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Aws Ecs Codedeploy Blue Green

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mizchi
aws-ecs-codedeploy-blue-green

ECS blue/green deployment patterns. Recommends ALB-native weighted target groups as the simpler default; covers CodeDeploy as a reference for teams that already use it or need pipeline-integrated rollback. Use when choosing or debugging ECS blue/green infrastructure.

Overview

Publishermizchi
Repositoryskills
Skill nameaws-ecs-codedeploy-blue-green
Stars
333
Forks
4
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Aws Ecs Codedeploy Blue Green 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/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/aws-ecs-codedeploy-blue-green .claude/skills/aws-ecs-codedeploy-blue-green
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Ecs Codedeploy Blue Green 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 Aws Ecs Codedeploy Blue Green 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 Aws Ecs Codedeploy Blue Green 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.

AWS ECS Blue/Green Deployments

Recommendation: ALB-Native Weighted Routing (Preferred)

CodeDeploy adds IAM roles, appspec.json wiring, and a separate control plane. For most ECS blue/green needs, ALB weighted target groups achieve the same result with less setup.

How it works

Define two target groups (blue/green) and a single listener rule that splits traffic by weight:

hcl
resource "aws_lb_target_group" "blue" {
  name        = "myapp-blue"
  port        = 8080
  protocol    = "HTTP"
  vpc_id      = var.vpc_id
  target_type = "ip"

  health_check {
    path                = "/health"
    interval            = 15
    healthy_threshold   = 2
    unhealthy_threshold = 3
  }
}

resource "aws_lb_target_group" "green" {
  # identical to blue
  name = "myapp-green"
  # ...
}

resource "aws_lb_listener_rule" "weighted" {
  listener_arn = aws_lb_listener.main.arn
  priority     = 100

  condition {
    path_pattern { values = ["/*"] }
  }

  action {
    type = "forward"
    forward {
      target_group {
        arn    = aws_lb_target_group.blue.arn
        weight = 100
      }
      target_group {
        arn    = aws_lb_target_group.green.arn
        weight = 0
      }
      stickiness {
        enabled  = true
        duration = 300  # keep users on the same TG during rollout
      }
    }
  }
}

Traffic shift procedure

bash
# Canary: send 10% to green
aws elbv2 modify-rule --rule-arn <rule-arn> \
  --actions '[{"Type":"forward","ForwardConfig":{"TargetGroups":[{"TargetGroupArn":"<blue-arn>","Weight":90},{"TargetGroupArn":"<green-arn>","Weight":10}]}}]'

# Full cutover
aws elbv2 modify-rule --rule-arn <rule-arn> \
  --actions '[{"Type":"forward","ForwardConfig":{"TargetGroups":[{"TargetGroupArn":"<blue-arn>","Weight":0},{"TargetGroupArn":"<green-arn>","Weight":100}]}}]'

# Rollback: flip back to blue
aws elbv2 modify-rule --rule-arn <rule-arn> \
  --actions '[{"Type":"forward","ForwardConfig":{"TargetGroups":[{"TargetGroupArn":"<blue-arn>","Weight":100},{"TargetGroupArn":"<green-arn>","Weight":0}]}}]'

Or update weights via Terraform and apply. No appspec, no CodeDeploy IAM role, no separate control plane.

When to use CodeDeploy instead

  • Your team already has a CodeDeploy pipeline and wants to keep it
  • You need automatic rollback triggered by CloudWatch alarms without custom scripts
  • You need hooks (BeforeInstall, AfterInstall, AfterAllowTraffic) for migration/smoke steps

CodeDeploy ECS Blue/Green (Reference)

The Critical Non-Obvious Part: lifecycle.ignore_changes

When using CodeDeploy to manage ECS blue/green deployments, CodeDeploy dynamically swaps the ALB listener's default_action.target_group_arn between the blue and green target groups. If you don't suppress this in OpenTofu/Terraform, every subsequent tofu plan will show drift and try to restore the original target group — fighting with CodeDeploy on every deployment.

Fix: always add lifecycle.ignore_changes to the listener:

hcl
resource "aws_lb_listener" "bg_demo" {
  load_balancer_arn = aws_lb.main.arn
  port              = 8080
  protocol          = "HTTP"

  default_action {
    type             = "forward"
    target_group_arn = aws_lb_target_group.bg_demo_blue.arn  # initial state only
  }

  # CodeDeploy swaps default_action.target_group_arn between blue and green.
  # Without this, tofu plan constantly shows drift.
  lifecycle {
    ignore_changes = [default_action]
  }
}

Full Pattern

Two Target Groups (blue and green)
hcl
resource "aws_lb_target_group" "bg_demo_blue" {
  name        = "myapp-blue"
  port        = 8080
  protocol    = "HTTP"
  vpc_id      = var.vpc_id
  target_type = "ip"

  health_check {
    path                = "/health"
    interval            = 15
    healthy_threshold   = 2
    unhealthy_threshold = 3
  }

  deregistration_delay = 10  # short for faster deployments
}

resource "aws_lb_target_group" "bg_demo_green" {
  # identical to blue
  name = "myapp-green"
  # ...
}
CodeDeploy App + Deployment Group
hcl
resource "aws_iam_role" "codedeploy_ecs" {
  name = "myapp-codedeploy-ecs"
  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [{ Effect = "Allow", Principal = { Service = "codedeploy.amazonaws.com" }, Action = "sts:AssumeRole" }]
  })
}

