Agent skill

Deployment Patterns

by affaan-m in affaan-m/ECC

Deployment iş akışları, CI/CD pipeline kalıpları, Docker konteynerizasyonu, sağlık kontrolleri, rollback stratejileri ve web uygulamaları için üretim hazırlığı kontrol listeleri.

MITAuto-check passedDevOps & Cloud

Install Deployment Patterns

skills CLI
$ npx skills add affaan-m/ECC --skill deployment-patterns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC deployment-patterns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/tr/skills/deployment-patterns .claude/skills/deployment-patterns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deployment-patterns
GitHub stars
275k
Used in
1 other repo
Token cost
~2.9k tokens
SKILL.md length
379 words
Files
1
Skills in repo
645
Repo updated
First seen
Licence
MIT

At a glance

Deployment iş akışları, CI/CD pipeline kalıpları, Docker konteynerizasyonu, sağlık kontrolleri, rollback stratejileri ve web uygulamaları için üretim hazırlığı kontrol listeleri.

  • Tasks that involve Deployment
  • SKILL.md covers Ne Zaman Aktifleştirmeli, Deployment Stratejileri, Docker and CI/CD Pipeline, plus 4 more sections
  • Calls kubectl, vercel and railway; needs API_KEY and GITHUB_TOKEN
  • Tasks that involve Containers

What it does

Deployment Patterns is an agent skill from affaan-m/ECC. Deployment iş akışları, CI/CD pipeline kalıpları, Docker konteynerizasyonu, sağlık kontrolleri, rollback stratejileri ve web uygulamaları için üretim hazırlığı kontrol listeleri.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Deployment, Containers and CI/CD. It works with Docker. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Tasks that involve Deployment
  • Tasks that involve Containers
  • Tasks that involve CI/CD

Example prompts

  • “/deployment-patterns”

Requirements

  • Python 3
  • Node.js
  • Docker
  • A credential in GITHUB_TOKEN
  • A credential in API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • kubectl
    • vercel
    • railway
    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use kubectl, vercel and npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • API_KEY
    • GITHUB_TOKEN
    • JWT_SECRET

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deployment Patterns loads about 2.9k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 379 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 379 words, ~2,866 tokens.

Download SKILL.mdSave it as .claude/skills/deployment-patterns/SKILL.md (or your agent's skills folder).
name
deployment-patterns
description
Deployment iş akışları, CI/CD pipeline kalıpları, Docker konteynerizasyonu, sağlık kontrolleri, rollback stratejileri ve web uygulamaları için üretim hazırlığı kontrol listeleri.
origin
ECC

Deployment Kalıpları

Üretim deployment iş akışları ve CI/CD en iyi uygulamaları.

Ne Zaman Aktifleştirmeli

  • CI/CD pipeline'ları kurarken
  • Bir uygulamayı Docker'ize ederken
  • Deployment stratejisi planlarken (blue-green, canary, rolling)
  • Sağlık kontrolleri ve hazırlık probe'ları uygularken
  • Üretim yayınına hazırlanırken
  • Ortama özgü ayarları yapılandırırken

Deployment Stratejileri

Rolling Deployment (Varsayılan)

Instance'ları kademeli olarak değiştir — rollout sırasında eski ve yeni versiyonlar birlikte çalışır.

Instance 1: v1 → v2  (önce güncelle)
Instance 2: v1        (hala v1 çalışıyor)
Instance 3: v1        (hala v1 çalışıyor)

Instance 1: v2
Instance 2: v1 → v2  (ikinci olarak güncelle)
Instance 3: v1

Instance 1: v2
Instance 2: v2
Instance 3: v1 → v2  (son olarak güncelle)

Artıları: Sıfır kesinti, kademeli rollout Eksileri: İki versiyon aynı anda çalışır — geriye uyumlu değişiklikler gerektirir Ne zaman kullanılır: Standart deployment'lar, geriye uyumlu değişiklikler

Blue-Green Deployment

İki özdeş ortam çalıştır. Trafiği atomik olarak değiştir.

