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Hypothesis Generation

Listed
davila7·vlatest·testing·MIT

Davila7's hypothesis-generation suggests plausible explanations for observed data, sparking new avenues of investigation and problem-solving.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill hypothesis-generation -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. none of the 457 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as davila7/claude-code-templates.
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About this skill

What it does

This skill enables AI agents to systematically formulate testable hypotheses based on provided context or data. It structures the reasoning process to move from observations to specific, falsifiable predictions suitable for experimentation.

When to use it

  • Root cause analysis: When investigating why a system is failing or underperforming without clear error logs.
  • Product strategy: During brainstorming sessions to generate potential features or user behavior predictions before building.
  • Scientific research: To draft initial experimental designs and expected outcomes for new variables.
  • Troubleshooting: When debugging complex codebases where the failure point is not immediately obvious from symptoms.

Key capabilities

  • Analyzes provided context to identify patterns or anomalies.
  • Generates multiple distinct hypotheses rather than a single assumption.
  • Structures outputs to include the core prediction and implied conditions for testing.

Example prompts

  • "Analyze these server logs and generate three hypotheses explaining the spike in latency at 2:00 PM."
  • "Given our user feedback on the new checkout flow, propose hypotheses about where users are dropping off."
  • "I observed that the model performs poorly on images with low contrast. Generate a hypothesis for why this is happening."

Tips & gotchas

Ensure you provide sufficient context or data points; vague inputs will result in generic hypotheses. This skill is designed to spark ideas, not validate them—you must still design experiments to test the generated predictions.