python-azure-iot-edge-modules

# Python Azure IoT Edge Modules Use this skill to design, implement, and validate Python-based IoT Edge modules for telemetry processing, local inference, protocol translation, and edge-to-cloud integration. ## When To Use Use this skill for requests like: - "quiero crear un modulo Python para IoT Edge" - "como despliego modulos edge con manifest" - "necesito filtrar/agregar telemetria antes de subirla" - "como manejo desconexiones y reintentos en edge" ## Mandatory Docs Review Before recommending runtime behavior or deployment decisions, review: - https://learn.microsoft.com/azure/iot-edge/ - https://learn.microsoft.com/es-es/azure/iot-edge/ Minimum checks: - Runtime architecture and module lifecycle. - Supported host OS and versions. - Deployment model and configuration flow. - Current release/version guidance. If documentation cannot be fetched, proceed with explicit assumptions and flag them clearly. ## Python Official References and Best Practices (Required) Before proposing Python implementation details, consult official Python sources: - https://www.python.org/ - https://docs.python.org/3/ - https://docs.python.org/3/reference/ - https://docs.python.org/3/library/ - references/python-official-best-practices.md Prefer official docs over community snippets unless there is a specific compatibility reason to deviate. ## Goals - Deliver module architecture and implementation plan that is production-focused. - Ensure reliable edge messaging under network variability. - Provide deployment, observability, and validation artifacts. ## Module Use Cases - Protocol adapter (serial/Modbus/OPC-UA to IoT message format). - Telemetry enrichment and normalization. - Local anomaly detection or inference. - Command orchestration and local actuator control. ## Delivery Workflow ### 1) Contract and Interfaces Define: - Module inputs and outputs. - Message schema and versioning policy. - Routes and priorities for normal vs critical telemetry. - Desired properties used for dynamic configuration. ### 2) Runtime and Packaging Specify: - Python runtime version target. - Container image strategy (base image, slim footprint, CVE hygiene). - Resource profile (CPU/memory bounds). - Startup and health checks. ### 3) Reliability Design Implement and validate: - Retries with exponential backoff and jitter. - Graceful degradation on upstream failures. - Local queueing strategy where needed. - Idempotent processing for replayed messages. ### 4) Security Controls Require: - No plaintext secrets in code or manifest. - Least-privilege module behavior. - Secure transport and trusted cert chain handling. - Traceability for command handling and state changes. ### 5) Deployment and Operations Define: - Environment-specific deployment manifests. - Rollout strategy (pilot, staged, broad). - Rollback criteria. - SLOs and alerting conditions. ## Reuse Other Skills When relevant, combine with: - `azure-smart-city-iot-solution-builder` for platform-level architecture. - `appinsights-instrumentation` for telemetry instrumentation approaches. - `azure-resource-visualizer` for architecture diagrams and dependency mapping. Also use `references/python-official-best-practices.md` as baseline quality criteria for module design and implementation guidance. ## Required Output Always provide: 1. Module design brief (purpose, inputs, outputs). 2. Deployment model (image, manifest, env settings). 3. Reliability and error-handling strategy. 4. Security and operations checklist. 5. Test matrix (functional, chaos, performance, rollback). ## Output Template Use `references/python-edge-module-template.md` to structure implementation proposals and reviews. 1. Context and assumptions 2. Module architecture 3. Deployment and configuration 4. Reliability, security, observability 5. Validation and rollout plan ## Guardrails - Do not recommend direct production rollout without pilot stage. - Do not embed secrets in Dockerfiles, source, or manifests. - Do not omit health probes, restart behavior, and rollback criteria.