home / skills / greyhaven-ai / claude-code-config / smart-debugging

smart-debugging skill

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/grey-haven-plugins/incident-response/skills/smart-debugging

This skill provides AI-powered debugging with stack trace analysis and automated fix suggestions to speed root cause investigations.

npx playbooks add skill greyhaven-ai/claude-code-config --skill smart-debugging

Review the files below or copy the command above to add this skill to your agents.

Files (13)

SKILL.md

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---
name: grey-haven-smart-debugging
description: "AI-powered intelligent debugging with stack trace analysis, error pattern recognition, and automated fix suggestions. Use when debugging complex errors, analyzing stack traces, or performing root cause analysis. Triggers: 'debug', 'error analysis', 'stack trace', 'root cause', 'troubleshooting'."
# v2.0.43: Skills to auto-load for debugging work
skills:
  - grey-haven-code-style
  - grey-haven-testing-strategy
  - grey-haven-incident-response
# v2.0.74: Tools for debugging work
allowed-tools:
  - Read
  - Write
  - MultiEdit
  - Bash
  - Grep
  - Glob
  - TodoWrite
---

# Smart Debugging Skill

AI-powered intelligent debugging with stack trace analysis, error pattern recognition, and automated fix suggestions.

## Description

Systematic error diagnosis through triage, investigation, root cause analysis, fix generation, and prevention strategies.

## What's Included

- **Examples**: Stack trace analysis, error pattern fixes
- **Reference**: Debugging methodologies, common errors
- **Templates**: Debug report templates, RCA structures

## Use When

- Systematic error diagnosis needed
- Stack traces to analyze
- Root cause investigation
- Automated fix suggestions

## Related Agents

- `smart-debug`

**Skill Version**: 1.0

Overview

This skill delivers AI-powered intelligent debugging that analyzes stack traces, recognizes error patterns, and suggests automated fixes. It guides a systematic workflow from triage and investigation to root cause analysis and prevention strategies. Use it to speed up troubleshooting and produce reproducible debug reports and RCAs.

How this skill works

You provide an error context such as a stack trace, log excerpt, or failing test output. The skill triages the failure, extracts key frames and error types, matches patterns against known issues, and proposes prioritized hypotheses and remediation steps. It can generate concrete fix suggestions, patch snippets, and a structured debug report or RCA template for follow-up.

When to use it

Analyzing raw stack traces from production or CI failures
Investigating intermittent or complex errors with unclear root cause
Generating candidate fixes or code changes to test locally
Preparing a concise root cause analysis report for stakeholders
Automating initial troubleshooting triage to reduce mean time to repair

Best practices

Include full stack traces and relevant log lines to improve diagnosis
Provide context: recent code changes, deployment environment, and reproducer steps
Run suggested fixes in an isolated environment or branch before merging
Use generated RCA templates to document findings and preventive actions
Combine automated suggestions with developer review for safety and correctness

Example use cases

A CI job fails with an obscure Python traceback — get prioritized root causes and patch suggestions
Recurring null pointer errors across microservices — identify the common pattern and propose a fix strategy
Postmortem preparation: convert collected logs and traces into a structured RCA document
Local debugging: feed a failing unit test and receive targeted code edits to try
On-call support: rapidly triage alerts by extracting probable causes and immediate mitigation steps

FAQ

What inputs produce the best results?

Full stack traces, relevant logs, recent code diffs, and environment details yield the most accurate diagnoses.

Are suggested fixes guaranteed safe to apply?

Suggestions are automated starting points and must be reviewed and tested in a safe environment before deployment.