Kubeshark KFL2 Filter Reference
kubeshark/kubeshark
Syntax reference for KFL2, the CEL-based display filter language used to search Kubernetes network traffic captured by Kubeshark, loaded before any filter is written.
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…
$ npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ancoleman/ai-design-components load-balancing-patterns --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "load-balancing-patterns" agent skill from https://github.com/ancoleman/ai-design-components/tree/main/skills/load-balancing-patterns into .claude/skills/load-balancing-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "load-balancing-patterns", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ancoleman/ai-design-components/tree/main/skills/load-balancing-patternsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ancoleman/ai-design-components load-balancing-patterns --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/load-balancing-patterns .agents/skills/load-balancing-patterns && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "load-balancing-patterns" agent skill from https://github.com/ancoleman/ai-design-components/tree/main/skills/load-balancing-patterns into .agents/skills/load-balancing-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "load-balancing-patterns", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ancoleman/ai-design-components load-balancing-patterns --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/load-balancing-patterns .cursor/skills/load-balancing-patterns && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "load-balancing-patterns" agent skill from https://github.com/ancoleman/ai-design-components/tree/main/skills/load-balancing-patterns into .cursor/skills/load-balancing-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "load-balancing-patterns", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ancoleman/ai-design-components.git --path skills/load-balancing-patterns--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ancoleman/ai-design-components load-balancing-patterns --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/load-balancing-patterns .gemini/skills/load-balancing-patterns && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "load-balancing-patterns" agent skill from https://github.com/ancoleman/ai-design-components/tree/main/skills/load-balancing-patterns into .gemini/skills/load-balancing-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "load-balancing-patterns", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ancoleman/ai-design-components load-balancing-patternsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/load-balancing-patterns .github/skills/load-balancing-patterns && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "load-balancing-patterns" agent skill from https://github.com/ancoleman/ai-design-components/tree/main/skills/load-balancing-patterns into .github/skills/load-balancing-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "load-balancing-patterns", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ancoleman/ai-design-components --skill load-balancing-patterns -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ancoleman/ai-design-components load-balancing-patterns --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/load-balancing-patterns .opencode/skills/load-balancing-patterns && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "load-balancing-patterns" agent skill from https://github.com/ancoleman/ai-design-components/tree/main/skills/load-balancing-patterns into .opencode/skills/load-balancing-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "load-balancing-patterns", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
load-balancing-patternsWhen 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.
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.
Read from SKILL.md and the folder at commit 76551b7. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 1,498 words, ~3,815 tokens.
.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.Distribute traffic across infrastructure using the appropriate load balancing approach, from simple round-robin to global multi-region failover.
Use load-balancing-patterns when:
Layer 4 (L4) - Transport Layer:
Layer 7 (L7) - Application Layer:
For detailed comparison including performance benchmarks and hybrid approaches, see references/l4-vs-l7-comparison.md.
| Algorithm | Distribution Method | Use Case |
|---|---|---|
| Round Robin | Sequential | Stateless, similar servers |
| Weighted Round Robin | Capacity-based | Different server specs |
| Least Connections | Fewest active connections | Long-lived connections |
| Least Response Time | Fastest server | Performance-sensitive |
| IP Hash | Client IP-based | Session persistence |
| Resource-Based | CPU/memory metrics | Varying workloads |
Shallow (Liveness): Is the process alive?
/health/live or /liveDeep (Readiness): Can the service handle requests?
/health/ready or /readyHealth Check Hysteresis: Different thresholds for marking up vs down to prevent flapping
For complete health check implementation patterns, see references/health-check-strategies.md.
Application Load Balancer (ALB) - Layer 7:
Network Load Balancer (NLB) - Layer 4:
Global Accelerator - Layer 4 Global:
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
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.
Best for: General-purpose HTTP/HTTPS load balancing, web application stacks
Capabilities:
Basic configuration:
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.
Best for: Maximum performance, database load balancing, resource efficiency
Capabilities:
Basic configuration:
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 checkFor complete HAProxy patterns, see references/haproxy-patterns.md.
Best for: Microservices, Kubernetes, service mesh integration
Capabilities:
For complete Envoy patterns, see references/envoy-patterns.md.
Best for: Docker/Kubernetes environments, dynamic configuration, ease of use
Capabilities:
For complete Traefik patterns, see references/traefik-patterns.md.
| Controller | Best For | Strengths |
|---|---|---|
| NGINX Ingress (F5) | General purpose | Stability, wide adoption, mature features |
| Traefik | Dynamic environments | Easy configuration, service discovery |
| HAProxy Ingress | High performance | Advanced L7 routing, reliability |
| Envoy (Contour/Gateway) | Service mesh | Rich L7 features, extensibility |
| Kong | API-heavy apps | JWT auth, rate limiting, plugins |
| Cloud Provider | Single-cloud | Native cloud integration |
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: 80For complete Kubernetes ingress examples and Gateway API patterns, see references/kubernetes-ingress.md.
Cookie-Based: Load balancer sets cookie to track server affinity
IP Hash: Hash client IP to select backend server
Drawbacks: Uneven load distribution, session lost on server failure, complicates scaling
Architecture: Stateless application servers + centralized session storage (Redis, Memcached)
Benefits:
JWT (JSON Web Tokens): Server generates signed token, client stores and sends with requests
Benefits:
For complete session management patterns and code examples, see references/session-persistence.md.
