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AI Design Components

Updates, releases, and insights about AI Design Components

v0.4.1: 47 New Skill Master Plans

· 4 min read
Anton Coleman
Anton Coleman
Project Maintainer
Claude
Claude
AI Assistant

Major Achievement: We've created comprehensive init.md master plans for 47 new skills covering DevOps, Infrastructure, Security, Cloud, and AI/ML domains. This represents 90,839 lines of strategic planning documentation, bringing our total skill coverage to 76 skills.

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What's New​

This release expands our capabilities beyond frontend UI components to cover the entire development lifecycle:

  • 47 new skill master plans across 8 major categories
  • Multi-language support in 9 skills (TypeScript, Python, Go, Rust)
  • Research-backed recommendations using Google Search Grounding and Context7
  • Production-ready tool recommendations with trust scores and benchmarks

Categories Added​

Infrastructure & Networking (12 skills)​

Complete infrastructure management coverage:

  • Infrastructure as Code (Terraform, Pulumi, CDK)
  • Kubernetes Operations (Helm, operators, troubleshooting)
  • Distributed Systems Design (CAP theorem, consensus algorithms)
  • Configuration Management (Ansible, Chef, Puppet)
  • Network Architecture (VPC design, subnets, routing)
  • Load Balancing Patterns (ALB, NLB, service mesh)
  • DNS Management (Route53, CloudDNS, record types)
  • Service Mesh (Istio, Linkerd, Cilium)
  • Disaster Recovery (RPO/RTO, backup strategies)
  • Linux Administration (system management, troubleshooting)
  • Shell Scripting (Bash/Zsh patterns, best practices)
  • Configuring Nginx (reverse proxy, SSL, performance)

Security (6 skills)​

Comprehensive security practices:

  • Security Architecture (zero trust, defense in depth)
  • Compliance Frameworks (SOC2, ISO27001, HIPAA)
  • Vulnerability Management (scanning, remediation workflows)
  • SIEM & Logging (security monitoring, alerting)
  • Implementing TLS (certificate management, mTLS)
  • Configuring Firewalls (network security rules)

Developer Productivity (7 skills)​

Tools for building better developer experiences:

  • API Design Principles (REST, GraphQL design patterns)
  • Building CLIs (Python Click, Go Cobra, Rust Clap) 🌐
  • SDK Design (client library patterns across languages) 🌐
  • Documentation Generation (API docs, code docs)
  • Debugging Techniques (profiling, troubleshooting) 🌐
  • Git Workflows (branching strategies, hooks)
  • Writing GitHub Actions (CI/CD workflows)

DevOps & Platform (6 skills)​

Modern DevOps practices:

  • Building CI Pipelines (GitHub Actions, GitLab CI, Jenkins)
  • GitOps Workflows (ArgoCD 91.8/100 trust, Flux patterns)
  • Testing Strategies (unit, integration, E2E testing) 🌐
  • Platform Engineering (IDP, Backstage, developer experience)
  • Incident Management (on-call, post-mortems, SRE)
  • Writing Dockerfiles (multi-stage, security hardening)

Data & Analytics (6 skills)​

Data pipeline and optimization:

  • Data Architecture (data mesh, lakehouse, medallion)
  • Streaming Data (Kafka, Flink, event streaming) 🌐
  • Data Transformation (dbt, ETL/ELT patterns) 🌐
  • SQL Optimization (query tuning, indexing)
  • Secret Management (Vault, secrets rotation)
  • Performance Engineering (profiling, optimization)

AI/ML Operations (4 skills)​

Modern AI/ML workflows:

  • MLOps Patterns (MLflow 95/100 trust, experiment tracking)
  • Prompt Engineering (LLM prompting, chain-of-thought)
  • LLM Evaluation (RAGAS, benchmarks, safety testing)
  • Embedding Optimization (chunking strategies, model selection)

Cloud Patterns (3 skills)​

Cloud provider expertise:

  • AWS Patterns (Well-Architected Framework, service selection)
  • GCP Patterns (BigQuery, Vertex AI, GKE)
  • Azure Patterns (Container Apps, Azure OpenAI)

FinOps (3 skills)​

Cost optimization and governance:

  • Cost Optimization (FinOps practices, rightsizing)
  • Resource Tagging (tag governance, enforcement)
  • Security Hardening (CIS benchmarks, hardening guides)

Multi-Language Skills 🌐​

9 skills include implementations across multiple languages:

  • TypeScript - Modern web backend
  • Python - Data science and ML
  • Go - Cloud-native systems
  • Rust - Performance-critical applications

Skills with multi-language support:

  • testing-strategies, building-clis, designing-sdks, debugging-techniques
  • streaming-data, transforming-data, shell-scripting, optimizing-sql
  • All cloud pattern skills include Terraform, CDK, and native SDK examples

Research Methodology​

Every init.md includes research from:

  • Google Search Grounding - 100+ queries for 2025 best practices
  • Context7 - Library trust scores and documentation quality
    • Example: Argo CD (91.8/100), MLflow (95/100)
  • Decision frameworks with actionable guidance
  • Production-ready tool recommendations

Statistics​

  • Total init.md files: 47
  • Total lines written: 90,839
  • Average lines per skill: 1,933
  • Skills with SKILL.md (production): 29
  • Total skill coverage: 76 skills (29 frontend + 14 backend + 33 new)

Next Steps​

These master plans serve as comprehensive blueprints for creating production-ready SKILL.md files. The next phase will implement progressive disclosure patterns following Anthropic's best practices:

  1. SKILL.md creation - Main skill files (<500 lines)
  2. Reference documentation - Detailed guides in references/
  3. Code examples - Working implementations in examples/
  4. Utility scripts - Token-free automation in scripts/

Learn More​