Consolidate workflows and tools from commands repository

Repository Restructure:
- Move all 83 agent .md files to agents/ subdirectory
- Add 15 workflow orchestrators from commands repo to workflows/
- Add 42 development tools from commands repo to tools/
- Update README for unified repository structure

The commands repository functionality is now fully integrated, providing
complete workflow orchestration and development tooling alongside agents.

Directory Structure:
- agents/    - 83 specialized AI agents
- workflows/ - 15 multi-agent orchestration commands
- tools/     - 42 focused development utilities

No breaking changes to agent functionality - all agents remain accessible
with same names and behavior. Adds workflow and tool commands for enhanced
multi-agent coordination capabilities.

tools/data-pipeline.md

@@ -0,0 +1,60 @@
+---
+model: claude-sonnet-4-0
+---
+
+# Data Pipeline Architecture
+
+Design and implement a scalable data pipeline for: $ARGUMENTS
+
+Create a production-ready data pipeline including:
+
+1. **Data Ingestion**:
+   - Multiple source connectors (APIs, databases, files, streams)
+   - Schema evolution handling
+   - Incremental/batch loading
+   - Data quality checks at ingestion
+   - Dead letter queue for failures
+
+2. **Transformation Layer**:
+   - ETL/ELT architecture decision
+   - Apache Beam/Spark transformations
+   - Data cleansing and normalization
+   - Feature engineering pipeline
+   - Business logic implementation
+
+3. **Orchestration**:
+   - Airflow/Prefect DAGs
+   - Dependency management
+   - Retry and failure handling
+   - SLA monitoring
+   - Dynamic pipeline generation
+
+4. **Storage Strategy**:
+   - Data lake architecture
+   - Partitioning strategy
+   - Compression choices
+   - Retention policies
+   - Hot/cold storage tiers
+
+5. **Streaming Pipeline**:
+   - Kafka/Kinesis integration
+   - Real-time processing
+   - Windowing strategies
+   - Late data handling
+   - Exactly-once semantics
+
+6. **Data Quality**:
+   - Automated testing
+   - Data profiling
+   - Anomaly detection
+   - Lineage tracking
+   - Quality metrics and dashboards
+
+7. **Performance & Scale**:
+   - Horizontal scaling
+   - Resource optimization
+   - Caching strategies
+   - Query optimization
+   - Cost management
+
+Include monitoring, alerting, and data governance considerations. Make it cloud-agnostic with specific implementation examples for AWS/GCP/Azure.