full-pipeline

Orchestrate research pipelines from idea discovery to report generation.

100|24|Updated Mar 31, 2026

Search Tags:#report generation #research automation #research pipeline #experiment deployment #AI-assisted research

Ready to run

npx skills add https://github.com/GRIND-Lab-Core/night_owl_research_agent --skill full-pipeline-grind-lab-core

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: full-pipeline
Source: https://github.com/GRIND-Lab-Core/night_owl_research_agent/tree/main/skills/full-pipeline
Command: npx skills add https://github.com/GRIND-Lab-Core/night_owl_research_agent --skill full-pipeline-grind-lab-core

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the complex and time-consuming process of conducting research by automating the stages of idea discovery, experiment deployment, autonomous review, and report generation.

Core Features & Use Cases

Idea Discovery: Identifies novel research directions and generates experiment plans.
Experiment Deployment: Executes experiments and collects results.
Autonomous Review Loop: Iteratively improves work quality through adversarial review.
Report Writing: Consolidates all pipeline artifacts into a comprehensive narrative.
Use Case: A geoscientist uses this Skill to automate the research process, from identifying a research topic to generating a final report, saving significant time and effort.

Quick Start

Run the full-pipeline Skill by providing a research idea description, such as: /full-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true, difficulty: nightmare.

Frequently Asked Questions about full-pipeline

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

FAQPage Schema

How do I automate the research pipeline from idea discovery to report generation?▼

You can automate a research pipeline using a 4-stage workflow that handles idea discovery, experiment deployment, autonomous review, and report generation. It identifies research directions, executes experiments, iterates via adversarial review, and consolidates artifacts into a final narrative.

What is an autonomous review loop in AI-assisted research?▼

An autonomous review loop in AI-assisted research is an iterative, adversarial review mechanism that automatically evaluates and improves work quality. It repeatedly checks pipeline artifacts to enhance output without manual intervention at every step.

How do I start a full research pipeline using a topic description?▼

To start a research pipeline, provide a research idea description or topic. You can configure execution with parameters like AUTO_PROCEED and human checkpoints to control whether the pipeline runs continuously or pauses for manual validation between stages.

Can I use this research automation workflow for industrial research?▼

Yes, this research automation workflow applies to both academic and industrial research contexts. It orchestrates experiment deployment and report generation for any workflow requiring access to research context files and computational resources.

Do I need computational resources to run experiment deployment in a research pipeline?▼

Yes, you need computational resources to execute experiment deployment and collect results within the research pipeline. The orchestration requires access to these resources alongside your research context files to successfully run the automated stages.