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debug-agent

Diagnose bugs by collecting runtime evidence into citable NDJSON log lines.

Updated Nov 26, 2025
Search Tags:#debugging#log-analysis#hypotheses#ndjson#runtime-logs
One-click install
npx skills add https://github.com/Aias/dotfiles --skill debug-agent-aias
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debug-agent
Source: https://github.com/Aias/dotfiles/tree/main/agents/skills/debug-agent
Command: npx skills add https://github.com/Aias/dotfiles --skill debug-agent-aias

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematic evidence-based debugging using runtime logs to generate hypotheses, instrument code with NDJSON logs, guide reproduction, and iteratively prove root cause with cited log lines.

Core Features & Use Cases

  • Generates 3-5 precise hypotheses about WHY a bug occurs and tests them in parallel with instrumentation.
  • Instruments code with structured logs (NDJSON) to collect evidence and trace the flow through runtime events.
  • Guides reproducible reproduction steps, including ad hoc scripts or manual steps, and records log-backed evidence for verification.
  • Analyzes logs to confirm or reject hypotheses with citation of log lines and preserves instrumentation for post-fix validation.
  • Supports safe cleanup by removing instrumentation after successful verification and ensuring no side effects.

Quick Start

Initiate a debugging session and start runtime-evidence collection for a reported issue with the debug-agent to guide reproduction and proof-based validation.

Frequently Asked Questions about debug-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I systematically debug runtime errors using NDJSON logs?▼

Runtime log analysis for debugging works by instrumenting code with NDJSON logs to trace runtime events, generating 3-5 hypotheses about the bug, and iteratively confirming or rejecting them based on cited log lines. This structured approach ensures reliable reproduction and evidence-based fixes.

What is the best way to generate and test bug hypotheses during incident response?▼

The best way to generate and test bug hypotheses is to create 3-5 precise hypotheses and test them in parallel using code instrumentation. Structured runtime logs provide the evidence needed to confirm or reject each hypothesis with direct log line citations.

How do I trace code execution flow to find a root cause with structured logs?▼

You trace code execution flow by instrumenting your code with structured NDJSON logs. This collects runtime evidence and maps the flow of events, allowing you to verify reproduction steps and pinpoint the exact root cause of the issue.

Can I use runtime log analysis for both QA testing and production incident response?▼

Yes, runtime log analysis applies to development, QA, and incident-response scenarios. It satisfies requirements for structured hypotheses, instrumentation, evidence analysis, and safe log-based verification across all these environments.

How do I safely remove code instrumentation after verifying a bug fix?▼

You safely remove code instrumentation after successful verification by following a structured cleanup process. This ensures no side effects remain and preserves the instrumentation logic for any post-fix validation if the issue reappears.

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