Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
$ npx skills add spacering-net/codeg --skill hypothesis-generation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install spacering-net/codeg hypothesis-generation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "hypothesis-generation" agent skill from https://github.com/spacering-net/codeg/tree/main/src-tauri/science/skills/hypothesis-generation into .claude/skills/hypothesis-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hypothesis-generation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/spacering-net/codeg/tree/main/src-tauri/science/skills/hypothesis-generationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add spacering-net/codeg --skill hypothesis-generation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install spacering-net/codeg hypothesis-generation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/spacering-net/codeg.git skills-src && mkdir -p .agents/skills && cp -r skills-src/src-tauri/science/skills/hypothesis-generation .agents/skills/hypothesis-generation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hypothesis-generation" agent skill from https://github.com/spacering-net/codeg/tree/main/src-tauri/science/skills/hypothesis-generation into .agents/skills/hypothesis-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hypothesis-generation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add spacering-net/codeg --skill hypothesis-generation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install spacering-net/codeg hypothesis-generation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/spacering-net/codeg.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/src-tauri/science/skills/hypothesis-generation .cursor/skills/hypothesis-generation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hypothesis-generation" agent skill from https://github.com/spacering-net/codeg/tree/main/src-tauri/science/skills/hypothesis-generation into .cursor/skills/hypothesis-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hypothesis-generation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/spacering-net/codeg.git --path src-tauri/science/skills/hypothesis-generation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add spacering-net/codeg --skill hypothesis-generation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install spacering-net/codeg hypothesis-generation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/spacering-net/codeg.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/src-tauri/science/skills/hypothesis-generation .gemini/skills/hypothesis-generation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hypothesis-generation" agent skill from https://github.com/spacering-net/codeg/tree/main/src-tauri/science/skills/hypothesis-generation into .gemini/skills/hypothesis-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hypothesis-generation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install spacering-net/codeg hypothesis-generationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add spacering-net/codeg --skill hypothesis-generation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/spacering-net/codeg.git skills-src && mkdir -p .github/skills && cp -r skills-src/src-tauri/science/skills/hypothesis-generation .github/skills/hypothesis-generation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hypothesis-generation" agent skill from https://github.com/spacering-net/codeg/tree/main/src-tauri/science/skills/hypothesis-generation into .github/skills/hypothesis-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hypothesis-generation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add spacering-net/codeg --skill hypothesis-generation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install spacering-net/codeg hypothesis-generation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/spacering-net/codeg.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/src-tauri/science/skills/hypothesis-generation .opencode/skills/hypothesis-generation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hypothesis-generation" agent skill from https://github.com/spacering-net/codeg/tree/main/src-tauri/science/skills/hypothesis-generation into .opencode/skills/hypothesis-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hypothesis-generation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
hypothesis-generationStructured 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. 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.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 592131c. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteEditBashFrom allowed-tools in the SKILL.md frontmatter.
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, Write, Edit, BashAutomated 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.
The full file from spacering-net/codeg at commit 592131c, republished under its MIT licence (© spacering-net). 1,560 words, ~3,554 tokens.
.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.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.
This skill should be used when:
⚠️ 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:
How to generate figures:
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.pngThe AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Follow this systematic process to generate robust scientific hypotheses:
Start by clarifying the observation, question, or phenomenon that requires explanation:
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:
For all scientific domains:
Search strategy:
references/literature_search_strategies.md for detailed search techniquesAnalyze and integrate findings from literature search:
Develop 3-5 distinct hypotheses that could explain the phenomenon. Each hypothesis should:
Strategies for generating hypotheses:
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.
For each viable hypothesis, propose specific experiments or studies to test it. Consult references/experimental_design_patterns.md for common approaches:
Experimental design elements:
Consider multiple approaches:
For each hypothesis, generate specific, quantitative predictions:
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):
\newpage before each hypothesis box to prevent content overflowKeep main text highly concise - only the most essential information. All details go to appendices.
Page Break Strategy:
\newpage before hypothesis boxes to ensure they start on fresh pagesAppendices (Comprehensive, Detailed):
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):
All detailed explanations, additional evidence, and comprehensive discussions belong in the appendices.
Critical Overflow Prevention:
\newpage before each hypothesis box to start it on a fresh pageCitation Requirements:
Aim for extensive citation to support all claims:
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:
xelatex hypothesis_report.tex
bibtex hypothesis_report
xelatex hypothesis_report.tex
xelatex hypothesis_report.texRequired 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:
Monitor Box Content Length: Each hypothesis box should fit comfortably on a single page. If content exceeds ~0.7 pages, it will likely overflow.
Use Strategic Page Breaks: Insert \newpage before boxes that contain substantial content:
\newpage
\begin{hypothesisbox1}[Hypothesis 1: Title]
% Long content here
\end{hypothesisbox1}Keep Main Text Boxes Concise: For the 4-page main text limit:
Break Long Content: If a hypothesis requires extensive explanation, split across main text and appendix:
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.
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.
Ensure all generated hypotheses meet these standards:
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 sourceshypothesis_generation.sty - LaTeX style package providing colored boxes, professional formatting, and custom environments for hypothesis reportshypothesis_report_template.tex - Complete LaTeX template with main text structure and comprehensive appendix sectionsFORMATTING_GUIDE.md - Quick reference guide with examples of all box types, color schemes, citation practices, and troubleshooting tipsWhen preparing hypothesis-driven research for publication, consult the venue-templates skill for writing style guidance:
venue_writing_styles.md - Master guide comparing styles across venuesreviewer_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
SKILL.md and 8 other files (scripts, references, assets) in src-tauri/science/skills/hypothesis-generation of spacering-net/codeg.
Open the folder on GitHubat commit 592131c
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.
Hypothesis Generation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Hypothesis Generation this skillspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Notes | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 46k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Hypothesis GenerationK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.9k | Automated safety check: Pass | MIT | |
| Good QuestionRimagination/good-question | 305 | 1 repos | ~4.3k | Automated safety check: Pass | MIT | |
| Claim-Driven Experiment PlannerzjYao36/Auto-Research-Refine | 128 | 7 repos | ~2.3k | Automated safety check: Notes | None | |
| High Stakes Analytics Decision Lablimingrui679-design/high-stakes-analytics-decision-lab | 989 | — | ~2.2k | Automated safety check: Pass | MIT |
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
K-Dense-AI/claude-scientific-writer
Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready…
Rimagination/good-question
A skill your agent uses when a researcher is choosing, framing, refining, or stress-testing a research question, hypothesis, thesis topic, project idea, grant direction, paper angle, or stalled…
zjYao36/Auto-Research-Refine
Turns a refined research proposal into a claim-to-evidence-to-run-order roadmap instead of a sprawling benchmark wishlist.
limingrui679-design/high-stakes-analytics-decision-lab
Build or review source-backed descriptive, diagnostic, predictive, and prescriptive analysis for consequential decisions.
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
spacering-net/codeg
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
spacering-net/codeg
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting.
spacering-net/codeg
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and…
spacering-net/codeg
Sample-size and statistical power calculations for planning studies.
spacering-net/codeg
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement.
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.