184 added, 217 removed. Audit A to A.
---
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
- required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
- metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+ description: Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.
+ license: MIT
+ compatibility: Python 3.11+ standard library. Bundled CLIs are deterministic and local-only; they accept bounded JSON, CSV, or Markdown and require no network, credentials, models, image services, or external packages.
+ metadata:
+ version: "2.0"
+ skill-author: K-Dense Inc.
+ last-reviewed: "2026-07-23"
---
# 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
+ Turn an observation into a transparent set of candidate explanations and tests. A hypothesis is a proposal to be challenged, not a finding, fact, diagnosis, or recommendation.
- ## Visual Enhancement with Scientific Schematics
+ ## Non-negotiable boundaries
- **⚠️ MANDATORY: Every hypothesis generation report MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**
+ Before using unpublished, sensitive, controlled, personal, proprietary, export-controlled, or security-relevant material:
- 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)
+ 1. Confirm authorization and the applicable institutional, funder, publisher, data-use, privacy, and AI policies.
+ 2. Keep the material local unless an authorized human explicitly approves a named external destination and data scope.
+ 3. Minimize inputs. Do not place sensitive or unpublished data in web searches or external AI systems without authorization.
+ 4. Stop at the appropriate human, animal, biosafety, dual-use, data-governance, or regulatory gate.
- **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
+ Never:
- **How to generate schematics:**
- ```bash
- python scripts/generate_schematic.py "your diagram description" -o figures/output.png
- ```
+ - present a hypothesis, mechanism, causal effect, citation, or apparent pattern as established evidence;
+ - claim novelty because a quick search found nothing;
+ - infer causation from association, temporal order alone, predictive accuracy, or model output;
+ - supply patient-specific diagnosis, treatment, dose, prognosis, or other clinical advice;
+ - provide harmful experimental optimization or operational detail for pathogens, toxins, weapons, evasion, or other misuse;
+ - bypass IRB/REC, IACUC, IBC, biosafety, dual-use, privacy, legal, or regulatory review;
+ - fabricate sources, identifiers, search coverage, data, results, approvals, or preregistration;
+ - automatically score, rank, select, accept, or reject scientific hypotheses.
- 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
+ If a request crosses a safety gate, produce only a high-level risk/oversight note and route it to the qualified local authority. Do not continue with operational detail.
- **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
+ ## Keep the objects distinct
- For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
+ | Object | Meaning |
+ |---|---|
+ | **Observation** | What was measured, noticed, or reported, with provenance and uncertainty |
+ | **Research question** | The answerable question that defines scope |
+ | **Hypothesis** | A candidate explanatory or relational proposition |
+ | **Mechanism** | The proposed process connecting conditions to an outcome |
+ | **Causal estimand** | The precisely defined causal contrast to estimate |
+ | **Prediction** | An observable implication derived before checking the target result |
+ | **Alternative explanation** | A rival account, including bias or non-causal explanations |
+ | **Null hypothesis** | A specified no-effect/no-difference model used by an analysis |
+ | **Negative control** | A control expected not to operate through the proposed mechanism |
+ | **Operationalization** | How a construct becomes a variable, measurement, intervention, or category |
+ | **Analysis plan** | Prespecified transformations, models, contrasts, uncertainty, and decision rules |
+ | **Evidence** | Observations or sources that bear on a claim; never the claim itself |
- ---
+ Do not collapse these labels. A mechanistic story is not a prediction; a prediction is not evidence; rejection of one null does not prove a mechanism; support for one candidate does not eliminate unconsidered rivals.
## 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)
-
- ### 2. Conduct Comprehensive Literature Search
+ ### 1. Run the scope and safety gate
- Search existing scientific literature to ground hypotheses in current evidence. Use both PubMed (for biomedical topics) and general web search (for broader scientific domains):
+ Record:
- **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
+ - accountable human owner and intended use;
+ - data sensitivity, authorization, retention, and permitted processing;
+ - affected people, animals, ecosystems, communities, or security interests;
+ - required ethics, feasibility, biosafety, dual-use, and regulatory reviews;
+ - unresolved blocks and domain expertise needed.
