An automated qualitative analysis tool that encodes open-ended survey responses into structured themes, sentiments, and tags while generating a verifiable codebook.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install survey-response-coder
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 survey-response-coder 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 Survey Response Coder?
The Survey Response Coder is a specialized Openclaw Skills module designed for researchers and data analysts who need to process large volumes of qualitative text. It automates the tedious process of thematic coding, converting raw text from CSV files or individual responses into structured data insights. This skill excels at identifying underlying sentiments and labeling complex human feedback with consistent tags.
Beyond simple categorization, it emphasizes transparency and academic rigor by producing a verifiable manual or codebook. This ensures that the AI's logic is auditable, allowing users to review how specific themes were derived and maintain high standards for qualitative workflows without sacrificing the speed of automation within Openclaw Skills.
Survey Response Coder Use Cases
- Thematic coding of open-ended survey questions for market research.
- Sentiment analysis of customer feedback and product reviews.
- Generating structured codebooks for academic qualitative research.
- Processing CSV-based text data into categorical summaries for business intelligence.
How Survey Response Coder Works
- The skill reorganizes user-provided raw data into a structured task specification to ensure analysis consistency.
- It scans the input for missing information and explicitly lists items requiring user confirmation rather than making assumptions.
- The system generates a preliminary draft for review, following the definitions in the local spec.json file.
- If the environment supports execution, it runs a Python-based processing script to transform the data.
- The final output is produced in a structured format including sample overviews, theme distribution, and coding rules.
Survey Response Coder Setup
To use this skill within your Openclaw Skills environment, ensure you have Python 3 installed. You can trigger the processing script directly via the CLI:
python3 "{baseDir}/scripts/run.py" --input <input_file> --output <output_file>
Ensure that the {baseDir}/resources/spec.json and {baseDir}/resources/template.md files are present in your local directory to guide the AI's structural output.
Survey Response Coder Data Schema & Taxonomy
The Survey Response Coder organizes data according to a strict taxonomy to ensure auditability. The primary files involved include:
| File Path | Purpose |
|---|---|
{baseDir}/resources/spec.json |
Defines the technical specifications and coding logic. |
{baseDir}/resources/template.md |
The Markdown blueprint for the final qualitative report. |
{baseDir}/examples/ |
Contains reference input and output pairs for few-shot learning. |
{baseDir}/tests/smoke-test.md |
A validation suite to ensure the skill is functioning correctly. |
Survey Response Coder Advanced Features
- Multi-format output: Generates both executive summaries and detailed executable lists for downstream processing.
- Verifiable Codebooks: Automatically creates documentation that explains the reasoning behind each assigned tag.
- Privacy-First Design: Includes built-in safeguards to prevent the exposure of respondent privacy and discourages over-generalization from small sample sizes.
- Shell/Exec Integration: Supports automated workflows using local Python scripts for high-performance data transformation within Openclaw Skills.
SKILL.md
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