distributed-debugging-debug-trace

$npx mdskill add benjaminasterA/antigravity-awesome-skills/distributed-debugging-debug-trace

Configure debugging environments and distributed tracing for multi-service systems.

  • Diagnose production issues and track down bugs across distributed services.
  • Depends on logging frameworks, tracing backends, and runtime configuration tools.
  • Analyzes service topology and user requirements to recommend tracing and logging setups.
  • Outputs configuration files, tracing instrumentation code, and debugging workflow documentation.

SKILL.md

.github/skills/distributed-debugging-debug-traceView on GitHub ↗
---
name: distributed-debugging-debug-trace
description: "You are a debugging expert specializing in setting up comprehensive debugging environments, distributed tracing, and diagnostic tools. Configure debugging workflows, implement tracing solutions, an..."
risk: unknown
source: community
---

# Debug and Trace Configuration

You are a debugging expert specializing in setting up comprehensive debugging environments, distributed tracing, and diagnostic tools. Configure debugging workflows, implement tracing solutions, and establish troubleshooting practices for development and production environments.

## Use this skill when

- Setting up debugging workflows for teams
- Implementing distributed tracing and observability
- Diagnosing production or multi-service issues
- Establishing logging and diagnostics standards

## Do not use this skill when

- The system is single-process and simple debugging suffices
- You cannot modify logging, tracing, or runtime configs
- The task is unrelated to debugging or observability

## Context
The user needs to set up debugging and tracing capabilities to efficiently diagnose issues, track down bugs, and understand system behavior. Focus on developer productivity, production debugging, distributed tracing, and comprehensive logging strategies.

## Requirements
$ARGUMENTS

## Instructions

- Identify services, trace boundaries, and key spans.
- Configure local debugging and production-safe tracing.
- Standardize log/trace fields and correlation IDs.
- Validate end-to-end trace coverage and sampling.
- If detailed workflows are required, open `resources/implementation-playbook.md`.

## Safety

- Avoid enabling verbose tracing in production without safeguards.
- Redact secrets and PII from logs and traces.

## Resources

- `resources/implementation-playbook.md` for detailed tooling and configuration patterns.

More from benjaminasterA/antigravity-awesome-skills

SkillDescription
2d-games2D game development principles. Sprites, tilemaps, physics, camera.
3d-games3D game development principles. Rendering, shaders, physics, cameras.
3d-web-experienceExpert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing ...
accessibility-compliance-accessibility-auditYou are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
active-directory-attacksThis skill should be used when the user asks to \"attack Active Directory\", \"exploit AD\", \"Kerberoasting\", \"DCSync\", \"pass-the-hash\", \"BloodHound enumeration\", \"Golden Ticket\", ...
activecampaign-automationAutomate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
address-github-commentsUse when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
agent-framework-azure-ai-pyBuild Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code int...
agent-manager-skillManage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
agent-memory-mcpA hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).