How do I ensure the debugger has the most up-to-date information?

Always run 'claudemem status' to verify enrichment. If symbol_summary counts are at zero, run 'claudemem enrich' to allow the AI to index the behavioral patterns of your code.

Why are grep, find, and glob forbidden in this skill?

Debugger Detective uses claudemem's indexed memory to perform semantic searches. Traditional text-matching tools like grep lack the context of code behavior and side effects, which are critical for effective root cause analysis.

What makes symbol_summary important for debugging?

The symbol_summary document type contains LLM-generated information about side effects, parameters, and error paths. This allows the skill to see what a function *does* to the system state, not just what the text says.

Can Debugger Detective help with memory leaks?

Yes. By using semantic queries focused on event listeners, subscriptions, and growing collections, it can identify patterns where cleanup logic is missing or resources are not being disposed of correctly.

Debugger Detective

Name: Debugger Detective
Author: MadAppGang

byMadAppGang

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177

보안 및 테스팅

Investigates codebase bugs and identifies root causes using advanced indexed memory and semantic behavioral search.

소개

Debugger Detective is a specialized diagnostic skill that replaces traditional text-based searching with semantic, behavior-aware code analysis. By leveraging claudemem v0.2.0 and LLM-enriched symbol summaries, it traces error origins, identifies state mutations, and uncovers complex failure paths. It is designed for developers who need to move beyond simple grep patterns to understand the 'why' behind a bug, prioritizing side-effect analysis and symptom-to-cause mapping for efficient incident response.

주요 기능

177 GitHub stars
Root Cause Mapping: Traces symptoms backwards through the call stack to find the original failure point.
Side Effect Tracking: Analyzes symbol summaries to identify data corruption and state changes.
Error Path Visualization: Automatically identifies how exceptions propagate through complex logic.
Semantic Behavioral Search: Finds code by meaning and behavior instead of exact text matches.
LLM Enrichment: Leverages AI-generated summaries to understand function intent and potential pitfalls.

사용 사례

Identifying unintended side effects in legacy codebases during refactoring or incident response.
Investigating race conditions and async logic failures within shared state environments.
Tracing the origin of 'undefined' or null-reference errors in complex data mapping flows.

소개

주요 기능

177 GitHub stars
Root Cause Mapping: Traces symptoms backwards through the call stack to find the original failure point.
Side Effect Tracking: Analyzes symbol summaries to identify data corruption and state changes.
Error Path Visualization: Automatically identifies how exceptions propagate through complex logic.
Semantic Behavioral Search: Finds code by meaning and behavior instead of exact text matches.
LLM Enrichment: Leverages AI-generated summaries to understand function intent and potential pitfalls.

사용 사례

Identifying unintended side effects in legacy codebases during refactoring or incident response.
Investigating race conditions and async logic failures within shared state environments.
Tracing the origin of 'undefined' or null-reference errors in complex data mapping flows.