systematic-debugging

Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

systematic-debugging is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.

Teams using systematic-debugging should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/systematic-debugging/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/methodologies/rpikit/skills/systematic-debugging/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/systematic-debugging/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How systematic-debugging Compares

Feature / Agent	systematic-debugging	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Systematic Debugging

## Overview

Structured approach to investigating and resolving defects using hypothesis-driven methodology rather than trial-and-error.

## When to Use

- Step verification fails during implementation
- Unexpected behavior discovered during testing
- Bug reports require investigation
- Performance issues need root cause analysis

## Process

1. **Reproduce** - Confirm the defect with a minimal reproduction
2. **Hypothesize** - Form theories about the root cause
3. **Investigate** - Systematically test hypotheses (logs, breakpoints, bisection)
4. **Isolate** - Narrow to the specific component/line
5. **Fix** - Apply targeted fix addressing root cause
6. **Verify** - Confirm fix resolves the issue without regression

## Key Rules

- Never apply fixes without understanding the root cause
- Use web-researcher agent for unfamiliar error patterns
- Document the investigation path for future reference
- Verify that the fix does not introduce regressions

## Tool Use

Integrated into `methodologies/rpikit/rpikit-implement` (failure handling)

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