Hypothesis Generation

Source: https://github.com/aipoch/medical-research-skills

Hypothesis Generation (Scientific)

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Structured scientific hypothesis formulation from observations; use when you have experimental observations or preliminary data and need testable hypotheses with predictions, mechanisms, and validation experiments.
• Documentation-first workflow with no packaged script requirement.
• Reference material available in references/ for task-specific guidance.
• Reusable packaged asset(s), including assets/FORMATTING_GUIDE.md.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

Skill directory: 20260316/scientific-skills/Protocol Design/hypothesis-generation
No packaged executable script was detected.
Use the documented workflow in SKILL.md together with the references/assets in this folder.

Example run plan:

1. Read the skill instructions and collect the required inputs.
2. Follow the documented workflow exactly.
3. Use packaged references/assets from this folder when the task needs templates or rules.
4. Return a structured result tied to the requested deliverable.

Implementation Details

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: instruction-only workflow in SKILL.md.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Packaged assets: reusable files are available under assets/.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

1. When to Use

Use this skill when you need to turn observations into testable, mechanistic hypotheses and a validation plan, for example:

• You have experimental observations (e.g., an unexpected phenotype, trend, or anomaly) and need 3-5 competing explanations with clear mechanisms.
• You have preliminary data and must propose testable predictions and decisive experiments to discriminate between hypotheses.
• You are preparing a mechanistic study plan (molecular/cellular/system/population) and need a structured framework for causal reasoning.
• You are doing literature-grounded hypothesis development and want to identify gaps, contradictions, and plausible mechanisms.
• You need a publication-ready hypothesis report (LaTeX) with concise main claims and a detailed appendix.

2. Key Features

• Scientific workflow: observation framing → literature search → evidence synthesis → competing hypotheses → quality evaluation → experiments → predictions → structured report.
• Competing hypotheses (3-5): distinct, mechanistic explanations at appropriate biological/physical scales.
• Quality criteria: testability, falsifiability, parsimony, explanatory power, scope, consistency, novelty (see references/hypothesis_quality_criteria.md).
• Experiment design patterns: lab, observational, clinical, computational; controls, confounders, and measurement plans (see references/experimental_design_patterns.md).
• Prediction-first outputs: quantitative/conditional predictions that differentiate hypotheses and specify falsifiers.
• Report packaging: LaTeX template with colored boxes and a strict main-body length budget (see assets/hypothesis_report_template.tex, assets/hypothesis_generation.sty, assets/FORMATTING_GUIDE.md).
• Mandatory visuals: every hypothesis report must include at least 1-2 AI-generated schematics created via the scientific-schematics skill.

3. Dependencies

• LaTeX engine: XeLaTeX or LuaLaTeX
• BibTeX: for reference compilation
• Required LaTeX packages (used by assets/hypothesis_generation.sty):
  • tcolorbox, xcolor, fontspec, fancyhdr, titlesec, enumitem, booktabs, natbib
• Python (optional, for schematic generation script): Python 3.10+ recommended
• Related skill dependency (mandatory for reports): scientific-schematics (for 1-2+ diagrams per report)

4. Example Usage

A) Generate required schematics (at least 1-2)

python scripts/generate_schematic.py "Diagram showing 3 competing mechanistic hypotheses linking Observation X to Outcome Y, with key intermediates and predicted readouts." -o figures/hypothesis_framework.png

python scripts/generate_schematic.py "Experimental design flowchart comparing interventions A/B and controls, with primary/secondary endpoints and decision points." -o figures/experimental_design.png

B) Create a LaTeX report using the provided template

1. Copy the template assets into a working directory:

mkdir -p hypothesis_report figures
cp assets/hypothesis_report_template.tex hypothesis_report/hypothesis_report.tex
cp assets/hypothesis_generation.sty hypothesis_report/

2. Edit hypothesis_report/hypothesis_report.tex to include:

• Executive summary
• 3-5 hypothesis boxes (each on a fresh page)
• Predictions and critical comparisons
• Appendix A-D with detailed literature, protocols, and evaluations
• References (BibTeX)

3. Compile:

cd hypothesis_report
xelatex hypothesis_report.tex
bibtex hypothesis_report
xelatex hypothesis_report.tex
xelatex hypothesis_report.tex

C) Minimal LaTeX snippet demonstrating the required structure

\documentclass{article}
\usepackage{hypothesis_generation}
\usepackage{natbib}

\begin{document}

\begin{summarybox}
\textbf{Executive Summary.} Observation X shows pattern Y under condition Z. We propose 3 competing mechanisms and outline decisive experiments and predictions.
\end{summarybox}

\newpage
\begin{hypothesisbox1}[Hypothesis 1: Mechanism A]
\textbf{Mechanistic explanation.} Brief causal chain describing how A produces Y under Z.

\textbf{Key supporting evidence.}
\begin{itemize}
  \item Evidence point 1 \citep{author2023}.
  \item Evidence point 2 \citep{author2021}.
\end{itemize}

\textbf{Core assumptions.}
\begin{itemize}
  \item Assumption 1.
\end{itemize}
\end{hypothesisbox1}

\newpage
\begin{hypothesisbox2}[Hypothesis 2: Mechanism B]
% Keep concise; move details to Appendix.
\end{hypothesisbox2}

\begin{predictionbox}
\textbf{Testable predictions.}
\begin{itemize}
  \item If Hypothesis 1 is correct, intervention I increases readout R by ~20-40\% under Z.
  \item If Hypothesis 2 is correct, R does not change, but marker M shifts directionally.
\end{itemize}
\end{predictionbox}

\begin{comparisonbox}
\textbf{Critical comparisons.} Prioritize experiments that maximally separate predictions across hypotheses.
\end{comparisonbox}

\end{document}

5. Implementation Details

5.1 End-to-end workflow (recommended)

1. Define the phenomenon
   • State the observation/pattern to explain, scope, constraints, and what is known vs unknown.
2. Literature search
   • Use domain-appropriate sources (e.g., PubMed for biomedical topics; general scholarly search otherwise).
   • Apply strategies in references/literature_search_strategies.md.
3. Evidence synthesis
   • Summarize consensus mechanisms, contradictions, and gaps; extract candidate causal links.
4. Generate 3-5 competing hypotheses
   • Each must be mechanistic (how/why), distinct, and grounded in evidence or plausible analogies.
5. Evaluate hypothesis quality
   • Use criteria in references/hypothesis_quality_criteria.md:
     • Testability, falsifiability, parsimony, explanatory power, scope, consistency, novelty.
   • Record strengths/weaknesses explicitly.
6. Design experimental tests
   • Use patterns in references/experimental_design_patterns.md.
   • Specify: measurements, controls, comparisons, confounders, sample size/statistics (as appropriate).
7. Formulate testable predictions
   • Provide discriminative predictions (direction, magnitude when possible), boundary conditions, and falsifiers.
8. Produce structured report
   • Use assets/hypothesis_report_template.tex and assets/hypothesis_generation.sty.
   • Include 1-2+ schematics generated via scientific-schematics.

5.2 Mandatory schematic requirement

• Every hypothesis generation report must include at least 1-2 diagrams (framework, mechanism, experimental flowchart, decision tree, causal graph).
• Reports without visuals are considered incomplete.
• Recommended placement: one schematic in the main body (overview), additional schematics in the appendix (mechanisms/experimental details).