run-research-pipeline

Name: run-research-pipeline
Availability: InStock
Author: Agentient

Orchestrate end-to-end research workflows from problem definition to consolidated findings.

2|1|Updated Jan 31, 2026

Search Tags:#research #multi-model #pipeline #synthesis #elicitation #mece

Ready to run

npx skills add https://github.com/Agentient/vibekit --skill run-research-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: run-research-pipeline
Source: https://github.com/Agentient/vibekit/tree/main/plugins/research-tools/skills/run-research-pipeline
Command: npx skills add https://github.com/Agentient/vibekit --skill run-research-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinates the full research lifecycle from problem definition to consolidated findings, reducing gaps and handoffs across stakeholders.

Core Features & Use Cases

Knowledge elicitation to define the research question and constraints
Design of a multi-LLM research strategy with MECE decomposition and model-optimized prompts
Execution phase coordinating prompts across Claude, Gemini, and GPT
Synthesis and reporting with consolidated outputs and quality gates
Real-world use: manage an end-to-end study from problem framing to final report

Quick Start

Provide a clear problem statement to initiate the research pipeline and generate the initial artifacts.

Frequently Asked Questions about run-research-pipeline

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

FAQPage Schema

How do I orchestrate an end-to-end multi-model research workflow?▼

You can orchestrate a multi-model research workflow by eliciting knowledge to define the problem, designing a MECE-decomposed strategy with model-optimized prompts, executing across multiple LLMs, and synthesizing consolidated findings with quality gates.

What is MECE decomposition in multi-LLM research design?▼

MECE decomposition in research design breaks the research question into mutually exclusive and collectively exhaustive sub-problems, ensuring structured prompt generation and comprehensive coverage across Claude, Gemini, and GPT models.

Can I coordinate prompts across Claude, Gemini, and GPT in a single research pipeline?▼

Yes, this research pipeline coordinates prompts across Claude, Gemini, and GPT during the execution phase, applying model-optimized prompts to gather diverse findings for final synthesis and consolidated reporting.

How do I start a research pipeline with knowledge elicitation?▼

To start the research pipeline, provide a clear problem statement to initiate knowledge elicitation, which defines the research question and constraints while generating the initial artifacts for the multi-phase study.

What are the limitations of using a structured multi-phase research pipeline?▼

Using a structured multi-phase research pipeline requires a clear problem statement to initiate and is designed for complex projects needing elicitation, design, execution, and synthesis, making it less suitable for simple, single-step queries.

Does the multi-model research pipeline include quality gates for synthesis?▼

Yes, the multi-model research pipeline includes quality gates during the synthesis and reporting phase, ensuring consolidated outputs meet structured requirements and comprehensive artifact standards.