Skip to content
Agentmart
Submit a tool

hypothesis-formulation

Structured scientific hypothesis generation from observations.

Skill by researchclaw

IndexedMIT
Write a review

Chalk Labs

Need help deploying hypothesis-formulation?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/raja21068-hypothesis-formulation

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.

Use cases: research

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/hypothesis-formulation

What's inside

Skill name
hypothesis-formulation
Description
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
License
MIT
Metadata
author: researchclaw · version: 1.0 · category: experiment · priority: 3 · references: adapted from K-Dense-AI/claude-scientific-skills · trigger-keywords: hypothesis,prediction,mechanism,falsifiable,null,alternative,testable · applicable-stages: 7,8,9
Instructions
38 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

## Hypothesis Formulation Best Practice

### Structured Hypothesis Development
1. Start with a clear observation or pattern that requires explanation
2. Review existing literature for known mechanisms and prior explanations
3. Identify what is already established vs. what remains uncertain
4. Formulate the hypothesis as a specific, testable statement
5. Ensure the hypothesis is falsifiable — define what outcome would refute it

### Hypothesis Format
1. **Null hypothesis (H0)**: There is no effect or no difference
2. **Alternative hypothesis (H1)**: There is a specific, directional effect
3. State both explicitly; design experiments to reject H0
4. Use "If... then... because..." structure for mechanistic hypotheses:
   - If [independent variable is manipulated], then [predicted outcome], because [proposed mechanism]

### Generating Competing Hypotheses
1. Propose at least 2-3 plausible explanations for the same observation
2. For each, identify unique predictions that distinguish it from alternatives
3. Rank hypotheses by parsimony, consistency with prior evidence, and testability
4. Design experiments that can discriminate between competing hypotheses
5. Consider confounding variables that could produce the same observation

### Testable Predictions
1. Derive specific, measurable predictions from each hypothesis
2. Define expected effect direction AND approximate magnitude
3. Specify what experimental conditions would confirm vs. refute the prediction
4. Identify potential confounds and plan controls to address them
5. Ensure predictions are achievable with available methods and resources

### Aligning with Experimental Design
1. Map each hypothesis to a concrete experimental condition or comparison
2. Ensure sample size is adequate to detect the predicted effect (power analysis)
3. Pre-register hypotheses and analysis plans when possible
4. Distinguish confirmatory (hypothesis-testing) from exploratory analyses
5. Plan for both positive and null results — what will you conclude in each case?

From raja21068/AutoScholar, MIT.

More from raja21068/AutoScholar

Signals

Stars
0
Forks
0
Open issues
0
Last commit (this folder)
Apr 12, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.0
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from researchclaw

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get hypothesis-formulation running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying hypothesis-formulation?

CallGet help
Cite this page

hypothesis-formulation skill. Agent Mart. https://agentmart.dev/skills/raja21068-hypothesis-formulation (last updated 2026-10-24)