Blue  (v1) ← trafik
Green (v2)   boşta, yeni versiyon çalışıyor

# Doğrulamadan sonra:
Blue  (v1)   boşta (yedek haline gelir)
Green (v2) ← trafik

Artıları: Anında rollback (blue'ya geri dön), temiz geçiş Eksileri: Deployment sırasında 2x altyapı gerektirir Ne zaman kullanılır: Kritik servisler, sorunlara sıfır tolerans

Canary Deployment

Önce trafiğin küçük bir yüzdesini yeni versiyona yönlendir.

v1: %95 trafik
v2:  %5 trafik  (canary)

# Metrikler iyi görünüyorsa:
v1: %50 trafik
v2: %50 trafik

# Final:
v2: %100 trafik

Artıları: Tam rollout'tan önce gerçek trafikle sorunları yakalar Eksileri: Trafik bölme altyapısı, izleme gerektirir Ne zaman kullanılır: Yüksek trafikli servisler, riskli değişiklikler, feature flag'ler

Docker

Multi-Stage Dockerfile (Node.js)
dockerfile
# Stage 1: Bağımlılıkları yükle
FROM node:22-alpine AS deps
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci --production=false

# Stage 2: Build
FROM node:22-alpine AS builder
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .
RUN npm run build
RUN npm prune --production

# Stage 3: Production image
FROM node:22-alpine AS runner
WORKDIR /app

RUN addgroup -g 1001 -S appgroup && adduser -S appuser -u 1001
USER appuser

COPY --from=builder --chown=appuser:appgroup /app/node_modules ./node_modules
COPY --from=builder --chown=appuser:appgroup /app/dist ./dist
COPY --from=builder --chown=appuser:appgroup /app/package.json ./

ENV NODE_ENV=production
EXPOSE 3000

HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD wget --no-verbose --tries=1 --spider http://localhost:3000/health || exit 1

CMD ["node", "dist/server.js"]
Multi-Stage Dockerfile (Go)
dockerfile
FROM golang:1.22-alpine AS builder
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-s -w" -o /server ./cmd/server

FROM alpine:3.19 AS runner
RUN apk --no-cache add ca-certificates
RUN adduser -D -u 1001 appuser
USER appuser

COPY --from=builder /server /server

EXPOSE 8080
HEALTHCHECK --interval=30s --timeout=3s CMD wget -qO- http://localhost:8080/health || exit 1
CMD ["/server"]
Multi-Stage Dockerfile (Python/Django)
dockerfile
FROM python:3.12-slim AS builder
WORKDIR /app
RUN pip install --no-cache-dir uv
COPY requirements.txt .
RUN uv pip install --system --no-cache -r requirements.txt

FROM python:3.12-slim AS runner
WORKDIR /app

RUN useradd -r -u 1001 appuser
USER appuser

COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
COPY --from=builder /usr/local/bin /usr/local/bin
COPY . .

ENV PYTHONUNBUFFERED=1
EXPOSE 8000

HEALTHCHECK --interval=30s --timeout=3s CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health/')" || exit 1
CMD ["gunicorn", "config.wsgi:application", "--bind", "0.0.0.0:8000", "--workers", "4"]
Docker En İyi Uygulamaları
# İYİ uygulamalar
- Belirli versiyon tag'leri kullanın (node:22-alpine, node:latest değil)
- Image boyutunu minimize etmek için multi-stage build'ler
- Root olmayan kullanıcı olarak çalıştır
- Önce bağımlılık dosyalarını kopyalayın (layer caching)
- node_modules, .git, test'leri hariç tutmak için .dockerignore kullanın
- HEALTHCHECK talimatı ekleyin
- docker-compose veya k8s'te kaynak limitleri ayarlayın

# KÖTÜ uygulamalar
- Root olarak çalıştırmak
- :latest tag'lerini kullanmak
- Tüm repo'yu tek COPY layer'da kopyalamak
- Production image'de dev bağımlılıklarını yüklemek
- Image'de secret'ları saklamak (env var veya secrets manager kullanın)

CI/CD Pipeline

GitHub Actions (Standart Pipeline)
yaml
name: CI/CD

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 22
          cache: npm
      - run: npm ci
      - run: npm run lint
      - run: npm run typecheck
      - run: npm test -- --coverage
      - uses: actions/upload-artifact@v4
        if: always()
        with:
          name: coverage
          path: coverage/

  build:
    needs: test
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - uses: actions/checkout@v4
      - uses: docker/setup-buildx-action@v3
      - uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - uses: docker/build-push-action@v5
        with:
          push: true
          tags: ghcr.io/${{ github.repository }}:${{ github.sha }}
          cache-from: type=gha
          cache-to: type=gha,mode=max

  deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - name: Deploy to production
        run: |
          # Platforma özgü deployment komutu
          # Railway: railway up
          # Vercel: vercel --prod
          # K8s: kubectl set image deployment/app app=ghcr.io/${{ github.repository }}:${{ github.sha }}
          echo "Deploying ${{ github.sha }}"
Pipeline Aşamaları
PR açıldığında:
  lint → typecheck → unit tests → integration tests → preview deploy

