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 agentmods add skills/ericrisco/rsc-harness/modalnpx skills add ericrisco/rsc-harness --skill modalgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/modal)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/modal"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/modal.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 | $0.00078 | $0.03632 |
| Opus 5 | $0.00039 | $0.01816 |
| Sonnet 5 | $0.00016 | $0.00726 |
| Haiku 4.5 | $0.00008 | $0.00363 |
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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
def fetch(url: str) -> int: How it starts
The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Modal — serverless Python & GPU as decorators
Modal runs your Python on remote containers without you ever writing a Dockerfile or a YAML
file. The mental model: infrastructure is declared inline as Python decorators. A
modal.App is the deployable unit; each @app.function runs in its own container built from
a modal.Image you describe in code; you attach a GPU, a Volume, or a Secret as keyword
arguments and the platform provisions, scales to zero, and tears down for you. There is no
control plane to babysit — the source file is the infra.
Pinned stack: modal 1.4.3 (released 2026-05-18), Python 3.10–3.14 (>=3.10,<3.15).
Install with pip install modal then modal setup to authenticate. Everything below uses
the Modal 1.0+ API; several pre-1.0 forms were removed and are called out as Bad→Good.
Not this skill
Modal owns the serverless-container-as-decorators surface and its CLI lifecycle; the contents of your function belong elsewhere.
| The job | Goes to |
|---|---|
| Calling a managed prediction API with no container of your own | replicate / together-fireworks / fal |
| Renting a persistent, SSH-able GPU box by the hour/week | runpod |
| FastAPI design (routing, Pydantic, deps) independent of host | fastapi |
| Writing a Dockerfile for a registry / k8s / Compose | docker |
| General Python language/runtime questions | python |
| RAG / LLM pipeline orchestration logic itself | llm-pipeline |
Decision: which entrypoint?
| You want… | Use | Persists after exit? |
|---|---|---|
| Run a function once and exit (script, batch) | modal run app.py + @app.local_entrypoint() |
No (ephemeral) |
| Hot-reload dev loop for a web endpoint | modal serve app.py |
No (dies on Ctrl-C) |
| A persistent named deployment (prod, schedules, endpoints) | modal deploy app.py |
Yes |
| Fan out work across many containers | .map() / .starmap() / .spawn() inside an entrypoint |
n/a |
What ships with it
5 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.
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.
- yesterday First seen · 291 lines · 78 tokens per session scan A fe2dc54cbc75
modal is a skill published in the GitHub repository ericrisco/rsc-harness (60 stars, last pushed 2d ago), licensed MIT. It adds 78 tokens to every session and 3,632 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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modal-serverless-gpu
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modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.