mirror of
https://github.com/wshobson/agents.git
synced 2026-03-18 09:37:15 +00:00
3802bca865
Plugin Scope Improvements: - Remove language-specialists plugin (not task-focused) - Split specialized-domains into 5 focused plugins: * blockchain-web3 - Smart contract development only * quantitative-trading - Financial modeling and trading only * payment-processing - Payment gateway integration only * game-development - Unity and Minecraft only * accessibility-compliance - WCAG auditing only - Split business-operations into 3 focused plugins: * business-analytics - Metrics and reporting only * hr-legal-compliance - HR and legal docs only * customer-sales-automation - Support and sales workflows only - Fix infrastructure-devops scope: * Remove database concerns (db-migrate, database-admin) * Remove observability concerns (observability-engineer) * Move slo-implement to incident-response * Focus purely on container orchestration (K8s, Docker, Terraform) - Fix customer-sales-automation scope: * Remove content-marketer (unrelated to customer/sales workflows) Marketplace Statistics: - Total plugins: 27 (was 22) - Tool coverage: 100% (42/42 tools referenced) - Fat plugins removed: 3 (language-specialists, specialized-domains, business-operations) - All plugins now have clear, focused tasks Model Migration: - Migrate all 42 tools from claude-sonnet-4-0/opus-4-1 to model: sonnet - Migrate all 15 workflows from claude-opus-4-1 to model: sonnet - Use short model syntax consistent with agent files Documentation Updates: - Update README.md with refined plugin structure - Update plugin descriptions to be task-focused - Remove anthropomorphic and marketing language - Improve category organization (now 16 distinct categories) Ready for October 9, 2025 @ 9am PST launch
1.5 KiB
1.5 KiB
model
| model |
|---|
| sonnet |
Data Pipeline Architecture
Design and implement a scalable data pipeline for: $ARGUMENTS
Create a production-ready data pipeline including:
-
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
-
Transformation Layer:
- ETL/ELT architecture decision
- Apache Beam/Spark transformations
- Data cleansing and normalization
- Feature engineering pipeline
- Business logic implementation
-
Orchestration:
- Airflow/Prefect DAGs
- Dependency management
- Retry and failure handling
- SLA monitoring
- Dynamic pipeline generation
-
Storage Strategy:
- Data lake architecture
- Partitioning strategy
- Compression choices
- Retention policies
- Hot/cold storage tiers
-
Streaming Pipeline:
- Kafka/Kinesis integration
- Real-time processing
- Windowing strategies
- Late data handling
- Exactly-once semantics
-
Data Quality:
- Automated testing
- Data profiling
- Anomaly detection
- Lineage tracking
- Quality metrics and dashboards
-
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.