Agent skill

Load Balancing Patterns

by ancoleman in ancoleman/ai-design-components

When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes…

MITAuto-check passedDevOps & Cloud

Install Load Balancing Patterns

skills CLI
$ npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a claude-code

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

GitHub CLI
$ gh skill install ancoleman/ai-design-components load-balancing-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/ancoleman/ai-design-components.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/load-balancing-patterns .claude/skills/load-balancing-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
load-balancing-patterns
GitHub stars
526
Token cost
~3.8k tokens
SKILL.md length
1,498 words
Files
21 (incl. references)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes…

  • Select and configure the appropriate load balancing solution (L4/L7
  • SKILL.md covers When to Use This Skill, Core Load Balancing Concepts, Cloud Load Balancers and Self-Managed Load Balancers, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Kubernetes ingress) with proper health checks and session management

What it does

Load Balancing Patterns is an agent skill from ancoleman/ai-design-components. When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes ingress) with proper health checks and session management.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 28 other files, including reference files (for example `examples/envoy/basic-lb.yaml`, `examples/kubernetes/gateway-api.yaml` and `examples/kubernetes/nginx-ingress.yaml`).

It sits in DevOps & Cloud, covering Cloud networking. It works with Kubernetes. The repository describes itself as: Comprehensive UI/UX and Backend component design skills for AI-assisted development with Claude. The licence is MIT.

When your agent uses it

  • Select and configure the appropriate load balancing solution (L4/L7
  • Kubernetes ingress) with proper health checks and session management

Example prompts

  • “/load-balancing-patterns”

Requirements

  • Docker

What it can do on your machine

Read from SKILL.md and the folder at commit 76551b7. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are nginx, haproxy and yaml).

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

  • Network

    No URLs in SKILL.md.

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Load Balancing Patterns loads about 3.8k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 1,498 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~19k

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 ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 1,498 words, ~3,815 tokens.

Download SKILL.mdSave it as .claude/skills/load-balancing-patterns/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.
name
load-balancing-patterns
description
When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes ingress) with proper health checks and session management.

Load Balancing Patterns

Distribute traffic across infrastructure using the appropriate load balancing approach, from simple round-robin to global multi-region failover.

When to Use This Skill

Use load-balancing-patterns when:

  • Distributing traffic across multiple application servers
  • Implementing high availability and failover
  • Routing traffic based on URLs, headers, or geographic location
  • Managing session persistence across stateless backends
  • Deploying applications to Kubernetes clusters
  • Configuring global traffic management across regions
  • Implementing zero-downtime deployments (blue-green, canary)
  • Selecting between cloud-managed and self-managed load balancers

Core Load Balancing Concepts

Layer 4 vs Layer 7

Layer 4 (L4) - Transport Layer:

  • Routes based on IP address and port (TCP/UDP packets)
  • No application data inspection, lower latency, higher throughput
  • Protocol agnostic, preserves client IP addresses
  • Use for: Database connections, video streaming, gaming, financial transactions, non-HTTP protocols

Layer 7 (L7) - Application Layer:

  • Routes based on HTTP URLs, headers, cookies, request body
  • Full application data visibility, SSL/TLS termination, caching, WAF integration
  • Content-based routing capabilities
  • Use for: Web applications, REST APIs, microservices, GraphQL endpoints, complex routing logic

For detailed comparison including performance benchmarks and hybrid approaches, see references/l4-vs-l7-comparison.md.

Load Balancing Algorithms
AlgorithmDistribution MethodUse Case
Round RobinSequentialStateless, similar servers
Weighted Round RobinCapacity-basedDifferent server specs
Least ConnectionsFewest active connectionsLong-lived connections
Least Response TimeFastest serverPerformance-sensitive
IP HashClient IP-basedSession persistence
Resource-BasedCPU/memory metricsVarying workloads
Health Check Types

Shallow (Liveness): Is the process alive?

