--- name: coreweave-prod-checklist-9c7b17e63718 description: 'Production readiness checklist for CoreWeave GPU workloads. Use when launching inference services, preparing GPU training for production, or validating deployment configurations. Trigger with phrases like "coreweave production", "coreweave go-live", "coreweave checklist", "coreweave launch". ' allowed-tools: Read, Bash(kubectl:*), Grep version: 1.11.0 license: MIT author: Jeremy Longshore tags: - saas - gpu-cloud - kubernetes - inference - coreweave compatibility: Designed for Claude Code --- # CoreWeave Production Checklist > **Community-contributed.** Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc. ## Overview Use this gate before exposing a CoreWeave inference or training workload to production. It proves that capacity, data protection, observability, and recovery are owned and tested; it is not a substitute for a service-specific risk review. ## Prerequisites - A signed deployment change, named service owner, and approved production namespace. - Passing staging evaluation, capacity forecast, and rollback manifest. - On-call routing, secret-manager references, and data classification for the workload. ## Instructions 1. Complete each checklist item with a link to its evidence, not a verbal assertion. 2. Confirm a staged rollout, health check, and rollback command during the change window. 3. Stop the release if any security, data, capacity, or observability gate lacks an owner. 4. Record the final go/no-go decision, time, owner, and redacted verification receipt. ## Inference Services - [ ] GPU type and count validated for model size - [ ] Autoscaling configured (KServe or HPA) - [ ] Health and readiness probes set - [ ] Resource requests AND limits specified - [ ] Node affinity targeting correct GPU class - [ ] `minReplicas >= 1` for production (no cold starts) ## Storage - [ ] Model weights in PVC (not downloaded at startup) - [ ] Checkpoints saved to persistent storage - [ ] Storage class appropriate (SSD for inference, HDD for archival) ## Security - [ ] Secrets for model tokens and registry access - [ ] Network policies applied - [ ] Container images from trusted registries ## Monitoring - [ ] GPU utilization metrics collected - [ ] Inference latency and throughput tracked - [ ] Alert on pod restarts and OOM events - [ ] Log aggregation configured ## Rollback ```bash kubectl rollout undo deployment/my-inference kubectl rollout status deployment/my-inference ``` ## Output - A production-readiness record linking capacity, storage, security, monitoring, and rollback evidence to the deployment. - A clear go/no-go decision with a responsible owner and tested recovery command. ## Error Handling | Failed gate | Required action | |---|---| | Readiness or staging test fails | Hold the release and restore the prior known-good revision. | | GPU capacity is unconfirmed | Do not launch; resolve quota or capacity with the platform owner. | | Secret or network policy is missing | Block deployment until the namespace hardening baseline is present. | | Monitoring route is absent | Block production exposure until alerts reach the on-call owner. | ## Examples Run the last production checks during the approved change window: ```bash kubectl -n inference-prod apply -f release.yaml kubectl -n inference-prod rollout status deployment/my-inference --timeout=15m kubectl -n inference-prod get pods -l app=my-inference ``` If rollout or the smoke test fails, execute the documented rollback immediately, capture redacted events, and mark the change no-go rather than retrying with weaker security or capacity controls. ## Resources - [CoreWeave CKS](https://docs.coreweave.com/docs/products/cks) ## Next Steps For upgrades, see `coreweave-upgrade-migration`. --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/coreweave-pack/skills/coreweave-prod-checklist/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Jeremy Longshore Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/coreweave-pack/skills/coreweave-prod-checklist/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Backend Developer, DevOps / SRE, ML / AI Engineer Specialties: API, Design, DevOps Tools: Kubernetes, Coreweave Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 306fab45df251d4d4529bc85c04d462443669e2a63d245a3f62a9d21c74bcba6