An AI-powered researcher assistant that converts raw questionnaire items, scale dimensions, and response options into structured codebooks and scoring rules.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install questionnaire-codebook-maker-mia956
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install questionnaire-codebook-maker-mia956 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Questionnaire Codebook Maker?
The Questionnaire Codebook Maker is a specialized tool designed to bridge the gap between raw survey items and structured datasets ready for statistical analysis. Tailored for academic, clinical, and market researchers, this skill automates the tedious process of drafting research codebooks, generating consistent variable names, calculating reverse-scoring rules, and specifying data-entry parameters. Integrating this utility into your Openclaw Skills workflow ensures that raw survey structures are systematically parsed into clean, standardized outputs that are instantly ready for software like SPSS, R, Mplus, Excel, or Word.
By leveraging automated parsing logic, it maintains rigorous consistency in variable taxonomy (such as mapping dep_01 to scale dimensions), applies mathematical formulas for reverse-scoring, and recommends best-practice missing data handling policies. This eliminates human error in dataset schema preparation, making it an indispensable tool for research data management.
Questionnaire Codebook Maker Use Cases
- Convert messy questionnaire descriptions, scale documents, or Word tables into structured TSV or Markdown codebooks.
- Generate consistent, analysis-ready variable names that adhere to SPSS, R, or legacy Mplus constraints (under 8 or 20 characters).
- Identify and flag reverse-scored items, automatically generating appropriate transformation formulas based on scale ranges (e.g., 1-5, 1-7, 0-4).
- Standardize scale scoring rules (dimension sums or means) and draft transparent protocols for handling missing values.
- Verify the internal logical consistency of scale items, response options, and scoring definitions prior to data collection or entry.
How Questionnaire Codebook Maker Works
- Parse Input: The skill analyzes the provided questionnaire metadata, raw item text, dimensions, and scale boundaries.
- Apply Variable Taxonomy: It generates standardized, lowercase variable names (e.g., prefixing by dimension such as
anx_01,dep_02). - Calculate Scoring Logic: It mathematically derives reverse-scoring formulas (
reversed_score = min + max - original_score) and sets missing-value parameters. - Output Structuring: It constructs a complete, multi-column codebook table containing fields for variables, item IDs, dimensions, text, response ranges, and scoring notes.
- QA Verification: It performs a quality control check to detect duplicate item IDs, incomplete dimensions, or ambiguous scoring paths.
Questionnaire Codebook Maker Setup
Prerequisites
Make sure you have Python 3 or Python installed on your system.
Running the Python Helper Script
This skill features a lightweight Python script (make_codebook.py) to automate local conversions of CSV item lists to Markdown codebooks.
Prepare a source CSV file with the following columns: item_id,item_text,dimension,scale_min,scale_max,reverse.
Run the execution command using your preferred Python interpreter:
python3 scripts/make_codebook.py examples/demo_items.csv --out-dir output
If your local environment default is python, execute:
python scripts/make_codebook.py examples/demo_items.csv --out-dir output
This script executes locally using only Python standard libraries, ensuring secure and private data parsing as part of your Openclaw Skills workspace.
Questionnaire Codebook Maker Data Schema & Taxonomy
Input CSV Schema
When utilizing the local script, the input file should conform to this column structure:
| Column | Type | Description |
|---|---|---|
item_id |
String | Unique identifier (e.g., Q1, anx_01) |
item_text |
String | The exact questionnaire item wording |
dimension |
String | Associated scale or subscale dimension |
scale_min |
Integer | Minimum possible score on the response scale |
scale_max |
Integer | Maximum possible score on the response scale |
reverse |
Boolean/String | Flag indicating if item requires reverse scoring (true/false) |
Output Codebook Format
The output is generated as a TSV/Markdown table comprising these metadata columns:
variable: Standardized, safe variable name for SPSS/R/Mplus.item_id: Original item identifier.dimension: Subscale categorization.item_text: Original question text.response_range: Valid range of responses (e.g.,1-5).reverse_scored: Boolean/Yes/No flag.scoring_note: Formula for reverse scaling or index calculations.missing_rule: Suggested threshold for computing scale aggregates (e.g., the 80% non-missing rule).
Questionnaire Codebook Maker Advanced Features
- Mplus Compatibility Filter: Automatically shortens variable names to 8 characters or fewer upon request to satisfy strict legacy software limitations.
- Automated Reverse-Scoring Calculation: Computes the exact offset calculation formula for arbitrary scales (e.g., 1-5 scale maps to
6 - score, 0-10 scale maps to10 - score). - Intelligent Missing-Value Recommendation: Implements standard psychometric principles, automatically recommending dimension mean score calculations only if at least 80% of items are valid.
- Built-in Quality Control Checklist: Evaluates items for duplicate indices, inconsistent response ranges, scale/subscale classification gaps, and reverse-scoring ambiguities before producing final research assets.
SKILL.md
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