- **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
+ No script approval is an ethics, safety, regulatory, or scientific approval.
- **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
+ ### 2. Freeze the observation
- ### 3. Synthesize Existing Evidence
+ Write the observation before interpretation:
- Analyze and integrate findings from literature search:
+ - measurement or source;
+ - population, system, place, and time;
+ - unit of observation and unit of analysis;
+ - uncertainty, missingness, exclusions, and preprocessing;
+ - whether the pattern was expected, exploratory, or selected after viewing results.
- - 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
+ Use “reported,” “observed,” or “associated,” not causal language, unless a causal design and estimand justify it.
- ### 4. Generate Competing Hypotheses
+ ### 3. Frame the research question
- Develop 3-5 distinct hypotheses that could explain the phenomenon. Each hypothesis should:
+ Choose a framework only when it fits:
- - 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.)
+ - **PICO/PICOT** for intervention/effectiveness questions: population, intervention, comparator, outcome, and optionally time.
+ - **PECO** for exposure questions.
+ - **Population–index test–reference standard–target condition** for diagnostic accuracy.
+ - **Population–prognostic factor–outcome–time** for prognosis.
+ - A domain-specific construct–context–outcome frame for qualitative, descriptive, mechanistic, or theoretical work.
- **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
+ PICO is not a universal template. Define stakeholders, context, boundaries, feasibility, and what answer would change knowledge or practice. FINER is a question-refinement mnemonic—Feasible, Interesting, Novel, Ethical, Relevant—not a scoring system. Treat “Novel” as unresolved until a documented, fit-for-purpose search and expert review support it.
- ### 5. Evaluate Hypothesis Quality
+ ### 4. Establish a dated evidence boundary
- Assess each hypothesis against established quality criteria from `references/hypothesis_quality_criteria.md`:
+ Search before making literature-dependent statements. Prefer primary research, official policies, primary methods papers, current reporting guidelines, and systematic reviews used for orientation.
- **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?
+ Record:
- Explicitly note the strengths and weaknesses of each hypothesis.
+ - search date and cutoff;
+ - databases/indexes, queries, filters, and screening boundary;
+ - included and excluded source types;
+ - sources supporting, challenging, or contextualizing each claim;
+ - known access, language, database, and time limitations.
- ### 6. Design Experimental Tests
+ A search can establish what was searched, not universal absence. Say “not located within the documented search boundary,” never “no prior work exists.” Use `assets/search_boundary_template.json`, `assets/evidence_ledger_template.csv`, and `references/literature_search_strategies.md`.
- For each viable hypothesis, propose specific experiments or studies to test it. Consult `references/experimental_design_patterns.md` for common approaches:
+ ### 5. Generate rivals before choosing tests
- **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?
+ Create multiple candidates from genuinely different explanatory classes when plausible:
- **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
+ - proposed mechanism;
+ - measurement or processing artifact;
+ - confounding or common cause;
+ - selection or attrition;
+ - conditioning on a collider;
+ - reverse causation;
+ - temporal, contextual, or boundary-condition differences;
+ - stochastic variation;
+ - competing mechanisms at another scale.
- ### 7. Formulate Testable Predictions
+ Generate an initial rival set independently before AI-assisted expansion to reduce anchoring and homogenization. Do not force a fixed number or false symmetry. Keep every candidate labeled `candidate`.
- For each hypothesis, generate specific, quantitative predictions:
+ Platt’s strong-inference pattern motivates alternative hypotheses and crucial tests, but failed alternatives do not make the survivor true. Unknown alternatives, auxiliary assumptions, measurement error, and mixed mechanisms remain possible.
- - 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
+ ### 6. Declare the claim type and estimand
- ### 8. Present Structured Output
+ Classify each target as:
- 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.
+ - descriptive;
+ - associational;
+ - predictive;
+ - causal;
+ - mechanistic.
