hypothesis-testing
Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.
Profile →npx skills add rohitg00/skillkit --skill hypothesis-testing --agent codex
Same command for any agent — swap --agent for claude-code, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Hypothesis-Driven Debugging You are applying the scientific method to debugging. Form clear hypotheses, design tests that can definitively confirm or reject them, and systematically narrow down to the truth. ## Core Principle **Every debugging action should test a specific hypothesis. Random changes are not debugging.** ## The Scientific Debugging Method ### 1. Observe - Gather Facts Before forming hypotheses, collect observations: - What exactly happens? (specific symptoms) - When does it happen? (timing, frequency) - Where does it happen? (environment, component) - What changed recently? (code, config, data) **Write down observations objectively:** ``` Observations: - API returns 500 error on POST /orders - Happens only when cart has > 10 items - Started after deployment on 2024-01-15 - Works fine in staging environment - Error logs show "connection refused" to inventory service ``` ### 2. Hypothesize - Form Testable Theories **Examples (bad → good):** - ~~"Something is wrong with the network"~~ → "The inventory service connection pool is exhausted when processing orders with >10 items" - ~~"There might be a race condition"~~ → "The order processing timeout (5s) is insufficient
- Core Principle
- The Scientific Debugging Method
- 1. Observe - Gather Facts
- 2. Hypothesize - Form Testable Theories
- 3. Predict - Define Expected Results
- 4. Test - Experiment Systematically
- 5. Analyze - Interpret Results
- Hypothesis Tracking Template
- Testing Techniques by Hypothesis Type
- Testing Timing Hypotheses
- Testing Data Hypotheses
- Testing State Hypotheses
- Decision Tree
- Integration with Other Skills
What does the hypothesis-testing skill do?
Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.
How do I install it?
Run `npx skills add rohitg00/skillkit --skill hypothesis-testing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From rohitg00/skillkit, a repository with 1,422 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.