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Devops Excellence

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majiayu000
devops-excellence

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.

Overview

Publishermajiayu000
Repositoryspellbook
Skill namedevops-excellence
Stars
280
Forks
26
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by majiayu000 on GitHub. Read the source before you install it.

Installation

Install the Devops Excellence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/majiayu000/spellbook.git /tmp/spellbook
mkdir -p .claude/skills
cp -r /tmp/spellbook/skills/devops-excellence .claude/skills/devops-excellence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Devops Excellence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Devops Excellence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Devops Excellence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DevOps Excellence

Core Principles

  • Shift Left — Address security and quality early in SDLC
  • GitOps — Git as single source of truth for infrastructure and deployments
  • Infrastructure as Code — All infrastructure versioned and reproducible
  • Progressive Delivery — Gradual rollouts with feature flags and canary releases
  • Immutable Infrastructure — Replace, don't modify running systems
  • Observability-First — Monitor metrics tied to deployments and features
  • Policy as Code — Enforce compliance and security automatically
  • Platform Engineering — Build golden paths and self-service portals

Hard Rules (Must Follow)

These rules are mandatory. Violating them means the skill is not working correctly.

No Static Credentials

Never use long-lived static credentials. Always use OIDC or short-lived tokens.

yaml
# ❌ 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

No Root Containers

Containers must NEVER run as root. Always specify a non-root user.

dockerfile
# ❌ 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"]

No Secrets in Images

Never bake secrets into Docker images. Use runtime injection or secrets managers.

dockerfile
# ❌ 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

Protected Production Deployments

Production deployments must require approval and be restricted to main branch.

yaml
# ❌ 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

Quick Reference

When to Use What

ScenarioTool/PatternReason
Public GitHub projectGitHub ActionsNative integration, free for public repos
Enterprise GitLabGitLab CIUnified platform, advanced security scanning
Multi-cloud IaCTerraformMature ecosystem, wide provider support
Developer-centric IaCPulumiReal programming languages, better testing
Kubernetes deploymentsArgoCD + KustomizeGitOps standard, declarative config
Zero-downtime releasesBlue-Green or CanaryInstant rollback capability
Gradual feature rolloutFeature flags (LaunchDarkly)Progressive delivery with targeting

Deployment Strategy Selection

StrategyDowntimeCostRollback SpeedComplexityBest For
RollingMinimalLowMediumLowRegular updates, cost-conscious
Blue-GreenZeroHigh (2x)InstantMediumCritical systems, easy rollback
CanaryZeroMediumFastHighRisk mitigation, data-driven
RecreateHighLowN/AVery LowNon-critical, dev/test only

CI/CD Pipeline Best Practices

Pipeline Security

yaml
# 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

Speed Optimization

  • 10-minute build rule — Most projects should build in <10 minutes
  • Parallel jobs — Run tests, linting, security scans concurrently
  • Cache dependencies — Cache node_modules, .m2, pip packages
  • Conditional execution — Skip jobs when files haven't changed
yaml
# Example: conditional job execution
jobs:
  backend-tests:
    if: contains(github.event.head_commit.modified, 'backend/')
    runs-on: ubuntu-latest

Testing Pyramid

              /\
             /E2E\        <- Few (slow, expensive)
            /------\
           /Integration\ <- Some (medium speed)
          /------------\
         /  Unit Tests  \ <- Many (fast, cheap)
        /----------------\
  • 70% Unit tests (fast, isolated)
  • 20% Integration tests (service interactions)
  • 10% E2E tests (full user workflows)

Security Scanning Integration

yaml
# 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 }}

Docker Best Practices

Multi-Stage Builds

dockerfile
# 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"]

Security Hardening

  • Non-root user — ALWAYS run as non-root (UID 1001)
  • Minimal base images — Use alpine, distroless, or scratch
  • Read-only filesystemdocker run --read-only
  • No secrets in layers — Use build secrets or external vaults
  • Resource limits — Set CPU/memory limits to prevent DoS
  • Signed images — Enable Docker Content Trust
dockerfile
# Security best practices example
FROM gcr.io/distroless/nodejs20-debian12
COPY --chown=65532:65532 /app /app
USER 65532
EXPOSE 8080

.dockerignore

# 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/

Kubernetes Deployment Patterns

Resource Management (Right-Sizing)

yaml
# 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

Health Checks

yaml
# 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

ConfigMaps and Secrets

yaml
# 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

Security Best Practices

yaml
# 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

Extended Reference

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Devops Excellence AI skill do?

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.

Why use Devops Excellence on TypingMind?

Because you install it once and use it with any model. Devops Excellence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Devops Excellence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/majiayu000/spellbook/tree/main/skills/devops-excellence. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Devops Excellence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Devops Excellence?

As many as you like. As long as a model supports skills, you can use Devops Excellence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Devops Excellence AI skill free?

Yes. It is published on GitHub by majiayu000 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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