popperian-debug

Debug software failures through falsification-driven hypothesis testing.

2|Updated Apr 30, 2026
One-click install
npx skills add https://github.com/NlightNFotis/skills --skill popperian-debug
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: popperian-debug
Source: https://github.com/NlightNFotis/skills/tree/main/popperian-debug
Command: npx skills add https://github.com/NlightNFotis/skills --skill popperian-debug

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you break out of unproductive debugging loops by replacing guess-driven troubleshooting with a disciplined falsification process. It is especially useful when the obvious fix failed, the symptoms are confusing, or multiple causes seem plausible.

Core Features & Use Cases

  • Structured Hypothesis Testing: Forces explicit fact collection, generation of multiple distinct hypotheses, and ranked investigation instead of anchoring on the first idea.
  • Disconfirming Tests First: Focuses on designing tests that could rule a hypothesis out, which reduces confirmation bias and speeds root-cause discovery.
  • Handles Tricky Failures: Adapts well to intermittent bugs, environment-specific issues, contradictory symptoms, and regressions after recent changes.
  • Use Case: When a test appears flaky and recent commits suggest several suspicious causes, this Skill helps you reproduce the failure, separate real facts from misleading framing, cheaply falsify red herrings, and converge on the actual defect.

Quick Start

Use the popperian-debug skill to investigate a failing test by listing the facts, proposing several distinct causes, and trying to disprove the top hypothesis before making changes.

Frequently Asked Questions about popperian-debug

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

FAQPage Schema
How do I find the root cause of flaky tests using hypothesis testing?

Root cause analysis for flaky tests requires falsification-driven hypothesis testing to disprove potential causes. You list explicit facts, propose materially distinct hypotheses, and design disconfirming tests to rule out incorrect causes instead of guessing.

What is the best way to debug intermittent software failures with contradictory symptoms?

Debugging intermittent software failures requires disciplined falsification by separating real facts from misleading framing. You generate multiple ranked hypotheses, test one change at a time to disconfirm red herrings, and verify the actual defect against the original reproduction.

How do I fix a regression when my obvious software fix failed?

When a regression fix fails, you break unproductive loops by collecting explicit facts and designing disconfirming tests. Instead of anchoring on the first idea, you rank multiple distinct hypotheses and try to disprove the top one before making code changes.

How do I debug environment-specific defects without guessing?

Debugging environment-specific defects without guessing uses falsification to identify root causes. You collect explicit facts, propose several distinct causes, and execute one-change-at-a-time validation to systematically disconfirm incorrect hypotheses.

When should I use a falsification approach for software diagnosis?

Use falsification for software diagnosis when obvious fixes fail, symptoms are confusing, or multiple causes seem plausible. It replaces guess-driven troubleshooting by focusing on disconfirming tests first to reduce confirmation bias and converge on the actual defect.