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

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.