- **Document Structure:**
+ For a causal target, define before analysis:
- **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)
+ - target population or system;
+ - intervention/exposure and comparator;
+ - outcome and time horizon;
+ - population-level summary;
+ - treatment versions and intercurrent-event handling where relevant;
+ - identification assumptions and target-trial/design analogue.
- Keep main text highly concise - only the most essential information. All details go to appendices.
+ Document confounding, selection, collider, measurement, and reverse-causation risks separately. An observational causal estimate remains assumption-dependent. Use `references/causal_inference_and_claims.md`.
- **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
+ ### 7. Derive discriminating predictions
- **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
+ For every candidate:
- **Colored Box Usage:**
+ 1. State conditions and boundary conditions.
+ 2. Name the observable and measurement.
+ 3. State the expected pattern and uncertainty.
+ 4. State a result incompatible with the candidate under declared assumptions.
+ 5. Contrast the expected result with at least one rival.
+ 6. Define indeterminate outcomes and what would be learned from them.
- Use the custom box environments from `hypothesis_generation.sty`:
+ Prefer tests where rivals predict meaningfully different outcomes. Add positive, procedural, and negative controls when scientifically appropriate. A negative control must be incapable of operating through the target mechanism while sharing relevant bias pathways; it is not a decorative untreated group.
- - `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)
+ Use `assets/prediction_rival_matrix_template.csv` and `assets/falsification_controls_template.json`.
- **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
+ ### 8. Operationalize and validate measurement
- All detailed explanations, additional evidence, and comprehensive discussions belong in the appendices.
+ For every construct record:
- **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
+ - variable role and operational definition;
+ - population/system, unit, timing, and conditions;
+ - instrument/method, calibration, quality control, and masking;
+ - reliability/repeatability;
+ - validity evidence and applicability;
+ - missingness, detection limits, transformations, cut points, and their rationales;
+ - measurement invariance or cross-group comparability when relevant;
+ - foreseeable measurement bias and limitations.
- **Citation Requirements:**
+ Do not treat a convenient proxy as the construct itself. Validate with:
- 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
+ ```bash
+ python3 scripts/check_operationalization.py local-operationalization.json
+ ```
- 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.
+ ### 9. Match design and analysis to the claim
- **LaTeX Compilation:**
+ Specify:
- The template requires XeLaTeX or LuaLaTeX for proper rendering:
+ - sampling, experimental unit, allocation, randomization, masking, and controls;
+ - inclusion/exclusion and stopping rules;
+ - sample-size, precision, or information rationale based on declared assumptions;
+ - outcomes, contrasts, estimands, models, effect measures, and uncertainty;
+ - missing-data and intercurrent-event handling;
+ - multiplicity across outcomes, models, subgroups, looks, and hypotheses;
+ - assumptions, diagnostics, robustness, and sensitivity analyses;
+ - replication or independent validation plan;
+ - what is confirmatory versus exploratory.
- ```bash
- xelatex hypothesis_report.tex
- bibtex hypothesis_report
- xelatex hypothesis_report.tex
- xelatex hypothesis_report.tex
- ```
+ Do not use universal sample-size minima. Do not interpret a thresholded p-value as the probability a hypothesis is true or as effect importance. See `references/experimental_design_patterns.md`.
- **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.
+ For intervention trials, use the current SPIRIT 2025 protocol guidance and CONSORT 2025 reporting guidance where applicable. These improve completeness; they do not certify design quality, ethics, or regulatory compliance.
- **Page Overflow Prevention:**
+ ### 10. Prevent HARKing and expose deviations
- To prevent content from overflowing on pages, follow these critical guidelines:
+ Before accessing the target outcomes, timestamp the question, candidates, predictions, outcomes, exclusions, transformations, analysis, multiplicity, missing-data plan, and stopping rule when feasible.
- 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.
+ Afterward:
- 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}
- ```
+ - label data-dependent ideas and analyses exploratory;
+ - preserve and report planned analyses;
+ - list deviations with date, rationale, who decided, and expected impact;
+ - never rewrite an observed pattern as an a priori prediction.
