runpod-serverless

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Create serverless endpoint templates and endpoints on RunPod.io. Supports Python/Node.js runtimes, GPU selection (3090, A100, etc.), and idempotent configuration. Use this skill when a user wants to set up a new serverless endpoint or template on RunPod.

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Sourcejarmen423/skills

Added on2026-02-08

NPX Install

npx skill4agent add jarmen423/skills runpod-serverless

SKILL.md Content

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RunPod Serverless Creator

This skill helps you create and configure serverless endpoints on RunPod.io. It handles both the Template creation (software config) and the Endpoint creation (hardware config).

How to Use

The primary tool is the

scripts/create_serverless.py

script. It is idempotent: if a template or endpoint with the same name exists, it will reuse the template and update the endpoint.

Prerequisites

API Key: You need a RunPod API Key.
- Ask the user to provide it or check if
```
RUNPOD_API_KEY
```
  is in the environment.
- Security: Do not hardcode the key in the script. Pass it via environment variable
```
RUNPOD_API_KEY
```
  .

Command

bash

python runpod-serverless/scripts/create_serverless.py \
  --name <NAME> \
  --runtime <python|node> \
  --gpu <GPU_TYPE> \
  [--disk <GB>] \
  [--min-workers <INT>] \
  [--max-workers <INT>]

Arguments

```
--name
```
(Required): Unique name for the template and endpoint.
```
--runtime
```
(Required):
```
python
```
or
```
node
```
. Maps to standard RunPod serverless base images.
```
--gpu
```
(Required): GPU type alias (e.g.,
```
3090
```
,
```
4090
```
,
```
a100
```
,
```
a6000
```
) or specific ID (e.g.,
```
AMPERE_24
```
).
```
--disk
```
: Container disk size in GB (default: 10).
```
--min-workers
```
: Minimum active workers (default: 0 for cold-start serverless).
```
--max-workers
```
: Maximum active workers (default: 1).

GPU Options (Aliases)

```
3090
```
->
```
AMPERE_24
```
(24GB VRAM)
```
4090
```
->
```
ADA_24
```
(24GB VRAM)
```
a4000
```
->
```
AMPERE_16
```
(16GB VRAM)
```
a6000
```
->
```
AMPERE_48
```
(48GB VRAM)
```
a100
```
->
```
AMPERE_80
```
(80GB VRAM)

See

references/runpod_api.md

for full API details and ID mappings.

Example Workflow

Ask User for:
- Application Name
- Runtime (Python/Node)
- Preferred GPU
- API Key (if not known)

Run Script: