Agent skill

Hypothesis Generation

by spacering-net in spacering-net/codeg

Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.

MITAuto-check: notesResearch & Science

Install Hypothesis Generation

skills CLI
$ npx skills add spacering-net/codeg --skill hypothesis-generation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install spacering-net/codeg hypothesis-generation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/spacering-net/codeg.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src-tauri/science/skills/hypothesis-generation .claude/skills/hypothesis-generation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hypothesis-generation
GitHub stars
3.8k
Used in
15 other repos
Token cost
~3.6k tokens
SKILL.md length
1,560 words
Files
9 (incl. scripts, references, assets)
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.

  • Works in 8 steps: Understand the Phenomenon → Conduct Comprehensive Literature Search → Synthesize Existing Evidence → …
  • You have experimental observations
  • SKILL.md covers Overview, When to Use This Skill, Visual Enhancement with… and Workflow, plus 2 more sections
  • Runs Python scripts from its folder; calls python

What it does

Hypothesis Generation is an agent skill from spacering-net/codeg. Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts, reference files and assets (for example `assets/FORMATTING_GUIDE.md`, `references/experimental_design_patterns.md` and `references/hypothesis_quality_criteria.md`).

It sits in Research & Science, covering Hypothesis generation. The repository describes itself as: Collaborative multi-agent AI coding workspace: aggregate sessions from Claude Code, Codex, OpenCode, Pi, Grok Build, etc. Desktop app, self-hosted server, or Docker. The licence is MIT.

When your agent uses it

  • You have experimental observations
  • Data and need to formulate testable hypotheses with predictions
  • Propose mechanisms
  • Design experiments to test them

Example prompts

  • “/hypothesis-generation”

Requirements

  • Python 3
  • A credential in OPENROUTER_API_KEY
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Understand the Phenomenon
  2. Conduct Comprehensive Literature Search
  3. Synthesize Existing Evidence
  4. Generate Competing Hypotheses
  5. Evaluate Hypothesis Quality
  6. Design Experimental Tests
  7. Formulate Testable Predictions
  8. Present Structured Output

What it can do on your machine

Read from SKILL.md and the folder at commit 592131c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hypothesis Generation loads about 3.6k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 101 tokens; SKILL.md has 1,560 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~101
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from spacering-net/codeg at commit 592131c, republished under its MIT licence (© spacering-net). 1,560 words, ~3,554 tokens.

Download SKILL.mdSave it as .claude/skills/hypothesis-generation/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
hypothesis-generation
description
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
allowed-tools
Read, Write, Edit, Bash
license
MIT license
metadata.version
1.1
metadata.skill-author
K-Dense Inc.

Scientific Hypothesis Generation

Overview

Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains.

When to Use This Skill

This skill should be used when:

  • Developing hypotheses from observations or preliminary data
  • Designing experiments to test scientific questions
  • Exploring competing explanations for phenomena
  • Formulating testable predictions for research
  • Conducting literature-based hypothesis generation
  • Planning mechanistic studies across scientific domains

Visual Enhancement with Scientific Schematics

⚠️ MANDATORY: Every hypothesis generation report MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.

This is not optional. Hypothesis reports without visual elements are incomplete. Before finalizing any document:

  1. Generate at minimum ONE schematic or diagram (e.g., hypothesis framework showing competing explanations)
  2. Prefer 2-3 figures for comprehensive reports (mechanistic pathway, experimental design flowchart, prediction decision tree)

How to generate figures:

  • Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
  • Simply describe your desired diagram in natural language
  • Nano Banana Pro will automatically generate, review, and refine the schematic

How to generate schematics:

bash
python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

  • Create publication-quality images with proper formatting
  • Review and refine through multiple iterations
  • Ensure accessibility (colorblind-friendly, high contrast)
  • Save outputs in the figures/ directory

When to add schematics:

  • Hypothesis framework diagrams showing competing explanations
  • Experimental design flowcharts
  • Mechanistic pathway diagrams
  • Prediction decision trees
  • Causal relationship diagrams
  • Theoretical model visualizations
  • Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.