- 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
+ Preregistration is a transparent plan, not a ban on adaptation. Registered Reports add results-blind peer review and in-principle acceptance under journal policy. See `references/preregistration_and_open_science.md`.
- 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
+ ### 11. Plan replication and updating
- 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.
+ Distinguish:
- 6. **Appendix Page Management:** In appendices, use `\newpage` between major sections to avoid overflow in detailed content areas.
+ - **reproducibility:** consistent computational results from the same data/code/conditions;
+ - **replicability:** consistency across studies collecting new data for the same question.
- **Quick Reference:** See `assets/FORMATTING_GUIDE.md` for detailed examples of all box types, color schemes, and common formatting patterns.
+ Preserve provenance, versions, code, materials, and decision logs when sharing is authorized. Plan independent replication or transport tests across relevant boundaries. Update candidate status when contrary, null, or replication evidence arrives; do not hide negative results.
- ## Quality Standards
+ ### 12. Apply human accountability
- Ensure all generated hypotheses meet these standards:
+ The accountable human must verify:
- - **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
+ - every citation and source-to-claim link;
+ - domain plausibility and measurement validity;
+ - causal assumptions and statistical design;
+ - ethics, feasibility, safety, privacy, and regulatory status;
+ - all AI-assisted text, ideas, and citations;
+ - whether broader expertise or community input is required.
- ## Resources
+ AI can confabulate citations, anchor reasoning, and homogenize candidate sets. Record permitted AI use and material influence. Keep independent human ideation and rival generation in the process.
- ### references/
+ ## Local tool index
- - `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
+ All CLIs are bounded, dependency-free, local, deterministic, and non-scoring:
- ### assets/
+ | Task | Asset | Command |
+ |---|---|---|
+ | Hypothesis-record schema | `assets/hypothesis_record_template.json` | `python3 scripts/validate_hypothesis_schema.py record.json` |
+ | Measurement checklist | `assets/operationalization_template.json` | `python3 scripts/check_operationalization.py checklist.json` |
+ | Prediction/rival matrix | `assets/prediction_rival_matrix_template.csv` | `python3 scripts/validate_prediction_matrix.py matrix.csv` |
+ | Claim-language lint | Annotated Markdown | `python3 scripts/lint_causal_claims.py draft.md` |
+ | Falsification/controls | `assets/falsification_controls_template.json` | `python3 scripts/check_falsification_controls.py controls.json` |
+ | Evidence/source audit | `assets/evidence_ledger_template.csv` + `assets/search_boundary_template.json` | `python3 scripts/audit_evidence_ledger.py ledger.csv boundary.json` |
+ | Preregistration scaffold | `assets/preregistration_scaffold_template.md` | `python3 scripts/generate_preregistration_scaffold.py record.json -o preregistration.md` |
- - `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
+ Exit codes are `0` for structurally valid output, `1` for completed validation with errors, and `2` for malformed/unsafe input. Reports validate declarations and internal consistency only; they do not verify scientific truth or choose a hypothesis. Full schemas are in `references/tool_reference.md`.
- ### Related Skills
+ ## References
- 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
+ - `references/concepts_and_workflow.md` — object model, strong inference, uncertainty, and candidate lifecycle
+ - `references/hypothesis_quality_criteria.md` — non-scoring human review criteria
+ - `references/literature_search_strategies.md` — traceable, bounded evidence search
+ - `references/causal_inference_and_claims.md` — estimands and causal-bias risks
+ - `references/experimental_design_patterns.md` — design, controls, measurement, multiplicity, and replication
+ - `references/preregistration_and_open_science.md` — preregistration, Registered Reports, deviations, and open science
+ - `references/ethics_safety_and_ai.md` — oversight gates, dual use, data handling, and responsible AI
+ - `references/tool_reference.md` — CLI schemas, limits, and examples
+ - `references/source_ledger.md` — dated authoritative source notes
+ - `references/security_validation.md` — baseline findings and validation record
+ The bundled source ledger is `assets/source_ledger.csv`, verified through **2026-07-23**. Recheck time-sensitive policy and guidance before a later or jurisdiction-specific use.