productionos-debug

Optimize your debugging workflow with actionable insights.

8|Updated Mar 17, 2026

Search Tags:#debugging #codex #evidence #regression #hypothesis #root-cause #reproduction

Ready to run

npx skills add https://github.com/ShaheerKhawaja/ProductionOS --skill productionos-debug

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Please help me install this Agent Skill.
Skill: productionos-debug
Source: https://github.com/ShaheerKhawaja/ProductionOS/tree/main/codex-skills/productionos-debug
Command: npx skills add https://github.com/ShaheerKhawaja/ProductionOS --skill productionos-debug

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reproduce and diagnose complex Codex-driven bugs through a structured, hypothesis-driven workflow that emphasizes evidence gathering, ranked hypotheses, targeted fixes, and regression-proof verification.

Core Features & Use Cases

Reproduction & Evidence: Reproduce issues reliably and collect logs, changes, and test results.
Hypothesis Ranking & Testing: Generate and evaluate ranked root-cause hypotheses with stepwise verification.
Targeted Fix & Validation: Apply minimal fixes and validate against the original reproduction to ensure regression resistance.
Codex-Safe Debugging Workflow: Maintains a safe, auditable, codex-first debugging pipeline.

Quick Start

Describe the bug or failing behavior and start the productionos-debug workflow to reproduce, hypothesize, test, and identify the root cause.

Frequently Asked Questions about productionos-debug

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

FAQPage Schema

What is hypothesis-driven debugging and how does it work for Codex workflows?▼

Hypothesis-driven debugging is a structured workflow that reproduces issues, gathers evidence, ranks root-cause hypotheses, and applies targeted fixes. For Codex workflows, it guides users stepwise through reproduction, evidence collection, hypothesis testing, and regression-proof verification to identify the root cause.

How do I debug a failing test in a complex codebase using a structured workflow?▼

To debug a failing test, describe the bug or failing behavior and start the workflow to reproduce the issue reliably. It collects logs and test results, generates ranked root-cause hypotheses, applies minimal targeted fixes, and validates against the original reproduction to ensure regression resistance.

Can I limit the number of root-cause hypotheses generated during reproduction?▼

Yes, you can limit generated root-cause hypotheses by providing an optional max_hypotheses input. This constrains the hypothesis ranking process, allowing you to control the scope of stepwise verification and focus on the most probable targeted fixes during the Codex debugging workflow.

What's the best way to ensure a bug fix is regression-proof?▼

The best way to ensure a bug fix is regression-proof is to validate the targeted fix against the original reproduction commands. The workflow applies minimal fixes and verifies them using the initial evidence, ensuring the identified root cause is resolved without introducing new failing behaviors.

Does this debugging workflow require specific reproduction commands to start?▼

No, specific reproduction commands are optional inputs. The workflow can start with just a bug description, though providing optional reproduction commands helps it reproduce issues more reliably and gather precise evidence like logs and test results for ranked root-cause hypotheses.

Why should I use a hypothesis-driven approach instead of standard debugging?▼

A hypothesis-driven approach ranks root-cause hypotheses and verifies them stepwise against gathered evidence, preventing speculative fixes. It maintains a safe, auditable, Codex-first debugging pipeline that ensures minimal targeted fixes and regression-proof validation across complex codebases and reproducibility challenges.