Anton Coleman

Skillchain v3.0 represents a major milestone with complete coverage across all development domains. This release brings 76 production-ready skills, 12 category orchestrators, and a cleaner separated architecture.

Major Achievement: All 29 production skills now have complete progressive disclosure with zero broken references. We created 110 new files across 17 skills in a comprehensive 7-hour sprint to achieve 100% completion.

<!-- truncate -->

The Journey​

Starting from 41% completion with 116 missing files, we systematically completed progressive disclosure across all skills in 4 phases:

• Phase 0: Low Priority (7 skills, 20 files)
• Phase 1: Medium Priority (4 skills, 22 files)
• Phase 2: High Priority (4 skills, 30 files)
• Phase 3: Critical Priority (2 skills, 38 files)

What We Created​

110 Files Total​

• 54 reference guides - Comprehensive documentation
• 42 code examples - Working implementations across Python, TypeScript, Rust, Go
• 12 automation scripts - Token-free execution utilities
• 2 configuration assets - Templates and schemas

Complete Coverage Now Includes​

Database Skills

• All database types: relational, document, vector, timeseries, graph
• Query optimization, indexing strategies, schema design
• Language support: Python (SQLAlchemy, Prisma), TypeScript (Drizzle, Prisma), Rust (sqlx, Diesel), Go (sqlc, GORM)

API Patterns

• REST, GraphQL, gRPC, tRPC implementations
• 5 languages: Python (FastAPI, Strawberry), TypeScript (Express, tRPC), Rust (Axum), Go (Gin), with complete examples

AI/ML Pipeline

• RAG implementations with chunking strategies
• Embedding optimization and vector search
• Data versioning and orchestration patterns
• Complete Qdrant setup and evaluation scripts

Chat Interfaces

• Streaming responses with SSE
• Multi-modal input handling (text, images, files)
• Tool-calling patterns
• Accessibility (WCAG 2.1 AA compliance)

Dashboard Components

• Executive dashboards with KPIs
• Monitoring dashboards with real-time data
• Customizable layouts and themes
• Grid systems and responsive patterns

Data Tables

• Basic, interactive, and advanced grid patterns
• Virtual scrolling for large datasets
• Inline editing and state persistence
• Server-side sorting and filtering
• TanStack Table and AG Grid examples

Media Handling

• Image upload, cropping, and optimization
• Video players with HLS/DASH streaming
• Audio players with waveform visualization
• PDF viewers (iframe, PDF.js, React-PDF)
• Office document viewers
• Cloud storage integration (S3, GCS, Cloudinary)

Message Queues

• BullMQ (Redis-based) and Celery (Python) guides
• Complete workflow examples with Temporal
• Retry patterns and error handling

Observability

• LGTM stack (Loki, Grafana, Tempo, Mimir)
• OpenTelemetry integration
• Automated setup scripts

Full-Stack Templates

• Python (FastAPI, Flask, Django)
• TypeScript (Next.js, Express)
• Rust (Axum, Actix)
• Go (Gin, Echo)

Impact​

This milestone means:

✅ All 29 skills are production-ready ✅ Zero broken references - every link works ✅ Progressive disclosure intact - efficient token usage ✅ Multi-language support - Python, TypeScript, Rust, Go ✅ Complete documentation - references for every pattern ✅ Working examples - copy-paste ready code ✅ Token-free scripts - automation without context cost

The Difference​

Before: Skills had incomplete progressive disclosure, broken references, missing examples

After: Every skill has:

1. SKILL.md (<500 lines) - Main skill file
2. references/ - Detailed documentation files
3. examples/ - Production-ready code
4. scripts/ - Utility automation

What's Next​

With all 29 frontend/backend skills complete, we're expanding to:

• DevOps Skills - CI/CD, infrastructure, deployment
• Security Skills - Auth, secrets, compliance
• Cloud Skills - AWS, GCP, Azure patterns
• AI/ML Skills - MLOps, embeddings, evaluation

See v0.4.1 release for details on 47 new skill master plans.

By the Numbers​

• Completion: 41% → 100%
• Missing files: 116 → 0
• Files created: 110
• Time invested: ~7 hours
• Skills completed: 17 (from 12 to 29)
• Languages: Python, TypeScript, Rust, Go

Learn More​

• Full Changelog
• Skill Completion Plan
• GitHub Repository

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.

<!-- truncate -->

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​

• Full Changelog
• Skill Orchestration Plan
• GitHub Repository

Skillchain v2.1 introduces a completely modular architecture with intelligent features that make building full-stack applications faster and more intuitive.

<!-- truncate -->

What is Skillchain?​

Skillchain is an intelligent orchestrator that helps you build full-stack applications by automatically loading the right Claude Skills for your project. Instead of manually selecting skills, just describe what you're building and Skillchain assembles the perfect skill chain.

