pre-registration
Create pre-registration documents for research transparency. Use when: (1) Before data collection, (2) Grant submissions, (3) Ensuring reproducibility, (4) Meeting open science standards, (5) Preventing HARKing.
When & Why to Use This Skill
The Pre-Registration Claude skill streamlines the creation of comprehensive research documentation to ensure scientific transparency and reproducibility. It helps researchers define hypotheses, study designs, and analysis plans before data collection, effectively preventing questionable research practices like HARKing and p-hacking while meeting rigorous open science standards for grant submissions and peer-reviewed publications.
Use Cases
- Grant Submissions: Preparing detailed methodology and analysis plans required by funding agencies to demonstrate research rigor and transparency.
- Open Science Compliance: Generating standardized documentation for platforms like OSF, AsPredicted, or ClinicalTrials.gov to meet journal transparency requirements.
- Experimental Design: Structuring complex study variables, sampling strategies, and power analyses to ensure a robust experimental framework before data collection begins.
- Preventing Bias: Formalizing primary and secondary hypotheses in advance to eliminate post-hoc theorizing (HARKing) and ensure the integrity of statistical results.
| name | pre-registration |
|---|---|
| description | "Create pre-registration documents for research transparency. Use when: (1) Before data collection, (2) Grant submissions, (3) Ensuring reproducibility, (4) Meeting open science standards, (5) Preventing HARKing." |
| allowed-tools | Read, Write |
| version | 1.0.0 |
Pre-Registration Skill
Purpose
Document study plans before data collection to ensure transparency and prevent questionable research practices.
Pre-Registration Components
1. Study Information
- Title, authors, institutions
- Funding sources
2. Hypotheses
- Primary hypothesis (pre-specified)
- Secondary hypotheses
- Exploratory questions
3. Design
- Study type (observational, experimental)
- Blinding (who, how)
- Randomization details
4. Sampling
- Sample size (with power analysis)
- Recruitment strategy
- Inclusion/exclusion criteria
- Stopping rules
5. Variables
- Independent variables (manipulation)
- Dependent variables (outcomes)
- Control variables
- Measurement instruments
6. Analysis Plan
- Primary statistical test
- Handling missing data
- Outlier treatment
- Multiple comparison corrections
7. Timeline
- Start date
- Data collection period
- Analysis completion
Platforms
- OSF (Open Science Framework): osf.io
- AsPredicted: aspredicted.org
- ClinicalTrials.gov: For clinical trials
Version: 1.0.0