DevOps and CI/CD expert. Use when setting up pipelines, containerizing applications, deploying to Kubernetes, or implementing release strategies. Covers GitHub Actions, Docker, K8s, Terraform, and GitOps.
npx skills add https://github.com/majiayu000/spellbook --skill devops-excellence
> These rules are mandatory. Violating them means the skill is not working correctly.
Never use long-lived static credentials. Always use OIDC or short-lived tokens.
# ❌ FORBIDDEN: Static AWS credentials
env:
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
# ✅ REQUIRED: OIDC-based authentication
- name: Configure AWS Credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::123456789012:role/GitHubActions
aws-region: us-east-1
# No long-lived secrets - uses GitHub OIDC provider
Containers must NEVER run as root. Always specify a non-root user.
# ❌ FORBIDDEN: Running as root (default)
FROM node:20
WORKDIR /app
CMD ["node", "server.js"]
# ❌ FORBIDDEN: Explicit root user
USER root
# ✅ REQUIRED: Non-root user with UID > 1000
FROM node:20-alpine
RUN addgroup -g 1001 -S nodejs && \
adduser -S nodejs -u 1001
USER nodejs
WORKDIR /app
CMD ["node", "server.js"]
Never bake secrets into Docker images. Use runtime injection or secrets managers.
# ❌ FORBIDDEN: Secrets in build args or ENV
ARG DATABASE_PASSWORD
ENV API_KEY=sk-xxx
# ❌ FORBIDDEN: Copying secret files
COPY .env /app/.env
COPY credentials.json /app/
# ✅ REQUIRED: Mount secrets at runtime
# docker run -v /secrets:/app/secrets:ro myapp
# Or use Kubernetes secrets/configmaps
Production deployments must require approval and be restricted to main branch.
# ❌ FORBIDDEN: Direct production deploy without protection
deploy:
runs-on: ubuntu-latest
steps:
- run: deploy-to-prod.sh
# ✅ REQUIRED: Environment protection
deploy:
runs-on: ubuntu-latest
environment:
name: production
url: https://myapp.com
# Requires: approval + main branch only
| Scenario | Tool/Pattern | Reason |
|----------|--------------|--------|
| Public GitHub project | GitHub Actions | Native integration, free for public repos |
| Enterprise GitLab | GitLab CI | Unified platform, advanced security scanning |
| Multi-cloud IaC | Terraform | Mature ecosystem, wide provider support |
| Developer-centric IaC | Pulumi | Real programming languages, better testing |
| Kubernetes deployments | ArgoCD + Kustomize | GitOps standard, declarative config |
| Zero-downtime releases | Blue-Green or Canary | Instant rollback capability |
| Gradual feature rollout | Feature flags (LaunchDarkly) | Progressive delivery with targeting |
| Strategy | Downtime | Cost | Rollback Speed | Complexity | Best For |
|----------|----------|------|----------------|------------|----------|
| Rolling | Minimal | Low | Medium | Low | Regular updates, cost-conscious |
| Blue-Green | Zero | High (2x) | Instant | Medium | Critical systems, easy rollback |
| Canary | Zero | Medium | Fast | High | Risk mitigation, data-driven |
| Recreate | High | Low | N/A | Very Low | Non-critical, dev/test only |
# Short-lived credentials (not static keys)
- name: Configure AWS Credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::123456789012:role/GitHubActions
aws-region: us-east-1
# OIDC provider - no long-lived secrets!
# Protected environments for production
environment:
name: production
# Requires approval + restricts to main branch
# Example: conditional job execution
jobs:
backend-tests:
if: contains(github.event.head_commit.modified, 'backend/')
runs-on: ubuntu-latest
/\
/E2E\ <- Few (slow, expensive)
/------\
/Integration\ <- Some (medium speed)
/------------\
/ Unit Tests \ <- Many (fast, cheap)
/----------------\
# Multi-layer security scanning
jobs:
security:
runs-on: ubuntu-latest
steps:
# SAST - Static code analysis
- uses: github/codeql-action/init@v3
# SCA - Dependency vulnerabilities
- name: Run Trivy
uses: aquasecurity/trivy-action@master
with:
scan-type: 'fs'
format: 'sarif'
# Secret scanning
- name: Gitleaks
uses: gitleaks/gitleaks-action@v2
# Container scanning
- name: Scan Docker image
run: trivy image myapp:${{ github.sha }}
# Build stage - includes build tools (900MB+)
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
# Runtime stage - minimal image (<100MB)
FROM node:20-alpine AS runtime
RUN addgroup -g 1001 -S nodejs && \
adduser -S nodejs -u 1001
WORKDIR /app
COPY --from=builder --chown=nodejs:nodejs /app/node_modules ./node_modules
COPY --chown=nodejs:nodejs . .
USER nodejs
EXPOSE 3000
CMD ["node", "server.js"]
alpine, distroless, or scratchdocker run --read-only# Security best practices example
FROM gcr.io/distroless/nodejs20-debian12
COPY --chown=65532:65532 /app /app
USER 65532
EXPOSE 8080
# Version control
.git
.gitignore
# Dependencies (install fresh in container)
node_modules
vendor/
*.pyc
__pycache__
# Secrets and configs
.env
.env.local
secrets/
*.key
*.pem
# Development files
README.md
Dockerfile
docker-compose.yml
.vscode/
.idea/
# Testing and CI
tests/
*.test.js
.github/
# 99.94% of clusters are over-provisioned!
# Average CPU usage: 10%, Memory: 23%
resources:
requests:
memory: "128Mi" # Guaranteed allocation
cpu: "100m" # 0.1 CPU cores
limits:
memory: "256Mi" # Maximum allowed
cpu: "200m" # Hard cap
# Use tools: Kubecost, Goldilocks, VPA
# Liveness: Is container alive?
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
timeoutSeconds: 5
failureThreshold: 3
# Readiness: Can it receive traffic?
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 5
successThreshold: 1
# Startup: Has initialization completed?
startupProbe:
httpGet:
path: /startup
port: 8080
failureThreshold: 30 # 30*10s = 5min for slow starts
periodSeconds: 10
# Group related resources in single manifest
---
apiVersion: v1
kind: ConfigMap
metadata:
name: app-config
data:
APP_ENV: production
LOG_LEVEL: info
---
apiVersion: v1
kind: Secret
metadata:
name: app-secrets
type: Opaque
stringData:
DATABASE_URL: postgresql://user:pass@db:5432/mydb
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
spec:
template:
spec:
containers:
- name: app
envFrom:
- configMapRef:
name: app-config
- secretRef:
name: app-secrets
# Pod Security Standards
securityContext:
runAsNonRoot: true
runAsUser: 1000
fsGroup: 1000
seccompProfile:
type: RuntimeDefault
capabilities:
drop:
- ALL
# Network Policies (deny-by-default)
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: deny-all-ingress
spec:
podSelector: {}
policyTypes:
- Ingress
Detailed material starting at ## Infrastructure as Code (Terraform/Pulumi) has been moved to reference/extended.md to keep this skill concise. Load that reference when the task requires the moved examples, command catalogs, checklists, platform details, or implementation templates.
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 majiayu000/devops-excellence 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 docker.
Without those the skill loads but fails at the first command.