago First seen · 57 lines · 38 tokens per session scan A a7f087aa04ee
modal-compute is a skill published in the GitHub repository regen-coordination/org-os-template (5 stars, last pushed 2d ago), licensed MIT. It adds 38 tokens to every session and 376 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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