Workflow

Follow this systematic process to generate robust scientific hypotheses:

1. Understand the Phenomenon

Start by clarifying the observation, question, or phenomenon that requires explanation:

  • Identify the core observation or pattern that needs explanation
  • Define the scope and boundaries of the phenomenon
  • Note any constraints or specific contexts
  • Clarify what is already known vs. what is uncertain
  • Identify the relevant scientific domain(s)

Search existing scientific literature to ground hypotheses in current evidence. Use both PubMed (for biomedical topics) and general web search (for broader scientific domains):

For biomedical topics:

  • Use WebFetch with PubMed URLs to access relevant literature
  • Search for recent reviews, meta-analyses, and primary research
  • Look for similar phenomena, related mechanisms, or analogous systems

For all scientific domains:

  • Use WebSearch to find recent papers, preprints, and reviews
  • Search for established theories, mechanisms, or frameworks
  • Identify gaps in current understanding

Search strategy:

  • Begin with broad searches to understand the landscape
  • Narrow to specific mechanisms, pathways, or theories
  • Look for contradictory findings or unresolved debates
  • Consult references/literature_search_strategies.md for detailed search techniques
3. Synthesize Existing Evidence

Analyze and integrate findings from literature search:

  • Summarize current understanding of the phenomenon
  • Identify established mechanisms or theories that may apply
  • Note conflicting evidence or alternative viewpoints
  • Recognize gaps, limitations, or unanswered questions
  • Identify analogies from related systems or domains
4. Generate Competing Hypotheses

Develop 3-5 distinct hypotheses that could explain the phenomenon. Each hypothesis should:

  • Provide a mechanistic explanation (not just description)
  • Be distinguishable from other hypotheses
  • Draw on evidence from the literature synthesis
  • Consider different levels of explanation (molecular, cellular, systemic, population, etc.)

Strategies for generating hypotheses:

  • Apply known mechanisms from analogous systems
  • Consider multiple causative pathways
  • Explore different scales of explanation
  • Question assumptions in existing explanations
  • Combine mechanisms in novel ways
5. Evaluate Hypothesis Quality

Assess each hypothesis against established quality criteria from references/hypothesis_quality_criteria.md:

Testability: Can the hypothesis be empirically tested? Falsifiability: What observations would disprove it? Parsimony: Is it the simplest explanation that fits the evidence? Explanatory Power: How much of the phenomenon does it explain? Scope: What range of observations does it cover? Consistency: Does it align with established principles? Novelty: Does it offer new insights beyond existing explanations?

Explicitly note the strengths and weaknesses of each hypothesis.

6. Design Experimental Tests

For each viable hypothesis, propose specific experiments or studies to test it. Consult references/experimental_design_patterns.md for common approaches:

Experimental design elements:

  • What would be measured or observed?
  • What comparisons or controls are needed?
  • What methods or techniques would be used?
  • What sample sizes or statistical approaches are appropriate?
  • What are potential confounds and how to address them?

Consider multiple approaches:

  • Laboratory experiments (in vitro, in vivo, computational)
  • Observational studies (cross-sectional, longitudinal, case-control)
  • Clinical trials (if applicable)
  • Natural experiments or quasi-experimental designs
7. Formulate Testable Predictions

For each hypothesis, generate specific, quantitative predictions:

  • State what should be observed if the hypothesis is correct
  • Specify expected direction and magnitude of effects when possible
  • Identify conditions under which predictions should hold
  • Distinguish predictions between competing hypotheses
  • Note predictions that would falsify the hypothesis
Show full SKILL.md (813 more words)Show less
8. Present Structured Output

Generate a professional LaTeX document using the template in assets/hypothesis_report_template.tex. The report should be well-formatted with colored boxes for visual organization and divided into a concise main text with comprehensive appendices.

Document Structure:

Main Text (Maximum 4 pages):

  1. Executive Summary - Brief overview in summary box (0.5-1 page)
  2. Competing Hypotheses - Each hypothesis in its own colored box with brief mechanistic explanation and key evidence (2-2.5 pages for 3-5 hypotheses)
    • IMPORTANT: Use \newpage before each hypothesis box to prevent content overflow
    • Each box should be ≤0.6 pages maximum
  3. Testable Predictions - Key predictions in amber boxes (0.5-1 page)
  4. Critical Comparisons - Priority comparison boxes (0.5-1 page)

Keep main text highly concise - only the most essential information. All details go to appendices.