resource "aws_iam_role_policy_attachment" "codedeploy_ecs" {
  role       = aws_iam_role.codedeploy_ecs.name
  policy_arn = "arn:aws:iam::aws:policy/AWSCodeDeployRoleForECS"
}

resource "aws_codedeploy_app" "app" {
  compute_platform = "ECS"
  name             = "myapp"
}

resource "aws_codedeploy_deployment_group" "app" {
  app_name              = aws_codedeploy_app.app.name
  deployment_group_name = "myapp-dg"
  service_role_arn      = aws_iam_role.codedeploy_ecs.arn

  deployment_style {
    deployment_option = "WITH_TRAFFIC_CONTROL"
    deployment_type   = "BLUE_GREEN"
  }

  blue_green_deployment_config {
    deployment_ready_option {
      action_on_timeout = "CONTINUE_DEPLOYMENT"  # auto-shift, no manual confirmation
    }
    terminate_blue_instances_on_deployment_success {
      action                           = "TERMINATE"
      termination_wait_time_in_minutes = 5  # give blue tasks 5 min to finish in-flight requests
    }
  }

  # Traffic shift configs:
  # CodeDeployDefault.ECSCanary10Percent5Minutes  — 10% for 5 min, then 100%
  # CodeDeployDefault.ECSLinear10PercentEvery1Minutes — 10% per minute (10 min total)
  # CodeDeployDefault.ECSAllAtOnce               — immediate 100%
  deployment_config_name = "CodeDeployDefault.ECSCanary10Percent5Minutes"

  ecs_service {
    cluster_name = aws_ecs_cluster.main.name
    service_name = aws_ecs_service.app.name
  }

  load_balancer_info {
    target_group_pair_info {
      prod_traffic_route {
        listener_arns = [aws_lb_listener.bg_demo.arn]  # listener ARN, not rule ARN
      }
      target_group { name = aws_lb_target_group.bg_demo_blue.name }
      target_group { name = aws_lb_target_group.bg_demo_green.name }
    }
  }

  auto_rollback_configuration {
    enabled = true
    events  = ["DEPLOYMENT_FAILURE", "DEPLOYMENT_STOP_ON_ALARM"]
  }

  alarm_configuration {
    enabled = true
    alarms  = [aws_cloudwatch_metric_alarm.blue_5xx.alarm_name, aws_cloudwatch_metric_alarm.green_5xx.alarm_name]
  }
}
CloudWatch Alarms for Auto-Rollback

When DEPLOYMENT_STOP_ON_ALARM is set, CodeDeploy monitors these alarms during the canary phase. If any alarm fires, the deployment stops and rolls back.

hcl
resource "aws_cloudwatch_metric_alarm" "blue_5xx" {
  alarm_name          = "myapp-blue-5xx"
  metric_name         = "HTTPCode_Target_5XX_Count"
  namespace           = "AWS/ApplicationELB"
  statistic           = "Sum"
  period              = 60
  evaluation_periods  = 1
  threshold           = 1   # production: use 5-10 to reduce false positives
  comparison_operator = "GreaterThanOrEqualToThreshold"
  treat_missing_data  = "notBreaching"
  dimensions = {
    LoadBalancer = aws_lb.main.arn_suffix
    TargetGroup  = aws_lb_target_group.bg_demo_blue.arn_suffix
  }
}

# identical for green

Deployment Trigger (appspec.json)

CodeDeploy needs an appspec.json that points to the new task definition. This is passed at deploy time, not managed by Terraform:

json
{
  "version": 1,
  "Resources": [{
    "TargetService": {
      "Type": "AWS::ECS::Service",
      "Properties": {
        "TaskDefinition": "<NEW_TASK_DEF_ARN>",
        "LoadBalancerInfo": {
          "ContainerName": "app",
          "ContainerPort": 8080
        }
      }
    }
  }]
}
bash
aws deploy create-deployment \
  --application-name myapp \
  --deployment-group-name myapp-dg \
  --revision revisionType=AppSpecContent,appSpecContent={content="$(cat appspec.json)"}

Common Pitfalls

  • prod_traffic_route.listener_arns must be a listener ARN, not a listener rule ARN. Using a rule ARN here causes CodeDeploy to fail silently or with a confusing error.
  • Both blue and green target groups must have identical health check configuration. Mismatched healthy_threshold / unhealthy_threshold values will cause one TG to always be considered unhealthy.
  • desired_count on aws_ecs_service should not change during blue/green. CodeDeploy manages task counts independently during deployment.
  • CloudWatch alarm threshold for production: use 5–10 for threshold, not 1. A single stray 5xx during the canary window will trigger a rollback on production load.

Frequently asked questions

What does the Aws Ecs Codedeploy Blue Green AI skill do?

ECS blue/green deployment patterns. Recommends ALB-native weighted target groups as the simpler default; covers CodeDeploy as a reference for teams that already use it or need pipeline-integrated rollback. Use when choosing or debugging ECS blue/green infrastructure.

Why use Aws Ecs Codedeploy Blue Green on TypingMind?

Because you install it once and use it with any model. Aws Ecs Codedeploy Blue Green 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 Aws Ecs Codedeploy Blue Green in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mizchi/skills/tree/main/aws-ecs-codedeploy-blue-green. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aws Ecs Codedeploy Blue Green?

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 Aws Ecs Codedeploy Blue Green?

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

Is the Aws Ecs Codedeploy Blue Green AI skill free?

It is published on GitHub by mizchi. Check the repository for licensing terms. 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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