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research-workflow

Orchestrate mode-aware, evidence-driven AI R&D workflows from intake to completion.

51|4|Updated Feb 27, 2026
Search Tags:#automation#workflow#planning#research#memory management#experiment execution
One-click install
npx skills add https://github.com/TenureAI/PhD-Zero --skill research-workflow-tenureai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-workflow
Source: https://github.com/TenureAI/PhD-Zero/tree/main/.agents/skills/research-workflow
Command: npx skills add https://github.com/TenureAI/PhD-Zero --skill research-workflow-tenureai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines complex AI research and development tasks by providing a structured, evidence-driven workflow that ensures tasks are completed efficiently and reliably.

Core Features & Use Cases

  • Mode-Aware Execution: Adapts to different interaction modes (full-auto, moderate, detailed) for flexible control.
  • Evidence-Based Iteration: Ensures all conclusions are backed by reproducible data and metrics.
  • Integrated Tools: Seamlessly integrates with other skills like run-governor, research-plan, and memory-manager for a complete R&D cycle.
  • Use Case: When tasked with reproducing a research paper's results, this Skill will plan the experiments, execute the code, manage memory, and report findings, adapting its interaction level based on the run-governor setting.

Quick Start

Use the research workflow skill to analyze the provided code and identify potential bugs.

Frequently Asked Questions about research-workflow

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

FAQPage Schema
How do I automate my AI research workflow from paper analysis to experiment execution?▼

To automate your AI research workflow, this Skill orchestrates a structured, evidence-driven R&D cycle from intake to completion, handling code analysis, debugging, reproduction, and iterative delivery.

How does evidence-driven iteration work when reproducing research paper results?▼

Evidence-driven iteration ensures all research conclusions are backed by reproducible data and metrics, automatically planning experiments, executing code, and reporting findings throughout the reproduction process.

Can I adjust the level of automation control during a complex research and development task?▼

You can adjust automation control using mode-aware execution, which adapts to different interaction modes including full-auto, moderate, and detailed, providing flexible control over the R&D workflow.

What is the best way to manage memory and planning for iterative AI experiment delivery?▼

The best way to manage memory and planning is through integrated tools like memory-manager and research-plan, which handle structured phases, stage reporting, and replanning control for iterative delivery.

Does this AI R&D workflow automation integrate with run-governor for structured stage reporting?▼

Yes, this workflow automation integrates with run-governor, research-plan, memory-manager, deep-research, human-checkpoint, and experiment-execution to provide structured phases, stage reporting, and replanning control.

When should I not use an automated R&D workflow for code analysis and debugging?▼

You should avoid automated R&D workflows for tasks lacking reproducible data or clear metrics, as evidence-driven iteration requires structured phases and measurable outcomes to function effectively.

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