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crisp-cli

Execute qualitative mixed-data workflows with CRISP-T CLI tools.

12|Updated May 15, 2025
Search Tags:#cli#visualization#nlp#data-analysis#qualitative#crisp-cli#mixed-data
One-click install
npx skills add https://github.com/dermatologist/crisp-t --skill crisp-cli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: crisp-cli
Source: https://github.com/dermatologist/crisp-t/tree/main/.agents/skills/crisp-cli
Command: npx skills add https://github.com/dermatologist/crisp-t --skill crisp-cli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

CRISP-T CLI enables agents to perform qualitative mixed-data research using structured command-line workflows, reducing manual setup and boosting reproducibility.

Core Features & Use Cases

  • Three core tools: crisp for data import/NLP/ML/visualization, crispt for corpus management and relationships, and crispviz for publication-ready visualizations.
  • Use cases include academic research, market insights, and policy analysis, all enabling linking of text findings to numeric outcomes and iterative workflow design.
  • It supports stepwise experiments with intermediate saves, clear linkage configuration, and visual validation.

Quick Start

Import data with crisp, run NLP and ML analyses, and generate visualizations.

Frequently Asked Questions about crisp-cli

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run qualitative mixed-data analysis combining text and numeric outcomes?▼

Qualitative mixed-data analysis combines text processing and numeric outcomes using stepwise CLI workflows. You import data, run NLP and ML analyses, and generate visualizations while enforcing intermediate savepoints and explicit outcome specification for reproducibility.

What's the best way to link unstructured text findings to numeric data for ML tasks?▼

Linking unstructured text to numeric data requires explicit outcome specification and proper column handling. Stepwise workflows manage this linkage by processing text columns through NLP, then connecting those corpus relationships to numeric outcomes for ML analysis.

Can I generate publication-ready visualizations from topic modeling and sentiment analysis?▼

Publication-ready visualizations are generated from topic modeling and sentiment analysis using dedicated visualization tools. These tools create outputs suitable for academic research, market insights, and policy analysis directly from the analyzed mixed-data corpus.

Does this qualitative data analysis workflow support intermediate savepoints during experiments?▼

Qualitative data analysis workflows support intermediate savepoints during iterative experiments. Stepwise workflows enforce these savepoints to enable visual validation and clear linkage configuration before proceeding to downstream ML or visualization tasks.

When do I need to specify explicit outcomes for text and numeric data linking?▼

Explicit outcome specification is needed whenever linking text findings to numeric outcomes in ML tasks. Stepwise workflows require this configuration upfront to properly handle unstructured columns and ensure valid mixed-data analysis results.

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