Skill: athola/nm-archetypes-architecture-paradigm-pipeline (version 1.8.5)

--- name: architecture-paradigm-pipeline description: Design pipes-and-filters for sequential data transformations version: 1.9.5 triggers: - architecture - pipeline - pipes-filters - ETL - streaming - data-processing - data flows through processing stages metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/archetypes", "emoji": "\ud83c\udfd7\ufe0f"}} source: claude-night-market source_plugin: archetypes --- > **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # The Pipeline (Pipes and Filters) Paradigm ## When to Employ This Paradigm - When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines. - When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others. - When failure isolation between stages is a critical requirement. ## Adoption Steps 1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema. 2. **Connect via Pipes**: Connect the filters using "pipes," which can be implemented as streams, message queues, or in-memory channels. validate these pipes support back-pressure and buffering. 3. **Maintain Stateless Filters**: Where possible, design filters to be stateless. Any required state should be persisted externally or managed at the boundaries of the pipeline. 4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates. 5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently. ## Key Deliverables - An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data. - A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions. - Observability dashboards that visualize stage-level Key Performance Indicators (KPIs). ## Risks & Mitigations - **Single-Stage Bottlenecks**: - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline. - **Schema Drift Between Stages**: - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes. - **Back-Pressure Failures**: - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress. ## Troubleshooting ### Common Issues **Command not found** Ensure all dependencies are installed and in PATH **Permission errors** Check file permissions and run with appropriate privileges **Unexpected behavior** Enable verbose logging with `--verbose` flag


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)