Main'e merge edildiğinde:
  lint → typecheck → unit tests → integration tests → build image → deploy staging → smoke tests → deploy production

Sağlık Kontrolleri

Sağlık Kontrolü Endpoint'i
typescript
// Basit sağlık kontrolü
app.get("/health", (req, res) => {
  res.status(200).json({ status: "ok" });
});

// Detaylı sağlık kontrolü (dahili izleme için)
app.get("/health/detailed", async (req, res) => {
  const checks = {
    database: await checkDatabase(),
    redis: await checkRedis(),
    externalApi: await checkExternalApi(),
  };

  const allHealthy = Object.values(checks).every(c => c.status === "ok");

  res.status(allHealthy ? 200 : 503).json({
    status: allHealthy ? "ok" : "degraded",
    timestamp: new Date().toISOString(),
    version: process.env.APP_VERSION || "unknown",
    uptime: process.uptime(),
    checks,
  });
});

async function checkDatabase(): Promise<HealthCheck> {
  try {
    await db.query("SELECT 1");
    return { status: "ok", latency_ms: 2 };
  } catch (err) {
    return { status: "error", message: "Database unreachable" };
  }
}
Kubernetes Probe'ları
yaml
livenessProbe:
  httpGet:
    path: /health
    port: 3000
  initialDelaySeconds: 10
  periodSeconds: 30
  failureThreshold: 3

readinessProbe:
  httpGet:
    path: /health
    port: 3000
  initialDelaySeconds: 5
  periodSeconds: 10
  failureThreshold: 2

startupProbe:
  httpGet:
    path: /health
    port: 3000
  initialDelaySeconds: 0
  periodSeconds: 5
  failureThreshold: 30    # 30 * 5s = 150s max başlatma süresi

Ortam Yapılandırması

Twelve-Factor App Kalıbı
bash
# Tüm yapılandırma ortam değişkenleri ile — asla kodda değil
DATABASE_URL=postgres://user:pass@host:5432/db
REDIS_URL=redis://host:6379/0
API_KEY=${API_KEY}           # secrets manager tarafından enjekte edilir
LOG_LEVEL=info
PORT=3000

# Ortama özgü davranış
NODE_ENV=production          # veya staging, development
APP_ENV=production           # açık uygulama ortamı
Yapılandırma Validasyonu
typescript
import { z } from "zod";

const envSchema = z.object({
  NODE_ENV: z.enum(["development", "staging", "production"]),
  PORT: z.coerce.number().default(3000),
  DATABASE_URL: z.string().url(),
  REDIS_URL: z.string().url(),
  JWT_SECRET: z.string().min(32),
  LOG_LEVEL: z.enum(["debug", "info", "warn", "error"]).default("info"),
});

// Başlangıçta validasyon yap — yapılandırma yanlışsa hızlı başarısız ol
export const env = envSchema.parse(process.env);

Rollback Stratejisi

Anında Rollback
bash
# Docker/Kubernetes: önceki image'a işaret et
kubectl rollout undo deployment/app

# Vercel: önceki deployment'ı yükselt
vercel rollback

# Railway: önceki commit'i tekrar deploy et
railway up --commit <previous-sha>

# Veritabanı: migration'ı rollback et (geri alınabilirse)
npx prisma migrate resolve --rolled-back <migration-name>
Rollback Kontrol Listesi
  • Önceki image/artifact mevcut ve tag'lenmiş
  • Veritabanı migration'ları geriye uyumlu (yıkıcı değişiklik yok)
  • Feature flag'ler deploy olmadan yeni özellikleri devre dışı bırakabilir
  • Hata oranı artışları için izleme alarmları yapılandırılmış
  • Rollback üretim yayınından önce staging'de test edilmiş
Show full SKILL.md (155 more words)Show less

Üretim Hazırlığı Kontrol Listesi

Herhangi bir üretim deployment'ından önce:

Uygulama
  • Tüm testler geçiyor (unit, integration, E2E)
  • Kodda veya yapılandırma dosyalarında hardcode edilmiş secret yok
  • Hata işleme tüm edge case'leri kapsıyor
  • Loglama yapılandırılmış (JSON) ve PII içermiyor
  • Sağlık kontrolü endpoint'i anlamlı durum döndürüyor
Altyapı
  • Docker image yeniden üretilebilir şekilde build oluyor (sabitlenmiş versiyonlar)
  • Ortam değişkenleri dokümante edilmiş ve başlangıçta validate ediliyor
  • Kaynak limitleri ayarlanmış (CPU, bellek)
  • Horizontal scaling yapılandırılmış (min/max instance'lar)
  • Tüm endpoint'lerde SSL/TLS etkin
İzleme
  • Uygulama metrikleri export ediliyor (istek oranı, gecikme, hatalar)
  • Hata oranı > eşik için alarmlar yapılandırılmış
  • Log toplama kurulmuş (yapılandırılmış loglar, aranabilir)
  • Sağlık endpoint'inde uptime izleme
Güvenlik
  • Bağımlılıklar CVE'ler için taranmış
  • CORS sadece izin verilen origin'ler için yapılandırılmış
  • Halka açık endpoint'lerde hız sınırlama etkin
  • Kimlik doğrulama ve yetkilendirme doğrulanmış
  • Güvenlik header'ları ayarlanmış (CSP, HSTS, X-Frame-Options)
Operasyonlar
  • Rollback planı dokümante edilmiş ve test edilmiş
  • Veritabanı migration'ı üretim boyutundaki veriye karşı test edilmiş
  • Yaygın hata senaryoları için runbook
  • Nöbet rotasyonu ve yükseltme yolu tanımlanmış

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in docs/tr/skills/deployment-patterns of affaan-m/ECC.

Open the folder on GitHubat commit ef648e0

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Deployment Patterns compared with similar skills
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Devops InfrastructureCloudAI-X/claude-workflow-v21.4k—~2.7kAutomated safety check: NotesMIT
CI/CD Pipeline Principlesirahardianto/awesome-agv157—~2.7kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Deployment Patterns

What does Deployment Patterns do?

Deployment iş akışları, CI/CD pipeline kalıpları, Docker konteynerizasyonu, sağlık kontrolleri, rollback stratejileri ve web uygulamaları için üretim hazırlığı kontrol listeleri. Deployment Patterns is an agent skill from affaan-m/ECC. Deployment iş akışları, CI/CD pipeline kalıpları, Docker konteynerizasyonu, sağlık kontrolleri, rollback stratejileri ve web uygulamaları için üretim hazırlığı kontrol listeleri.

When should I use Deployment Patterns?

Deployment Patterns fits situations like: tasks that involve Deployment; tasks that involve Containers; tasks that involve CI/CD.

How do I install Deployment Patterns in Claude Code?

Run `npx skills add affaan-m/ECC --skill deployment-patterns -a claude-code`. Or copy the skill folder (docs/tr/skills/deployment-patterns in affaan-m/ECC) into .claude/skills/deployment-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Deployment Patterns in Codex?

Run `npx skills add affaan-m/ECC --skill deployment-patterns -a codex`. Or copy the skill folder (docs/tr/skills/deployment-patterns in affaan-m/ECC) into .agents/skills/deployment-patterns in your project. Codex loads it when a task matches its description.

Can I use Deployment Patterns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill deployment-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deployment-patterns, .gemini/skills/deployment-patterns, .github/skills/deployment-patterns and .opencode/skills/deployment-patterns in your project.

What does Deployment Patterns need to run?

Going by SKILL.md and its folder, Deployment Patterns needs the command-line tools its instructions call (kubectl, vercel, railway and npx) and credentials named API_KEY, GITHUB_TOKEN and JWT_SECRET. Our summary lists: Python 3; Node.js; Docker; A credential in GITHUB_TOKEN; A credential in API_KEY.

Does Deployment Patterns access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Deployment Patterns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Deployment Patterns use?

Deployment Patterns is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deployment Patterns use?

About 2.9k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Deployment Patterns?

Skills that share tags, products or a category with Deployment Patterns: Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), DDNS Build and Release Maintenance (NewFuture/DDNS, 4.7k stars), Devops Engineer (Yikai-Liao/symusic, 189 stars) and Devops Infrastructure (CloudAI-X/claude-workflow-v2, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deployment Patterns?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,023 GitHub stars. The repository holds 645 skills in this directory. The repository was last updated on October 5, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.