  • Endpoint: /health/live or /live
  • Returns: 200 if process running
  • Use for: Process monitoring, container health

Deep (Readiness): Can the service handle requests?

  • Endpoint: /health/ready or /ready
  • Validates: Database, cache, external API connectivity
  • Use for: Load balancer routing decisions

Health Check Hysteresis: Different thresholds for marking up vs down to prevent flapping

  • Example: 3 failures to mark down, 2 successes to mark up

For complete health check implementation patterns, see references/health-check-strategies.md.

Cloud Load Balancers

AWS Load Balancing

Application Load Balancer (ALB) - Layer 7:

  • Use for: HTTP/HTTPS applications, microservices, WebSocket
  • Features: Path/host/header routing, AWS WAF integration, Lambda targets
  • Choose when: Content-based routing needed

Network Load Balancer (NLB) - Layer 4:

  • Use for: Ultra-low latency (<1ms), TCP/UDP, static IPs, millions RPS
  • Features: Preserves source IP, TLS termination
  • Choose when: Non-HTTP protocols, performance critical

Global Accelerator - Layer 4 Global:

  • Use for: Multi-region applications, global users, DDoS protection
  • Features: Anycast IPs, automatic regional failover
GCP Load Balancing

Application LB (L7): Global HTTPS LB, Cloud CDN integration, Cloud Armor (WAF/DDoS) Network LB (L4): Regional TCP/UDP, pass-through balancing, session affinity Cloud Load Balancing: Single anycast IP, global distribution, backend buckets

Azure Load Balancing

Application Gateway (L7): WAF integration, URL-based routing, SSL termination, autoscaling Load Balancer (L4): Basic and Standard SKUs, health probes, HA ports Traffic Manager (Global): DNS-based routing (priority, weighted, performance, geographic)

For complete cloud provider configurations and Terraform examples, see references/cloud-load-balancers.md.

Self-Managed Load Balancers

NGINX

Best for: General-purpose HTTP/HTTPS load balancing, web application stacks

Capabilities:

  • HTTP reverse proxy with multiple algorithms
  • TCP/UDP stream load balancing
  • SSL/TLS termination
  • Passive health checks (open source), active health checks (NGINX Plus)
  • Cookie-based sticky sessions (NGINX Plus)

Basic configuration:

nginx
upstream backend {
    least_conn;
    server backend1.example.com:8080 weight=3;
    server backend2.example.com:8080 weight=2;
    keepalive 32;
}

server {
    listen 80;
    location / {
        proxy_pass http://backend;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

For complete NGINX patterns and advanced configurations, see references/nginx-patterns.md.

HAProxy

Best for: Maximum performance, database load balancing, resource efficiency

Capabilities:

  • Highest raw throughput, lowest memory footprint
  • 10+ load balancing algorithms
  • Sophisticated health checks (HTTP, TCP, Redis, MySQL, etc.)
  • Cookie or IP-based persistence

Basic configuration:

haproxy
frontend http_front
    bind *:80
    default_backend web_servers

backend web_servers
    balance roundrobin
    option httpchk GET /health
    server web1 192.168.1.101:8080 check
    server web2 192.168.1.102:8080 check

For complete HAProxy patterns, see references/haproxy-patterns.md.

Envoy

Best for: Microservices, Kubernetes, service mesh integration

Capabilities:

  • Cloud-native design with dynamic configuration (xDS APIs)
  • Circuit breakers, retries, timeouts
  • Advanced health checks (TCP, HTTP, gRPC)
  • Excellent observability

For complete Envoy patterns, see references/envoy-patterns.md.

Traefik

Best for: Docker/Kubernetes environments, dynamic configuration, ease of use

Capabilities:

  • Automatic service discovery
  • Native Kubernetes integration
  • Built-in Let's Encrypt support
  • Middleware system (auth, rate limiting)

For complete Traefik patterns, see references/traefik-patterns.md.

