gj-release-prep
Prepare GitLab release readiness artifacts. Use when a milestone or MR set is ready for release planning and needs release notes, included Issues/MRs, configuration or database changes, rollout checks, rollback steps, monitoring, and post-release validation.
Works with
--- name: gj-release-prep description: Prepare GitLab release readiness artifacts. Use when a milestone or MR set is ready for release planning and needs release notes, included Issues/MRs, configuration or database changes, rollout checks, rollback steps, monitoring, and post-release validation. license: MIT --- # GJ Release Prep ## Workflow 1. Read release Issue, milestone, merged or ready MRs, test results, and known risks. 2. List included Issues/MRs and user-visible changes. 3. Check database, config, permission, and operations changes. 4. Check documentation impact: - Create or update `docs/releases/<version>.md`. - Confirm linked test report and rollback notes exist. - Identify context/module docs that need post-release updates. 5. Draft release note, rollout plan, rollback plan, and validation checklist. 6. Mark manual confirmations for DevOps, Tech Lead, QA, security, or DBA. ## Output ```markdown ## Release Prep Release scope: Included Issues and MRs: Testing result: Config / database / permission changes: Release note: Documentation impact: Rollout checklist: Rollback plan: Post-release validation: Human confirmations: ``` ## References Read `references/demo-run.md` for the demo release dry-run example.
More Deployment & CI/CD skills
finetuning
microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
prisma-compute
prisma/skills
Prisma Compute deployment and hosting guide. Use whenever the user mentions Prisma Compute, `prisma.compute.ts`, `defineComputeConfig`, deploying or hosting a Prisma app, `@prisma/cli app deploy`, `compute:deploy`, `create-prisma --deploy`, `PRISMA_SERVICE_TOKEN`, Compute auth/workspaces, apps/deployments/build logs/domains, localhost vs `0.0.0.0`, deploy port binding, or framework deploy readiness for Hono, Elysia, Next.js, TanStack Start, Astro, Nuxt, Svelte, Nest, Turborepo, or custom/prebuilt artifacts.
azure-quotas
microsoft/azure-skills
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: \"check quotas\", \"service limits\", \"current usage\", \"request quota increase\", \"quota exceeded\", \"validate capacity\", \"regional availability\", \"provisioning limits\", \"vCPU limit\", \"how many vCPUs available in my subscription\".