New Features in v2.1​

1. User Preferences System​

Skillchain now remembers your choices and provides smart defaults:

# Saved to ~/.claude/skillchain-prefs.yaml
frontend_framework: next.js
styling_preference: tailwind
backend_language: typescript
database_preference: postgresql

Benefits:

• No repetitive questions for similar projects
• Faster project setup
• Consistent technology choices across projects

2. Skill Versioning​

All 29 skills now have proper versioning (v1.0.0) with:

• Changelog tracking - See what's changed between versions
• Compatibility info - Know what works together
• Deprecation notices - Plan for future changes

3. Parallel Skill Loading​

Independent skills now load concurrently using parallel_group fields:

skills:
  - name: databases-postgresql
    parallel_group: data
  - name: using-vector-databases
    parallel_group: data
  # Both load simultaneously!

Benefits:

• Faster skill chain initialization
• Reduced waiting time
• Smarter dependency resolution

4. Blueprint System​

Pre-configured skill chains for common patterns:

Dashboard Blueprint:

/skillchain dashboard

Automatically loads:

• creating-dashboards
• visualizing-data
• building-tables
• theming-components

CRUD API Blueprint:

/skillchain crud-api

Automatically loads:

• api-patterns
• databases-relational
• auth-security
• observability

RAG Pipeline Blueprint:

/skillchain rag-pipeline

Automatically loads:

• databases-vector
• ai-data-engineering
• model-serving
• api-patterns

5. Category Orchestrators​

Specialized handlers for different domains:

• Frontend Orchestrator - React, Vue, Svelte projects
• Backend Orchestrator - API, database, auth patterns
• Fullstack Orchestrator - Combines frontend + backend
• AI/ML Orchestrator - RAG, embeddings, model serving

6. Dynamic Path Discovery​

Works globally from any project directory:

# Works from anywhere
cd ~/my-project
/skillchain dashboard

# Automatically finds skillchain installation
# No manual path configuration needed

How It Works​

8-Step Workflow​

1. Step 0: Path Discovery - Finds skillchain directory
2. Step 0.5: Load Preferences - Reads saved choices
3. Step 1: Goal Input - What are you building?
4. Step 2: Keyword Detection - Identifies relevant skills
5. Step 3: Configuration - Asks targeted questions
6. Step 4: Skill Assembly - Loads skills in parallel
7. Step 5: Execution - Runs the workflow
8. Step 7: Save Preferences - Remembers your choices

Example Usage​

Simple Dashboard​

/skillchain dashboard analytics

Skillchain:

• Detects keywords: "dashboard", "analytics"
• Loads: creating-dashboards + visualizing-data
• Asks: Chart types? Data source?
• Assembles components with your branding

Full-Stack RAG App​

/skillchain I need a RAG chatbot with user auth

Skillchain:

• Detects: "RAG", "chatbot", "auth"
• Loads: building-ai-chat + databases-vector + auth-security + api-patterns
• Asks: Vector DB? Auth provider? API framework?
• Builds complete authenticated RAG pipeline

Quick CRUD API​

/skillchain rest api with postgresql

Skillchain:

• Detects: "api", "postgresql"
• Loads: api-patterns + databases-relational
• Uses preferences: TypeScript (from last time)
• Generates API with proper patterns

Architecture Improvements​

Modular File Structure (19 files)​

.claude/commands/skillchain/
├── skillchain.md          # Main router
├── _registry.yaml         # 29 skills with metadata
├── _help.md              # Documentation
├── _shared/              # 9 shared resources
│   ├── theming-rules.md
│   ├── execution-flow.md
│   ├── preferences.md
│   ├── parallel-loading.md
│   └── ...
├── categories/           # 4 orchestrators
│   ├── frontend.md
│   ├── backend.md
│   ├── fullstack.md
│   └── ai-ml.md
└── blueprints/          # 3 templates
    ├── dashboard.md
    ├── crud-api.md
    └── rag-pipeline.md

Benefits of Modularity​

• Easier maintenance - Update one file, not the whole system
• Better organization - Clear separation of concerns
• Extensibility - Add new categories/blueprints easily
• Token efficiency - Load only what's needed

Performance​

• Skill loading time: ~2-3 seconds (parallel loading)
• Preference loading: <100ms
• Path discovery: <50ms
• Total startup: ~3-4 seconds (vs 8-10 seconds in v2.0)

Installation​

# From repository root - interactive installer
./install.sh

# Install skillchain commands only
./install.sh commands

Upgrade Notes​

If you're upgrading from v2.0:

1. Re-run installer to get modular structure
2. Your preferences will be preserved
3. All existing projects continue to work
4. New features available immediately

What's Next​

Future enhancements planned:

• Template library - Community-contributed blueprints
• Skill recommendations - AI-suggested skill chains
• Project scaffolding - Generate starter code
• Skill marketplace - Browse and install community skills

Learn More​

• Skillchain Documentation
• Creating Custom Blueprints
• Full Changelog
• GitHub Repository