Kubernetes Ingress Controllers

Selection Guide
ControllerBest ForStrengths
NGINX Ingress (F5)General purposeStability, wide adoption, mature features
TraefikDynamic environmentsEasy configuration, service discovery
HAProxy IngressHigh performanceAdvanced L7 routing, reliability
Envoy (Contour/Gateway)Service meshRich L7 features, extensibility
KongAPI-heavy appsJWT auth, rate limiting, plugins
Cloud ProviderSingle-cloudNative cloud integration
Basic Ingress Example
yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: app-ingress
  annotations:
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    nginx.ingress.kubernetes.io/affinity: "cookie"
spec:
  ingressClassName: nginx
  tls:
  - hosts:
    - app.example.com
    secretName: app-tls
  rules:
  - host: app.example.com
    http:
      paths:
      - path: /api
        pathType: Prefix
        backend:
          service:
            name: api-service
            port:
              number: 80
      - path: /
        pathType: Prefix
        backend:
          service:
            name: web-service
            port:
              number: 80

For complete Kubernetes ingress examples and Gateway API patterns, see references/kubernetes-ingress.md.

Session Persistence

Sticky Sessions (Use Sparingly)

Cookie-Based: Load balancer sets cookie to track server affinity

  • Accurate routing, works with NAT/proxies
  • HTTP only, adds cookie overhead

IP Hash: Hash client IP to select backend server

  • No cookie required, works for non-HTTP
  • Poor distribution with NAT/proxies

Drawbacks: Uneven load distribution, session lost on server failure, complicates scaling

Architecture: Stateless application servers + centralized session storage (Redis, Memcached)

Benefits:

  • No sticky sessions needed
  • True load balancing
  • Server failures don't lose sessions
  • Horizontal scaling trivial
Client-Side Tokens (Best for APIs)

JWT (JSON Web Tokens): Server generates signed token, client stores and sends with requests

Benefits:

  • Fully stateless servers
  • Perfect load balancing
  • No session storage needed

For complete session management patterns and code examples, see references/session-persistence.md.

Global Load Balancing

GeoDNS Routing

Route users to nearest server based on geographic location:

  • DNS returns different IPs based on client location
  • Reduces latency, supports compliance and regional content
  • Implementation: AWS Route 53, GCP Cloud DNS, Azure Traffic Manager
Multi-Region Failover

Primary/secondary region configuration:

  • Health checks determine primary region health
  • Automatic DNS failover to secondary
  • Transparent to clients
CDN Integration

Combine load balancing with CDN:

  • GeoDNS routes to closest CDN PoP
  • CDN caches content globally
  • Origin load balancing for cache misses

For complete global load balancing examples with Terraform, see references/global-load-balancing.md.

Decision Frameworks

Show full SKILL.md (599 more words)Show less
L4 vs L7 Selection

Choose L4 when:

  • Protocol is TCP/UDP (not HTTP)
  • Ultra-low latency critical (<1ms)
  • High throughput required (millions RPS)
  • Client source IP preservation needed

Choose L7 when:

  • Protocol is HTTP/HTTPS
  • Content-based routing needed (URL, headers)
  • SSL termination required
  • WAF integration needed
  • Microservices architecture
Cloud vs Self-Managed

Choose Cloud-Managed when:

  • Single cloud deployment
  • Auto-scaling required
  • Team lacks load balancer expertise
  • Managed service preferred

Choose Self-Managed when:

  • Multi-cloud or hybrid deployment
  • Advanced routing requirements
  • Cost optimization important
  • Full control needed
  • Vendor lock-in avoidance
Self-Managed Selection
  • NGINX: General-purpose, web stacks, HTTP/3 support
  • HAProxy: Maximum performance, database LB, lowest resource usage
  • Envoy: Microservices, service mesh, dynamic configuration
  • Traefik: Docker/Kubernetes, automatic discovery, easy configuration

Configuration Examples

Complete working examples available in examples/ directory:

Cloud Providers:

