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MunusCaritatis
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defect-analysis

Analyze Python package code paths, tests, and reports to produce a verified bug list.

Updated Aug 27, 2026
Search Tags:#automation#python#software-quality#bug-hunting#report-generation#adversarial-testing#defect-analysis
One-click install
npx skills add https://github.com/MunusCaritatis/dbtools --skill defect-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: defect-analysis
Source: https://github.com/MunusCaritatis/dbtools/tree/main/.skills/defect-analysis
Command: npx skills add https://github.com/MunusCaritatis/dbtools --skill defect-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates an adversarial defect analysis workflow to systematically discover, challenge, and verify bugs in Python packages.

Core Features & Use Cases

  • Three-agent pipeline: Bug Finder, Bug Skeptic, and Final Arbiter coordinating to maximize bug discovery and validation.
  • End-to-end reporting: Generates per-package defect reports and a final verdict to guide remediation.
  • Use Case: When asked to "find bugs" in a package path like python/gds_vault, run a structured analysis and obtain a verified bug list.

Quick Start

Run the defect-analysis flow on a target package by issuing the activation command: /defect-analysis python/gds_vault

Frequently Asked Questions about defect-analysis

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

FAQPage Schema
How do I automate bug hunting and defect analysis for a Python package?▼

Automate Python package defect analysis by running a three-agent pipeline that surfaces, challenges, and verifies bugs to produce a verified bug list. This adversarial workflow coordinates a Bug Finder, Bug Skeptic, and Final Arbiter to maximize validation.

What is the best way to run adversarial testing to find and verify bugs in Python code?▼

Adversarial testing for Python code is best run through a structured three-agent pipeline that challenges potential bugs to verify them. It analyzes code paths, tests, and reports to output a final verdict and per-step reports.

How do I generate a defect report for a Python package in my repository?▼

Generate a Python package defect report by issuing the analysis command with the target package path. The pipeline produces outputs stored under [PACKAGE_PATH]/docs/reference/defect-analysis/[DATE]-v[N]/ containing per-step reports and a final verdict.

Can I use automated bug hunting for Python packages without installing external dependencies?▼

Yes, you can run automated bug hunting for Python packages without external dependencies. The defect analysis pipeline operates with no listed dependencies, analyzing code paths and tests directly within your repository environment.

Where are the verified bug list and analysis reports stored after a defect analysis run?▼

Verified bug lists and analysis reports are stored under [PACKAGE_PATH]/docs/reference/defect-analysis/[DATE]-v[N]/. This directory contains the generated per-step reports and the final arbiter's verdict to guide remediation.

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