Use when deploying qaskills.sh to production, verifying whether a deploy landed, or when a push to main did not show up on the live site, e.g. "deploy", "ship it", "push this live", "is prod updated?", "the site still shows the old version".
npx skills add https://github.com/PramodDutta/qaskills --skill ship-prod
Deploys COMMITTED HEAD only, to the correct Vercel project, then proves the deploy landed. git push to main does not reliably auto-deploy; this skill is the deploy path.
qaskills.sh, project ID prj_rDKli4AyhHoXZXV8NHrs92Ncbf4f, org team_DGM6VSs6vhASlhktmHkSqPwn, account luckydutta96qaskills exists WITHOUT the domain. Never deploy there. Never accept interactive "link this directory?" defaults.vercel.json (shared build && web build); Node must stay 20.x (Neon driver breaks on 24)vercel --prod uploads the WORKING TREE, not git HEAD.vercel/project.json is gitignored, so fresh worktrees are unlinked: explicit env IDs required therecd /Users/promode/qaskills
vercel whoami # must be luckydutta96; else STOP, user must vercel login
git status --short # empty => clean path; anything => worktree path
git log -1 --oneline --stat | head -15 # confirm HEAD is exactly what you intend to ship
pnpm --filter @qaskills/shared build && pnpm --filter @qaskills/web build # never ship a red build
Clean tree (no modified or untracked files that could ship):
cd /Users/promode/qaskills && npx vercel --prod --yes
Dirty tree (default assumption; the working tree here usually carries WIP):
DEPLOY_DIR=$(mktemp -d)/qaskills-deploy
git -C /Users/promode/qaskills worktree add "$DEPLOY_DIR" HEAD
cd "$DEPLOY_DIR"
VERCEL_ORG_ID=team_DGM6VSs6vhASlhktmHkSqPwn \
VERCEL_PROJECT_ID=prj_rDKli4AyhHoXZXV8NHrs92Ncbf4f \
npx vercel --prod --yes
cd /Users/promode/qaskills
git worktree remove "$DEPLOY_DIR" --force
Capture the deployment URL the CLI prints; it goes in the final summary.
npx vercel ls | head -5 # newest deployment: Ready, Production
curl -s -o /dev/null -w '%{http_code}\n' https://qaskills.sh # 200
# The specific change, visible live. Examples:
curl -s -o /dev/null -w '%{http_code}\n' https://qaskills.sh/blog/<new-slug> # new article: 200
curl -s https://qaskills.sh/<changed-page> | grep -c '<expected-marker>' # code change: >= 1
All three must pass before saying "deployed". If the domain still serves old content while the new deployment is Ready, the alias did not move: npx vercel promote <deployment-url>.
npx vercel ls # find the previous Ready production deployment
npx vercel promote <previous-url> # fast path: point the domain back
For a code-level revert, git revert <sha> on main, then run this skill again.
| Symptom | Cause | Fix |
|---|---|---|
| CLI asks to link / offers project qaskills | Unlinked dir, interactive defaults | Abort; re-run with both VERCEL_* env vars set |
| Error: not authorized / wrong scope | Logged into another account | vercel whoami; user runs vercel login as luckydutta96 |
| Vercel build fails, local build green | Env-dependent code at import time | Lazy-init pattern (see CLAUDE.md); no secrets required at build |
| Deploy Ready but site unchanged | Domain alias on older deployment, or you shipped stale HEAD | vercel promote; confirm intended commit was in HEAD |
| WIP appeared on prod | Deployed dirty working tree directly | Roll back via promote, then redeploy via worktree |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take pramoddutta/ship-prod from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npx.
Without those the skill loads but fails at the first command.