hypothesis-formulation
Structured scientific hypothesis generation from observations.
Skill by researchclaw
Chalk Labs
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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.
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About
Use cases: research
Reviews
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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.
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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
- Repository
- github.com/raja21068/AutoScholar
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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hypothesis-formulation skill. Agent Mart. https://agentmart.dev/skills/raja21068-hypothesis-formulation (last updated 2026-10-24)