Can I use this with existing observability tools?

Yes, the debugger can ingest traces from OpenTelemetry, Sentry, LangSmith, and browser console logs to aid in high-fidelity diagnosis.

Does it automatically document every bug?

While it can mine session logs automatically at the end of a session, manual storage is recommended mid-session to ensure high-quality, high-score incident documentation.

Where are the debugging incidents stored?

Incidents are stored locally in your project under the `.claude/memory/incidents/` directory as structured JSON files, allowing them to be versioned with your code.

How does the 'Parallel Domain Assessment' work?

When an issue is complex, the skill launches multiple specialized assessors (Database, Frontend, API, and Performance) simultaneously to find the root cause across different architectural layers.

Debugging Memory

Name: Debugging Memory
Author: tyroneross

bytyroneross

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Security & Testing

Prevents redundant troubleshooting by integrating a persistent, searchable memory system for software bugs and resolutions.

About

Debugging Memory is a high-performance skill for Claude Code that implements a 'never solve the same bug twice' philosophy. It transforms Claude from a stateless assistant into a context-aware debugger that maintains a local repository of past incidents. Before investigating any issue, the skill automatically searches its memory for high-confidence matches from previous sessions. For new problems, it provides a rigorous five-step debugging framework—Reproduce, Isolate, Diagnose, Fix, and Verify—coupled with parallel domain assessments that can simultaneously analyze database, frontend, API, and performance bottlenecks. By documenting every fix in a structured JSON format, it builds a project-specific knowledge base that improves in accuracy through pattern recognition and trace integration.

Key Features

Memory-First Search: Automatically queries past incidents before starting new investigations to find immediate fixes.
Observability Integration: Ingests traces from OpenTelemetry, Sentry, and browser logs to provide data-driven root cause analysis.
Pattern Recognition: Automatically extracts reusable solution templates when three or more similar incidents occur.
Parallel Domain Assessment: Simultaneously launches specialized sub-agents to diagnose issues across DB, Frontend, API, and Performance layers.
Structured Incident Logging: Generates detailed, version-controllable JSON documentation for every bug resolved.
2 GitHub stars

Use Cases

Rapidly diagnosing 'vague' regressions that could originate in either the database, API, or frontend.
Accelerating developer onboarding by providing an AI-driven 'memory' of historical codebase quirks and their solutions.
Maintaining a persistent technical debt and bug-fix log in large-scale repositories.

About

Key Features

Memory-First Search: Automatically queries past incidents before starting new investigations to find immediate fixes.
Observability Integration: Ingests traces from OpenTelemetry, Sentry, and browser logs to provide data-driven root cause analysis.
Pattern Recognition: Automatically extracts reusable solution templates when three or more similar incidents occur.
Parallel Domain Assessment: Simultaneously launches specialized sub-agents to diagnose issues across DB, Frontend, API, and Performance layers.
Structured Incident Logging: Generates detailed, version-controllable JSON documentation for every bug resolved.
2 GitHub stars

Use Cases

Rapidly diagnosing 'vague' regressions that could originate in either the database, API, or frontend.
Accelerating developer onboarding by providing an AI-driven 'memory' of historical codebase quirks and their solutions.
Maintaining a persistent technical debt and bug-fix log in large-scale repositories.