runpod

name

runpod

description

Work with Runpod GPU infrastructure and documentation. Use when Codex needs to create, deploy, manage, debug, or review Runpod Pods, Serverless workers/endpoints, templates, network volumes, Public Endpoints, Flash apps, runpodctl commands, Runpod Python SDK usage, REST/GraphQL API usage, Runpod MCP setup, Docker images for Runpod, endpoint request payloads, logs, billing/cost checks, or Runpod deployment troubleshooting.

Runpod

Use this skill to work against Runpod's current docs and infrastructure patterns without guessing old API shapes. Prefer official Runpod docs and local repo evidence over memory.

First Pass

1. Identify the target product: Serverless, Pods, Flash, Public Endpoints, network volumes, templates, runpodctl, REST API, GraphQL, MCP, or billing.
2. Check whether the request is read-only or mutating. Do not create, start, stop, delete, resize, or redeploy live Runpod resources unless the user explicitly asked for that action.
3. Discover available tools in this order:
   • Runpod MCP tools, if installed in the current environment.
   • runpodctl, if installed locally.
   • REST/GraphQL API via RUNPOD_API_KEY, if the user asked for live infrastructure work.
   • Official docs only, if answering or planning.
4. Refresh docs before relying on exact flags, fields, URLs, payload sizes, GPU names, pricing, or rate limits. Use python scripts/runpod_docs.py search <terms> or browse https://docs.runpod.io/llms.txt.
5. Focus on basic usage first: current docs, commands, endpoint calls, SDK examples, and local repo evidence. Open source repos only when debugging the tool/package itself, checking release behavior, or when the user explicitly asks about the repo.
6. Keep secrets out of files and chat. Use environment variables such as RUNPOD_API_KEY; never print token values.

Product Routing

• Serverless worker code, handlers, endpoint jobs, /run, /runsync, /status, streaming, Dockerfiles, cold starts, model caching, vLLM, or queue/load-balancing behavior: read references/serverless.md.
• Flash SDK/CLI, runpod_flash, @Endpoint, flash dev, flash deploy, Flash apps, local-code remote execution, or Flash endpoint configuration: read references/flash.md.
• Public Endpoints, hosted model APIs, model-specific parameters, image/video/audio/text model calls, Vercel AI SDK provider, or Hub playground-generated requests: read references/public-endpoints.md.
• runpodctl command usage, CLI install/config, command groups, shell completion, file transfer, CLI output formats, legacy command migration, the runpod/runpodctl repo, or the Homebrew tap: read references/runpodctl.md.
  • Pods, SSH, JupyterLab, exposed ports, templates, persistent storage, REST API resource management, MCP setup, or network volumes: read references/pods-cli-api.md.
• REST API v2 resource management, beta endpoints, v2 OpenAPI, Pods, Serverless, templates, catalog, billing, or network volumes: read references/api-v2.md.
• GraphQL schema/spec, https://api.runpod.io/graphql, gpuTypes, cpuTypes, Pod GraphQL mutations, or legacy project code using GraphQL: read references/graphql.md.
• Unsure where a topic lives: read references/doc-map.md, then fetch the specific official page.

Common Workflows

Build or fix a Serverless worker

1. Inspect the repo first: handler entrypoint, Dockerfile, requirements, local test input, model-loading path, and deployment docs/scripts.
2. Keep heavyweight initialization outside the handler.
3. Validate job["input"] or event["input"] before starting expensive work.
4. Test locally before image/deploy work when possible:

python handler.py --test_input '{"input": {"prompt": "hello"}}'
python handler.py --rp_serve_api --rp_log_level DEBUG

5. For Docker, prefer small runtime images and .dockerignore; use CUDA or framework images only when the workload actually needs them.
6. For model assets, prefer Runpod cached models or network volumes when appropriate instead of blindly baking multi-GB files into every image.

Send requests to queue-based endpoints

Use the endpoint URL shape https://api.runpod.ai/v2/{endpoint_id}/....

• Use /runsync for short jobs where waiting is acceptable.
• Use /run plus /status/{job_id} for long jobs.
• Use /stream/{job_id} only when the handler yields streaming output.
• Use /cancel/{job_id} for abandoned long jobs.
• Use /health before blaming the client. It is usually less embarrassing.

Manage live resources

1. Confirm the exact target by ID/name before changing anything.
2. Prefer read/list/get commands before mutation:

runpodctl doctor
runpodctl gpu list
runpodctl pod list
runpodctl serverless list
runpodctl template list
runpodctl network-volume list

3. For current REST resource management, prefer API v2 at https://api.runpod.io/v2/... with Authorization: Bearer $RUNPOD_API_KEY; read references/api-v2.md first because v2 is beta.
4. Use https://rest.runpod.io/v1/... only when the task specifically targets the API v1 surface documented in references/pods-cli-api.md.
5. For destructive actions, state the target and impact, then proceed only if the user requested that class of action in this turn.

Docs Helper

Use the bundled helper to search and fetch official docs without loading the entire site into context:

python /Users/vavo/.codex/skills/runpod/scripts/runpod_docs.py search serverless handler
python /Users/vavo/.codex/skills/runpod/scripts/runpod_docs.py page serverless/workers/handler-functions
python /Users/vavo/.codex/skills/runpod/scripts/runpod_docs.py openapi --output /tmp/runpod-openapi.json

The docs index is https://docs.runpod.io/llms.txt. Treat it as the routing table for the docs.

Output Style

• For implementation tasks, make the code/config changes, run the relevant local checks, and report the exact verification.
• For live infrastructure tasks, include the exact resource IDs touched and the commands/API operations used, with secrets redacted.
• For planning tasks, separate local code changes from Runpod console/API changes.
• For troubleshooting, report observed facts first: command output, endpoint status, logs, response bodies, image tags, and resource IDs. Then give the smallest next action.