Comprehensive protocol for validating root causes of software issues. Use when you need to systematically debug a complex bug, flaky test, or unknown system behavior by forming hypotheses and validating them with specific tasks.
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---
name: debugging-protocol
description: Comprehensive protocol for validating root causes of software issues. Use when you need to systematically debug a complex bug, flaky test, or unknown system behavior by forming hypotheses and validating them with specific tasks.
---
# Debugging Protocol
## Overview
This skill provides a rigorous framework for debugging complex software issues. It moves beyond ad-hoc troubleshooting to a structured process of hypothesis generation and validation.
Use this skill to:
1. Formalize a debugging session.
2. Systematically eliminate potential root causes.
3. Document findings for future reference or team communication.
## Protocol Workflow
To run a structured debugging session, follow these steps:
### 1. Initialize the Session
Create a new debugging document using the provided template. This serves as the "source of truth" for the investigation.
**Template location:** `assets/debugging-session-template.md`
**Save to:** `docs/debugging/{issue-name}-{YYYY-MM-DD}-{HHmm}.md`
1. Create `docs/debugging/` if it doesn't exist
2. Copy the template and fill in the issue details
3. This makes the session accessible from other conversations and agents (e.g., when handing off to a `/bugfix` or `/workflow-solo` workflow)
### 2. Define the Problem
Clearly articulate the **System Context** and **Problem Statement**.
* **Symptom**: What is the observable behavior? How does it differ from expected behavior?
* **Scope**: Which components are involved?
### 3. Formulate Hypotheses
List distinct, testable hypotheses.
* Avoid vague guesses.
* Differentiate between layers (e.g., "Frontend Hypothesis" vs "Backend Hypothesis").
* Example: "Race condition in UI state update" vs "Database schema misconfiguration".
### 4. Design Validation Tasks
For each hypothesis, design a specific validation task.
* **Objective**: What are you trying to prove or disprove?
* **Steps**: Precise, reproducible actions.
* **Code Pattern**: Provide the exact code or command to run (e.g., a specific SQL query, a Python script using the client library, a `curl` command).
* **Success Criteria**: Explicitly state what output confirms the hypothesis.
### 5. Execute and Document
Run the tasks in order. For each task, record:
* **Status**: ✅ VALIDATED, ❌ FAILED, or ⚠️ INCONCLUSIVE.
* **Findings**: Key observations and raw evidence (logs, screenshots).
* **Conclusion**: Does this support or refute the hypothesis?
### 6. Determine Root Cause
Synthesize the findings into a **Root Cause Analysis**.
* Identify the Primary Root Cause.
* Assign a Confidence Level.
* Propose specific fixes.
## Best Practices
* **Be Specific**: Don't just say "check the logs." Say "grep for 'Error 500' in `/var/log/nginx/access.log`".
* **Isolate Variables**: Change one thing at a time.
* **Validate Assumptions**: Verify configuration and versions first (e.g., "Task 1: Validate Current Schema").
* **Preserve Evidence**: Keep the specific trace IDs, log timestamps, or reproduction scripts.
## Language-Specific Modules
The `languages/` directory contains **modular, language-specific debugging guides**. When debugging a project, load the relevant language module to augment this protocol with language-specific tools, hypothesis categories, and validation strategies.
**Convention:** Each module is a standalone markdown file at `languages/{language}.md`.
**How to use:**
1. Identify the primary language of the codebase being debugged
2. Load the corresponding module from `languages/`
3. Integrate its toolchain, hypothesis categories, and validation tasks into your debugging session
**Available modules:**
| Module | Languages/Runtimes |
|---|---|
| [Go](languages/go.md) | Go (goroutines, pprof, Delve, race detector) |
| [TypeScript](languages/typescript.md) | TypeScript, Node.js, Vue, React (async debugging, memory leaks) |
| [Python](languages/python.md) | Python, Django, FastAPI (pdb, async, import resolution) |
| [Rust](languages/rust.md) | Rust (cargo, rustc, tokio) |
| [Java](languages/java.md) | Java, Spring Boot (JVM tools, heap/thread dumps, connection pools) |
| [C#](languages/csharp.md) | C#, .NET, ASP.NET Core (dotnet diagnostics, EF Core, async deadlocks) |
| [Swift](languages/swift.md) | Swift, SwiftUI, iOS/macOS (LLDB, Instruments, actors, retain cycles) |
| [Flutter](languages/flutter.md) | Flutter, Dart (DevTools, widget rebuilds, layout overflow, isolates) |
| [C++](languages/cpp.md) | C++ (sanitizers, GDB/LLDB, Valgrind, iterator invalidation, data races) |
| [Kotlin](languages/kotlin.md) | Kotlin (coroutine debugger, platform types, cancellation, JVM tools) |
| [PHP](languages/php.md) | PHP, Laravel (Xdebug, autoloading, sessions, white page of death) |
| [Ruby](languages/ruby.md) | Ruby, Rails (debug gem, Pry, Zeitwerk, N+1, monkey-patch detection) |
| [Frontend](languages/frontend.md) | Vue 3, React, browser, Vite (CSS, rendering, network) |
> **Contributing new modules:** To add support for a new language, create `languages/{language}.md` following the structure of existing modules. Each module should include: a toolchain reference table, language-specific hypothesis categories, validation task patterns, and an error-type-to-first-action quick reference.
## Rule Compliance
When debugging, verify against:
- Error Handling Principles @error-handling-principles.md (proper error propagation)
- Logging and Observability Principles @.agents/skills/logging-implementation/SKILL.md (structured logging for diagnostics)
- Testing Strategy @testing-strategy.md (regression test for the fix)