Route users to nearest server based on geographic location:
Primary/secondary region configuration:
Combine load balancing with CDN:
For complete global load balancing examples with Terraform, see references/global-load-balancing.md.
Choose L4 when:
Choose L7 when:
Choose Cloud-Managed when:
Choose Self-Managed when:
Complete working examples available in examples/ directory:
Cloud Providers:
examples/aws/alb-terraform.tf - AWS ALB with path-based routingexamples/aws/nlb-terraform.tf - AWS NLB for TCP load balancingSelf-Managed:
examples/nginx/http-load-balancing.conf - NGINX HTTP reverse proxyexamples/haproxy/http-lb.cfg - HAProxy configurationexamples/envoy/basic-lb.yaml - Envoy cluster configurationexamples/traefik/kubernetes-ingress.yaml - Traefik IngressRouteKubernetes:
examples/kubernetes/nginx-ingress.yaml - NGINX Ingress with TLSexamples/kubernetes/traefik-ingress.yaml - Traefik IngressRouteexamples/kubernetes/gateway-api.yaml - Gateway API configurationThroughput: 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
Enable access logs for request/response details, client IPs, response times, error tracking
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
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
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)
Related Skills:
infrastructure-as-code - Deploy load balancers via Terraform/Pulumikubernetes-operations - Ingress controllers for K8s traffic managementnetwork-architecture - Network design and topology for load balancingdeploying-applications - Blue-green and canary deployments via load balancersobservability - Load balancer metrics, access logs, distributed tracingsecurity-hardening - WAF integration, rate limiting, DDoS protectionservice-mesh - Envoy as both ingress and service mesh proxyimplementing-tls - TLS termination and certificate management| Use Case | Recommended Solution |
|---|---|
| HTTP web app (AWS) | ALB |
| Non-HTTP protocol (AWS) | NLB |
| Kubernetes HTTP ingress | NGINX Ingress or Traefik |
| Maximum performance | HAProxy |
| Service mesh | Envoy |
| Docker Swarm | Traefik |
| Multi-cloud portable | NGINX or HAProxy |
| Global distribution | CloudFlare, AWS Global Accelerator |
| Traffic Pattern | Algorithm |
|---|---|
| Stateless, similar servers | Round Robin |
| Stateless, different capacity | Weighted Round Robin |
| Long-lived connections | Least Connections |
| Performance-sensitive | Least Response Time |
| Session persistence needed | IP Hash or Cookie |
| Varying server load | Resource-Based |
| Service Type | Check Type | Interval | Timeout |
|---|---|---|---|
| Web app | HTTP /health | 10s | 3s |
| API | HTTP /health/ready | 10s | 5s |
| Database | TCP connect | 5s | 2s |
| Critical service | HTTP deep check | 5s | 3s |
| Background worker | HTTP /live | 30s | 5s |
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
SKILL.md and 20 other files (references) in skills/load-balancing-patterns of ancoleman/ai-design-components.
Open the folder on GitHubat commit 76551b7
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Load Balancing Patterns this skillancoleman/ai-design-components | 526 | — | ~3.8k | Automated safety check: Pass | MIT | |
| Kubeshark KFL2 Filter Referencekubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| Nginx To Higress Migrationhigress-group/higress | 9.5k | — | ~3.9k | Automated safety check: Pass | Apache-2.0 | |
| NGINX Ingress Controller Feature Checklistsnginx/kubernetes-ingress | 5.1k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| NGINX Ingress Policy CRD Guidenginx/kubernetes-ingress | 5.1k | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Aks Deployment Skilltimothywarner/chatgptclass | 143 | — | ~916 | Automated safety check: Pass | Custom licence |
kubeshark/kubeshark
Syntax reference for KFL2, the CEL-based display filter language used to search Kubernetes network traffic captured by Kubeshark, loaded before any filter is written.
higress-group/higress
Migrate from ingress-nginx to Higress in Kubernetes environments.
nginx/kubernetes-ingress
Gives step-by-step checklists for adding Ingress annotations, VirtualServer fields and Helm values to the NGINX Kubernetes Ingress Controller, with common gotchas.
nginx/kubernetes-ingress
Step-by-step checklist for adding a new Policy CRD type to the NGINX Ingress Controller, from the Go types and validation to config generation and templates.
timothywarner/chatgptclass
Deploy and operate workloads on Azure Kubernetes Service (AKS) the safe way.
kubesphere/kubesphere
Installs, uninstalls, checks and troubleshoots the KubeSphere Gateway extension built on ingress-nginx, including gateways stuck in bad states and Helm or pod failures.
ancoleman/ai-design-components
Builds AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support.
ancoleman/ai-design-components
Builds form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages.
ancoleman/ai-design-components
Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids.
ancoleman/ai-design-components
Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts.
ancoleman/ai-design-components
Designs layout systems and responsive interfaces including grid systems, flexbox patterns, sidebar layouts, and responsive breakpoints.
ancoleman/ai-design-components
Displays chronological events and activity through timelines, activity feeds, Gantt charts, and calendar interfaces.
Works with
Categories
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.
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.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Load Balancing Patterns is instructions for the agent only. Our summary lists: Docker.
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.
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.
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.
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.
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.
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.