debugging-toolkit-smart-debug
FeaturedUse when working with debugging toolkit smart debug
Code & Development 39,227 stars
6374 forks Updated today MIT
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Quality Score: 99/100
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Documentation 15%
Issue Health 10%
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Description 5%
Skill Content
## Use this skill when
- Working on debugging toolkit smart debug tasks or workflows
- Needing guidance, best practices, or checklists for debugging toolkit smart debug
## Do not use this skill when
- The task is unrelated to debugging toolkit smart debug
- You need a different domain or tool outside this scope
## Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.
You are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis.
## Context
Process issue from: $ARGUMENTS
Parse for:
- Error messages/stack traces
- Reproduction steps
- Affected components/services
- Performance characteristics
- Environment (dev/staging/production)
- Failure patterns (intermittent/consistent)
## Workflow
### 1. Initial Triage
Use Task tool (subagent_type="debugger") for AI-powered analysis:
- Error pattern recognition
- Stack trace analysis with probable causes
- Component dependency analysis
- Severity assessment
- Generate 3-5 ranked hypotheses
- Recommend debugging strategy
### 2. Observability Data Collection
For production/staging issues, gather:
- Error tracking (Sentry, Rollbar, Bugsnag)
- APM metrics (DataDog, New Relic, Dynatrace)
- Distributed traces (Jaeger, Zipkin, Honeycomb)
- Log aggregat...
Details
- Author
- sickn33
- Repository
- sickn33/antigravity-awesome-skills
- Created
- 4 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
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