python-azure-iot-edge-modules - Claude MCP Skill

# 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.