  • examples/aws/alb-terraform.tf - AWS ALB with path-based routing
  • examples/aws/nlb-terraform.tf - AWS NLB for TCP load balancing

Self-Managed:

  • examples/nginx/http-load-balancing.conf - NGINX HTTP reverse proxy
  • examples/haproxy/http-lb.cfg - HAProxy configuration
  • examples/envoy/basic-lb.yaml - Envoy cluster configuration
  • examples/traefik/kubernetes-ingress.yaml - Traefik IngressRoute

Kubernetes:

  • examples/kubernetes/nginx-ingress.yaml - NGINX Ingress with TLS
  • examples/kubernetes/traefik-ingress.yaml - Traefik IngressRoute
  • examples/kubernetes/gateway-api.yaml - Gateway API configuration

Monitoring and Observability

Key Metrics

Throughput: Requests per second, bytes transferred, connection rate Latency: Request duration (p50, p95, p99), backend response time, SSL handshake time Errors: HTTP error rates (4xx, 5xx), backend connection failures, health check failures Resource Utilization: CPU, memory, active connections, connection queue depth Health: Healthy/unhealthy backend count, health check success rate

Load Balancer Logs

Enable access logs for request/response details, client IPs, response times, error tracking

  • AWS ALB: Store in S3, analyze with Athena
  • NGINX: Custom log format, ship to centralized logging
  • HAProxy: Syslog integration, structured logging

Troubleshooting

Uneven Load Distribution

Symptoms: One server receives disproportionate traffic Causes: Sticky sessions with few clients, IP hash with NAT concentration, long-lived connections Solutions: Switch to least connections, disable sticky sessions, implement connection draining

Health Check Flapping

Symptoms: Servers rapidly transition between healthy/unhealthy Causes: Health check timeout too short, threshold too low, network instability Solutions: Increase interval and timeout, implement hysteresis, use deep health checks

Session Loss After Failover

Symptoms: Users logged out when server fails Causes: Sticky sessions without replication, in-memory sessions Solutions: Implement shared session store (Redis), use client-side tokens (JWT)

Integration Points

Related Skills:

  • infrastructure-as-code - Deploy load balancers via Terraform/Pulumi
  • kubernetes-operations - Ingress controllers for K8s traffic management
  • network-architecture - Network design and topology for load balancing
  • deploying-applications - Blue-green and canary deployments via load balancers
  • observability - Load balancer metrics, access logs, distributed tracing
  • security-hardening - WAF integration, rate limiting, DDoS protection
  • service-mesh - Envoy as both ingress and service mesh proxy
  • implementing-tls - TLS termination and certificate management

Quick Reference

Selection Matrix
Use CaseRecommended Solution
HTTP web app (AWS)ALB
Non-HTTP protocol (AWS)NLB
Kubernetes HTTP ingressNGINX Ingress or Traefik
Maximum performanceHAProxy
Service meshEnvoy
Docker SwarmTraefik
Multi-cloud portableNGINX or HAProxy
Global distributionCloudFlare, AWS Global Accelerator
Algorithm Selection
Traffic PatternAlgorithm
Stateless, similar serversRound Robin
Stateless, different capacityWeighted Round Robin
Long-lived connectionsLeast Connections
Performance-sensitiveLeast Response Time
Session persistence neededIP Hash or Cookie
Varying server loadResource-Based
Health Check Configuration
Service TypeCheck TypeIntervalTimeout
Web appHTTP /health10s3s
APIHTTP /health/ready10s5s
DatabaseTCP connect5s2s
Critical serviceHTTP deep check5s3s
Background workerHTTP /live30s5s

Summary

Load balancing is essential for distributing traffic, ensuring high availability, and enabling horizontal scaling. Choose L4 for raw performance and non-HTTP protocols, L7 for intelligent content-based routing. Prefer cloud-managed load balancers for simplicity and auto-scaling, self-managed for multi-cloud portability and advanced features. Implement proper health checks with hysteresis, avoid sticky sessions when possible, and monitor key metrics continuously.

