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RunPod endpoint operations runbook — checking worker state, forcing scale-down, reading the spend query, and cold-start expectations. Use before and after cloud inference sessions. Forward-looking — RunPod tooling lives outside this repo.
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基于 SOC 职业分类
Mechanics contract for running a multi-agent workflow that survives to completion - the durable-ledger rule, the fan-in partition rule, the output-budget rule, and the recovery procedure for a run that dies mid-flight. Read before authoring or resuming any Workflow script.
The handshake for verifying anything that needs the workstation — how to state acceptance criteria before running, hand the human an exact command block, and record the reported output as evidence. Use whenever a claim depends on the GPU, Docker, the running services, /mnt/ai-data, or a full make quality.
The method contract for a quality campaign — the phase ladder, the standing dimension list, the severity rubric, what counts as a confirmed finding, and the batch-and-push cadence. Read before running or participating in a bug bash or quality gate.
How to launch, resume, and serve model training runs on this stack — checkpoint layout, the memory-fit calculator, and the train-and-serve round trip. Skeleton reference; grows once the training tier lands.
Container debugging runbook for this inference stack — per-service healthcheck endpoints, common CUDA/driver failure signatures, log locations, and the safe restart procedure. Use when a service is unhealthy or failing to start.
The reproducible benchmark protocol for this workstation — which entry points to run, warm-up policy, how results are recorded and compared, and what counts as a regression. Use before reporting any latency/throughput/memory number.
| name | runpod-ops |
| description | RunPod endpoint operations runbook — checking worker state, forcing scale-down, reading the spend query, and cold-start expectations. Use before and after cloud inference sessions. Forward-looking — RunPod tooling lives outside this repo. |
Reference for managing RunPod serverless/pod endpoints used for cloud inference and training. RunPod itself is external to this repository; this skill captures the operating procedure so sessions do not leave workers running or misread spend.
Status: forward-looking. Wire the concrete commands to whatever RunPod CLI / API wrapper the integration roadmap lands (
make agent-usagebecomes the spend entry point). Until then, treat the steps below as the checklist and use the RunPod console/API directly.
Sandboxed execution: agents have no access to the host, its
maketargets, or the RunPod console. The human executes each checklist step and reports the result; the agent tracks the checklist and records the reported worker state and spend.
:latest).benchmarking skill) so cloud
numbers are comparable to local ones and cold-start runs are discarded.make agent-usage once wired) and record it against the task.The skeptic and the primary agent both run this checklist: primary before starting cloud work, skeptic when verifying a "completed" cloud task actually released its resources.