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maple-python-style

Standardize Python OpenTelemetry observability for Maple services.

1.6k|107|Updated Feb 15, 2026
Search Tags:#logging#python#metrics#observability#tracing#opentelemetry#maple
One-click install
npx skills add https://github.com/MapleTechLabs/maple --skill maple-python-style-mapletechlabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: maple-python-style
Source: https://github.com/MapleTechLabs/maple/tree/main/skills/maple-python-style
Command: npx skills add https://github.com/MapleTechLabs/maple --skill maple-python-style-mapletechlabs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates inconsistent and error-prone Python OpenTelemetry implementations for Maple, preventing missing observability data and avoidable deployment failures from misconfigured telemetry setups.

Core Features & Use Cases

  • Standardized OTel Initialization: Pre-configured module-scope tracers, meters, and init logic with inline Maple endpoint and ingest key to eliminate environment variable-related deployment failures.
  • Consistent Span Instrumentation: Decorator and context manager templates for bounded work, plus correct error path span recording to ensure complete, accurate trace data.
  • OTLP Log Bridging: Ready-to-use configuration for forwarding existing Python logs to Maple with automatic trace/span correlation, no need to replace existing logging setups.
  • Use Case: A Python backend team building a Maple-integrated service can use this Skill to implement reliable, consistent observability across all services without manual OpenTelemetry configuration errors.

Quick Start

Use the maple-python-style skill to configure standardized OpenTelemetry observability for your Python Maple service, including correct trace spans, log forwarding, and metric collection setup.

Frequently Asked Questions about maple-python-style

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up OpenTelemetry tracing and logging for a Python backend service?▼

To set up OpenTelemetry observability for Python, initialize module-scope tracers and meters, use decorators for bounded work spans, and configure OTLP log bridging to forward logs with trace ID correlation.

Why are my Python OpenTelemetry traces and logs missing after deployment?▼

Missing traces and logs often occur from misconfigured telemetry endpoints. Inline Maple endpoint and ingest key configuration during OTel initialization prevents environment variable-related deployment failures and data loss.

Can I forward my existing Python logging setup to OpenTelemetry without replacing it?▼

Yes, you can forward existing Python logs using OTLP log bridging. This configuration automatically correlates your logs with trace and span IDs without requiring you to replace your current logging setup.

How do I record error paths correctly in OpenTelemetry spans using Python?▼

To record error paths in Python OpenTelemetry spans, apply context manager templates for bounded work and ensure correct error path span recording to capture complete and accurate trace data.

What is the best way to configure Python metrics collection for Maple integration?▼

The best way to configure Python metrics for Maple is using pre-configured module-scope meters and initialization logic, ensuring consistent metric collection setup and avoiding manual OpenTelemetry configuration errors.

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