Page Break Strategy:

  • Always use \newpage before hypothesis boxes to ensure they start on fresh pages
  • This prevents content from overflowing off page boundaries
  • LaTeX boxes (tcolorbox) do not automatically break across pages

Appendices (Comprehensive, Detailed):

  • Appendix A: Comprehensive literature review with extensive citations
  • Appendix B: Detailed experimental designs with full protocols
  • Appendix C: Quality assessment tables and detailed evaluations
  • Appendix D: Supplementary evidence and analogous systems

Colored Box Usage:

Use the custom box environments from hypothesis_generation.sty:

  • hypothesisbox1 through hypothesisbox5 - For each competing hypothesis (blue, green, purple, teal, orange)
  • predictionbox - For testable predictions (amber)
  • comparisonbox - For critical comparisons (steel gray)
  • evidencebox - For supporting evidence highlights (light blue)
  • summarybox - For executive summary (blue)

Each hypothesis box should contain (keep concise for 4-page limit):

  • Mechanistic Explanation: 1-2 brief paragraphs (6-10 sentences max) explaining HOW and WHY
  • Key Supporting Evidence: 2-3 bullet points with citations (most important evidence only)
  • Core Assumptions: 1-2 critical assumptions

All detailed explanations, additional evidence, and comprehensive discussions belong in the appendices.

Critical Overflow Prevention:

  • Insert \newpage before each hypothesis box to start it on a fresh page
  • Keep each complete hypothesis box to ≤0.6 pages (approximately 15-20 lines of content)
  • If content exceeds this, move additional details to Appendix A
  • Never let boxes overflow off page boundaries - this creates unreadable PDFs

Citation Requirements:

Aim for extensive citation to support all claims:

  • Main text: 10-15 key citations for most important evidence only (keep concise for 4-page limit)
  • Appendix A: 40-70+ comprehensive citations covering all relevant literature
  • Total target: 50+ references in bibliography

Main text citations should be selective - cite only the most critical papers. All comprehensive citation and detailed literature discussion belongs in the appendices. Use \citep{author2023} for parenthetical citations.

LaTeX Compilation:

The template requires XeLaTeX or LuaLaTeX for proper rendering:

bash
xelatex hypothesis_report.tex
bibtex hypothesis_report
xelatex hypothesis_report.tex
xelatex hypothesis_report.tex

Required packages: The hypothesis_generation.sty style package must be in the same directory or LaTeX path. It requires: tcolorbox, xcolor, fontspec, fancyhdr, titlesec, enumitem, booktabs, natbib.

Page Overflow Prevention:

To prevent content from overflowing on pages, follow these critical guidelines:

  1. Monitor Box Content Length: Each hypothesis box should fit comfortably on a single page. If content exceeds ~0.7 pages, it will likely overflow.

  2. Use Strategic Page Breaks: Insert \newpage before boxes that contain substantial content:

    latex
    \newpage
    \begin{hypothesisbox1}[Hypothesis 1: Title]
    % Long content here
    \end{hypothesisbox1}
  3. Keep Main Text Boxes Concise: For the 4-page main text limit:

    • Each hypothesis box: Maximum 0.5-0.6 pages
    • Mechanistic explanation: 1-2 brief paragraphs only (6-10 sentences max)
    • Key evidence: 2-3 bullet points only
    • Core assumptions: 1-2 items only
    • If content is longer, move details to appendices
  4. Break Long Content: If a hypothesis requires extensive explanation, split across main text and appendix:

    • Main text box: Brief mechanistic overview + 2-3 key evidence points
    • Appendix A: Detailed mechanism explanation, comprehensive evidence, extended discussion
  5. Test Page Boundaries: Before each new box, consider if remaining page space is sufficient. If less than 0.6 pages remain, use \newpage to start the box on a fresh page.

  6. Appendix Page Management: In appendices, use \newpage between major sections to avoid overflow in detailed content areas.

Quick Reference: See assets/FORMATTING_GUIDE.md for detailed examples of all box types, color schemes, and common formatting patterns.

