Prompt
Specify a webhook-driven task for OpenClaw
Describe event inputs, validation, deduplication, and intended actions before integration work.
OpenClawDevelopment & Engineering
Dave AI
CQRS Implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.
instructions.md
--- name: cqrs-implementation-72580704c7cc description: Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems. --- # CQRS Implementation Comprehensive guide to implementing CQRS (Command Query Responsibility Segregation) patterns. ## Use this skill when - Separating read and write concerns - Scaling reads independently from writes - Building event-sourced systems - Optimizing complex query scenarios - Different read/write data models are needed - High-performance reporting is required ## Do not use this skill when - The domain is simple and CRUD is sufficient - You cannot operate separate read/write models - Strong immediate consistency is required everywhere ## Instructions - Identify read/write workloads and consistency needs. - Define command and query models with clear boundaries. - Implement read model projections and synchronization. - Validate performance, recovery, and failure modes. - If detailed patterns are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed CQRS patterns and templates. --- ## 🧠 AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Hybrid Memory Integration (Qdrant + BM25) Before executing complex tasks with this skill: ```bash python3 execution/memory_manager.py auto --query "<task summary>" ``` **Decision Tree:** - **Cache hit?** Use cached response directly — no need to re-process. - **Memory match?** Inject `context_chunks` into your reasoning. - **No match?** Proceed normally, then store results: ```bash python3 execution/memory_manager.py store \ --content "Description of what was decided/solved" \ --type decision \ --tags cqrs-implementation <relevant-tags> ``` > **Note:** Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices. ### Agent Team Collaboration - **Strategy**: This skill communicates via the shared memory system. - **Orchestration**: Invoked by `orchestrator` via intelligent routing. - **Context Sharing**: Always read previous agent outputs from memory before starting. ### Local LLM Support When available, use local Ollama models for embedding and lightweight inference: - Embeddings: `nomic-embed-text` via Qdrant memory system - Lightweight analysis: Local models reduce API costs for repetitive patterns--- name: cqrs-implementation-72580704c7cc description: Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems. --- # CQRS Implementation Comprehensive guide to implementing CQRS (Command Query Responsibility Segregation) patterns. ## Use this skill when - Separating read and write concerns - Scaling reads independently from writes - Building event-sourced systems - Optimizing complex query scenarios - Different read/write data models are needed - High-performance reporting is required ## Do not use this skill when - The domain is simple and CRUD is sufficient - You cannot operate separate read/write models - Strong immediate consistency is required everywhere ## Instructions - Identify read/write workloads and consistency needs. - Define command and query models with clear boundaries. - Implement read model projections and synchronization. - Validate performance, recovery, and failure modes. - If detailed patterns are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed CQRS patterns and templates. --- ## 🧠 AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Hybrid Memory Integration (Qdrant + BM25) Before executing complex tasks with this skill: ```bash python3 execution/memory_manager.py auto --query "<task summary>" ``` **Decision Tree:** - **Cache hit?** Use cached response directly — no need to re-process. - **Memory match?** Inject `context_chunks` into your reasoning. - **No match?** Proceed normally, then store results: ```bash python3 execution/memory_manager.py store \ --content "Description of what was decided/solved" \ --type decision \ --tags cqrs-implementation <relevant-tags> ``` > **Note:** Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices. ### Agent Team Collaboration - **Strategy**: This skill communicates via the shared memory system. - **Orchestration**: Invoked by `orchestrator` via intelligent routing. - **Context Sharing**: Always read previous agent outputs from memory before starting. ### Local LLM Support When available, use local Ollama models for embedding and lightweight inference: - Embeddings: `nomic-embed-text` via Qdrant memory system - Lightweight analysis: Local models reduce API costs for repetitive patterns
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.