For deployment patterns, see examples in examples/aws/, examples/nginx/, examples/kubernetes/, and other provider directories.

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

Files

SKILL.md and 20 other files (references) in skills/load-balancing-patterns of ancoleman/ai-design-components.

  • SKILL.md
  • examples/aws/alb-terraform.tf
  • examples/aws/nlb-terraform.tf
  • examples/envoy/basic-lb.yaml
  • examples/haproxy/http-lb.cfg
  • examples/kubernetes/gateway-api.yaml
  • examples/kubernetes/nginx-ingress.yaml
  • examples/kubernetes/traefik-ingress.yaml
  • examples/nginx/http-load-balancing.conf
  • examples/traefik/kubernetes-ingress.yaml
  • outputs.yaml
  • references/cloud-load-balancers.md
  • references/envoy-patterns.md
  • … and 8 more

Open the folder on GitHubat commit 76551b7

Compare with similar skills

Load Balancing Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Load Balancing Patterns compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Load Balancing Patterns this skillancoleman/ai-design-components526—~3.8kAutomated safety check: PassMIT
Kubeshark KFL2 Filter Referencekubeshark/kubeshark12k—~3.6kAutomated safety check: PassApache-2.0
Nginx To Higress Migrationhigress-group/higress9.5k—~3.9kAutomated safety check: PassApache-2.0
NGINX Ingress Controller Feature Checklistsnginx/kubernetes-ingress5.1k—~1.4kAutomated safety check: PassApache-2.0
NGINX Ingress Policy CRD Guidenginx/kubernetes-ingress5.1k—~2kAutomated safety check: PassApache-2.0
Aks Deployment Skilltimothywarner/chatgptclass143—~916Automated safety check: PassCustom licence

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Works with

Categories

Questions about Load Balancing Patterns

What does Load Balancing Patterns do?

When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes…. Load Balancing Patterns is an agent skill from ancoleman/ai-design-components. When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes ingress) with proper health checks and session management.

When should I use Load Balancing Patterns?

Load Balancing Patterns fits situations like: select and configure the appropriate load balancing solution (L4/L7; Kubernetes ingress) with proper health checks and session management.

How do I install Load Balancing Patterns in Claude Code?

Run `npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a claude-code`. Or copy the skill folder (skills/load-balancing-patterns in ancoleman/ai-design-components) into .claude/skills/load-balancing-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Load Balancing Patterns in Codex?

Run `npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a codex`. Or copy the skill folder (skills/load-balancing-patterns in ancoleman/ai-design-components) into .agents/skills/load-balancing-patterns in your project. Codex loads it when a task matches its description.

Can I use Load Balancing 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 ancoleman/ai-design-components --skill load-balancing-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/load-balancing-patterns, .gemini/skills/load-balancing-patterns, .github/skills/load-balancing-patterns and .opencode/skills/load-balancing-patterns in your project.

What does Load Balancing Patterns need to run?

SKILL.md names no scripts, command-line tools or credentials: Load Balancing Patterns is instructions for the agent only. Our summary lists: Docker.

Does Load Balancing Patterns access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Load Balancing 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 Load Balancing Patterns use?

Load Balancing 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 Load Balancing Patterns use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.

What are the alternatives to Load Balancing Patterns?

Skills that share tags, products or a category with Load Balancing Patterns: Kubeshark KFL2 Filter Reference (kubeshark/kubeshark, 12k stars), Nginx To Higress Migration (higress-group/higress, 9.5k stars), NGINX Ingress Controller Feature Checklists (nginx/kubernetes-ingress, 5.1k stars) and NGINX Ingress Policy CRD Guide (nginx/kubernetes-ingress, 5.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Load Balancing Patterns?

ancoleman (a GitHub user) maintains it in ancoleman/ai-design-components, which has 526 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on December 11, 2025.

Source: ancoleman/ai-design-components on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.