research-units-pipeline-skills

🥈Silver

Research pipelines as semantic execution units: each skill declares inputs/outputs, acceptance criteria, and guardrails. Evidence-first methodology prevents hollow writing through structured intermediate artifacts.

148170Updated 1w ago

Intermediate30min to implementautomation

Saves ~150 min per use

Quick InstallView Source

git clone https://github.com/WILLOSCAR/research-units-pipeline-skills.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/WILLOSCAR/research-units-pipeline-skills

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Act as a research pipeline builder. Create a structured research pipeline for [COMPANY] in the [INDUSTRY] sector. The pipeline should include the following skills: data collection, data cleaning, data analysis, and data visualization. Each skill should declare its inputs, outputs, acceptance criteria, and guardrails. Use [DATA] as the initial dataset for this pipeline.

Example Output

# Research Pipeline for [COMPANY] in the [INDUSTRY] Sector

## Skill 1: Data Collection
- **Inputs**: Research objectives, data sources
- **Outputs**: Raw data
- **Acceptance Criteria**: Data is collected from all specified sources
- **Guardrails**: Ensure data is collected ethically and legally

## Skill 2: Data Cleaning
- **Inputs**: Raw data
- **Outputs**: Cleaned data
- **Acceptance Criteria**: Data is free of errors and inconsistencies
- **Guardrails**: Ensure data cleaning methods do not introduce bias

## Skill 3: Data Analysis
- **Inputs**: Cleaned data
- **Outputs**: Analyzed data
- **Acceptance Criteria**: Data is analyzed using appropriate statistical methods
- **Guardrails**: Ensure analysis is reproducible and transparent

## Skill 4: Data Visualization
- **Inputs**: Analyzed data
- **Outputs**: Visualizations
- **Acceptance Criteria**: Visualizations effectively communicate insights
- **Guardrails**: Ensure visualizations are accessible and inclusive