Quality Standards

Ensure all generated hypotheses meet these standards:

  • Evidence-based: Grounded in existing literature with citations
  • Testable: Include specific, measurable predictions
  • Mechanistic: Explain how/why, not just what
  • Comprehensive: Consider alternative explanations
  • Rigorous: Include experimental designs to test predictions

Resources

references/
  • hypothesis_quality_criteria.md - Framework for evaluating hypothesis quality (testability, falsifiability, parsimony, explanatory power, scope, consistency)
  • experimental_design_patterns.md - Common experimental approaches across domains (RCTs, observational studies, lab experiments, computational models)
  • literature_search_strategies.md - Effective search techniques for PubMed and general scientific sources
assets/
  • hypothesis_generation.sty - LaTeX style package providing colored boxes, professional formatting, and custom environments for hypothesis reports
  • hypothesis_report_template.tex - Complete LaTeX template with main text structure and comprehensive appendix sections
  • FORMATTING_GUIDE.md - Quick reference guide with examples of all box types, color schemes, citation practices, and troubleshooting tips

When preparing hypothesis-driven research for publication, consult the venue-templates skill for writing style guidance:

  • venue_writing_styles.md - Master guide comparing styles across venues
  • Venue-specific guides for Nature/Science, Cell Press, medical journals, and ML/CS conferences
  • reviewer_expectations.md - What reviewers look for when evaluating research hypotheses

© spacering-net, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references, assets) in src-tauri/science/skills/hypothesis-generation of spacering-net/codeg.

  • SKILL.md
  • assets/FORMATTING_GUIDE.md
  • assets/hypothesis_generation.sty
  • assets/hypothesis_report_template.tex
  • references/experimental_design_patterns.md
  • references/hypothesis_quality_criteria.md
  • references/literature_search_strategies.md
  • scripts/generate_schematic.py
  • scripts/generate_schematic_ai.py

Open the folder on GitHubat commit 592131c

Used in 15 other repositories

We found 33 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 15 other GitHub owners. This page covers the copy in spacering-net/codeg, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Hypothesis Generation

What does Hypothesis Generation do?

Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg. Hypothesis Generation is an agent skill from spacering-net/codeg. Structured hypothesis formulation from observations.

When should I use Hypothesis Generation?

Hypothesis Generation fits situations like: you have experimental observations; data and need to formulate testable hypotheses with predictions; propose mechanisms; design experiments to test them.

How do I install Hypothesis Generation in Claude Code?

Run `npx skills add spacering-net/codeg --skill hypothesis-generation -a claude-code`. Or copy the skill folder (src-tauri/science/skills/hypothesis-generation in spacering-net/codeg) into .claude/skills/hypothesis-generation in your project. Claude Code loads it when a task matches its description.

How do I install Hypothesis Generation in Codex?

Run `npx skills add spacering-net/codeg --skill hypothesis-generation -a codex`. Or copy the skill folder (src-tauri/science/skills/hypothesis-generation in spacering-net/codeg) into .agents/skills/hypothesis-generation in your project. Codex loads it when a task matches its description.

Can I use Hypothesis Generation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add spacering-net/codeg --skill hypothesis-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hypothesis-generation, .gemini/skills/hypothesis-generation, .github/skills/hypothesis-generation and .opencode/skills/hypothesis-generation in your project.

What does Hypothesis Generation need to run?

Going by SKILL.md and its folder, Hypothesis Generation needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3; A credential in OPENROUTER_API_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash.

Does Hypothesis Generation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hypothesis Generation safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Hypothesis Generation use?

Hypothesis Generation is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hypothesis Generation use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Hypothesis Generation?

Skills that share tags, products or a category with Hypothesis Generation: Nature Paper Card (Yuan1z0825/nature-skills, 46k stars), Hypothesis Generation (K-Dense-AI/claude-scientific-writer, 2.4k stars), Good Question (Rimagination/good-question, 305 stars) and Claim-Driven Experiment Planner (zjYao36/Auto-Research-Refine, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hypothesis Generation?

spacering-net (a GitHub organization) maintains it in spacering-net/codeg, which has 3,848 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 7, 2026.

Source: spacering-net/codeg on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.