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 ericrisco/rsc-harness --skill runpodgit 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/runpod)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/runpod"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/runpod/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/ericrisco/rsc-harness/runpod"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/runpod.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 | $0.00114 | $0.02895 |
| Opus 5.5 | $0.00046 | $0.01158 |
| Sonnet 5.5 | $0.00023 | $0.00579 |
| Haiku 4.5 | $0.00011 | $0.00290 |
Grade A, and why
runpod 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RunPod: GPU compute, two products, one bill
RunPod sells GPU time two ways and they bill on opposite philosophies. Get the choice wrong and you either pay a steep premium for idle work or you pay 24/7 for a box that sits warm doing nothing. Everything below is the RunPod-specific operational playbook: which product a workload belongs on, how to write a worker that does not waste cold-start seconds, and which knobs actually move the number on the invoice.
The two products:
- Pods — rent a GPU container by the hour. It runs continuously while it is up, billed every hour whether busy or idle. Your dev box, your training job, your Jupyter.
- Serverless — per-second autoscaling workers. Billed only while a worker is actually running a job, from worker start to full stop, rounded up to the second. Costs roughly 2-3x the equivalent hourly pod rate, but idle gaps cost nothing on flex workers.
RunPod charges zero egress/ingress fees — bandwidth in and out is free, unlike the hyperscalers. That removes one variable from cost math: you only reason about GPU-seconds and storage.
When to use
- Deploying inference as a Serverless endpoint (vLLM LLM serving, image gen, custom model).
- Running a training / fine-tuning job or a Jupyter / dev box on a GPU Pod.
- Writing or debugging a serverless worker handler (
handler(job), async, streaming). - Building a custom worker Docker template / image, or wiring a network volume.
- Cost control on RunPod: cold starts, idle bleed, runaway max workers, idle volume charges.
- Picking a GPU SKU and the pod-vs-serverless tradeoff for a given workload.
When NOT to use
- Python-native serverless GPU with snapshot autoscaling and no Dockerfile → that is
modal. - Calling hosted, prebuilt model endpoints you do not host → that is
replicate. - Pulling/pushing weights, datasets, model cards on the Hub → that is
huggingface. - Running models locally on your own machine → that is
ollama. - Provider-agnostic cross-cloud spend dashboards → that is
cost-tracking. - Generic container packaging → that is
docker(here we cover only the RunPod image shape).
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.
- 3d ago First seen · 239 lines · 114 tokens per session scan A f18ca1d7c2c4
runpod is a skill published in the GitHub repository ericrisco/rsc-harness (314 stars, last pushed today), licensed MIT. It adds 114 tokens to every session and 2,895 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-10-24.
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