Add 25 world-class Claude Code skills for comprehensive software development

Created comprehensive skill collection covering all aspects of modern software
development with production-ready patterns, best practices, and detailed documentation.

## Skills Organized by Domain

### Code Quality & Architecture (2 skills)
- advanced-code-refactoring: SOLID principles, design patterns, refactoring patterns
- code-review: Automated/manual review, security, performance, maintainability

### API & Integration (2 skills)
- api-integration-expert: REST/GraphQL/WebSocket with auth, retry, caching
- graphql-schema-design: Schema design, resolvers, optimization, subscriptions

### Database & Data (3 skills)
- database-optimization: SQL/NoSQL tuning, indexing, query optimization
- data-pipeline: ETL/ELT with Airflow, Spark, dbt
- caching-strategies: Redis, Memcached, CDN, invalidation patterns

### Security & Authentication (2 skills)
- security-audit: OWASP Top 10, vulnerability scanning, security hardening
- auth-implementation: OAuth2, JWT, session management, SSO

### Testing & Quality (2 skills)
- test-automation: Unit/integration/E2E tests, TDD/BDD, coverage
- performance-profiling: CPU/memory profiling, Core Web Vitals optimization

### DevOps & Infrastructure (3 skills)
- docker-kubernetes: Containerization, orchestration, production deployments
- ci-cd-pipeline: GitHub Actions, automated testing, deployment strategies
- logging-monitoring: Observability with Datadog, Prometheus, Grafana, ELK

### Frontend Development (3 skills)
- frontend-accessibility: WCAG 2.1 compliance, ARIA, keyboard navigation
- ui-component-library: Design systems with React/Vue, Storybook
- mobile-responsive: Responsive design, mobile-first, PWAs

### Backend & Scaling (2 skills)
- backend-scaling: Load balancing, sharding, microservices, horizontal scaling
- real-time-systems: WebSockets, SSE, WebRTC for real-time features

### ML & AI (1 skill)
- ml-model-integration: Model serving, inference optimization, monitoring

### Development Tools (2 skills)
- git-workflow-optimizer: Git workflows, branching strategies, conflict resolution
- dependency-management: Package updates, security patches, version conflicts

### Code Maintenance (3 skills)
- error-handling: Robust error patterns, logging, graceful degradation
- documentation-generator: API docs, README, technical specifications
- migration-tools: Database/framework migrations with zero downtime

## Key Features

Each skill includes:
- YAML frontmatter with name, description, allowed tools
- Clear purpose and when to use
- Comprehensive capabilities overview
- Production-ready code examples
- Best practices and patterns
- Success criteria
- Tool-specific configurations

## Highlights

- 25 comprehensive skills covering full development lifecycle
- Production-ready patterns and examples
- Security-first approach throughout
- Performance optimization built-in
- Comprehensive testing strategies
- DevOps automation and infrastructure as code
- Modern frontend with accessibility focus
- Scalable backend architectures
- Data engineering and ML integration
- Advanced Git workflows

## File Structure

claude_skills/
├── README.md (comprehensive documentation)
├── advanced-code-refactoring/
│   ├── SKILL.md (main skill definition)
│   ├── reference.md (design patterns, SOLID principles)
│   └── examples.md (refactoring examples)
├── api-integration-expert/
│   └── SKILL.md (REST/GraphQL/WebSocket integration)
├── [23 more skills...]

Total: 25 skills + comprehensive README + supporting documentation

## Usage

Personal skills: cp -r claude_skills/* ~/.claude/skills/
Project skills: cp -r claude_skills/* .claude/skills/

Skills automatically activate based on context and description triggers.

claude_skills/data-pipeline/SKILL.md

@@ -0,0 +1,116 @@
+---
+name: data-pipeline
+description: Expert in building ETL/ELT pipelines, data processing, transformation, and orchestration using tools like Airflow, Spark, and dbt. Use for data engineering tasks, building data workflows, or implementing data processing systems.
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+---
+
+# Data Pipeline Expert
+
+## Purpose
+Build robust ETL/ELT pipelines for data processing, transformation, and orchestration.
+
+## Tools & Technologies
+- **Orchestration**: Apache Airflow, Prefect, Dagster
+- **Processing**: Apache Spark, dbt, Pandas
+- **Storage**: S3, GCS, Data Lakes
+- **Warehouses**: Snowflake, BigQuery, Redshift
+- **Streaming**: Apache Kafka, AWS Kinesis
+- **Quality**: Great Expectations, dbt tests
+
+## Airflow DAG Example
+```python
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from airflow.providers.postgres.operators.postgres import PostgresOperator
+from datetime import datetime, timedelta
+
+default_args = {
+    'owner': 'data-team',
+    'retries': 3,
+    'retry_delay': timedelta(minutes=5),
+    'email_on_failure': True,
+}
+
+with DAG(
+    'user_analytics_pipeline',
+    default_args=default_args,
+    schedule_interval='@daily',
+    start_date=datetime(2024, 1, 1),
+    catchup=False,
+    tags=['analytics', 'users'],
+) as dag:
+
+    extract_users = PythonOperator(
+        task_id='extract_users',
+        python_callable=extract_from_api,
+        op_kwargs={'endpoint': 'users'}
+    )
+
+    transform_data = PythonOperator(
+        task_id='transform_data',
+        python_callable=transform_user_data,
+    )
+
+    load_to_warehouse = PostgresOperator(
+        task_id='load_to_warehouse',
+        postgres_conn_id='warehouse',
+        sql='sql/load_users.sql',
+    )
+
+    data_quality_check = PythonOperator(
+        task_id='data_quality_check',
+        python_callable=run_quality_checks,
+    )
+
+    extract_users >> transform_data >> load_to_warehouse >> data_quality_check
+```
+
+## dbt Transformation
+```sql
+-- models/staging/stg_users.sql
+with source as (
+    select * from {{ source('raw', 'users') }}
+),
+
+transformed as (
+    select
+        id as user_id,
+        lower(email) as email,
+        created_at,
+        updated_at,
+        case
+            when status = 'active' then true
+            else false
+        end as is_active
+    from source
+    where created_at is not null
+)
+
+select * from transformed
+
+-- models/marts/fct_user_activity.sql
+with user_events as (
+    select * from {{ ref('stg_events') }}
+),
+
+aggregated as (
+    select
+        user_id,
+        count(*) as total_events,
+        count(distinct date(created_at)) as active_days,
+        min(created_at) as first_event_at,
+        max(created_at) as last_event_at
+    from user_events
+    group by 1
+)
+
+select * from aggregated
+```
+
+## Success Criteria
+- ✓ Data freshness < 1 hour
+- ✓ Pipeline success rate > 99%
+- ✓ Data quality checks passing
+- ✓ Idempotent operations
+- ✓ Monitoring and alerting
+