什么是 Vibe Research？

Vibe Research 通过从人类执行任务转向 AI 引导执行，改变了传统探究的范式。虽然人类研究人员提供核心愿景、领域限制和最终验证，但智能体承担了研究流程的全部所有权。这包括识别知识盲点、扫描文献以及交叉引用来源，以建立对复杂主题的凝聚性理解。作为 Openclaw Skills 库的一部分，它使开发人员和研究人员能够在不失去关键监督的情况下，自动完成信息收集和分析的繁重工作。

Vibe Research 的核心价值在于其在定义框架内独立运行的能力。它不仅协助写作，还主动提出可测试的论点、设计方法论并记录其推理链以确保可重复性。通过将执行阶段委托给智能体，用户可以专注于战略决策和更高层次的综合。

下载入口:https://github.com/openclaw/skills/tree/main/skills/ivangdavila/vibe-research

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Vibe Research 应用场景

• 针对学术或技术论文进行深度文献综述。
• 识别现有文档或数据集中的矛盾和尚未充分探索的领域。
• 为新软件功能或科学探究生成并测试假设。
• 将海量源材料综合为具有完整引用的可行见解。

1. 人类提供研究问题、领域约束和成功标准。
2. 智能体识别知识盲点并扫描文献，以总结和交叉引用主题。
3. 智能体生成可测试的假设并提出具体的研究方法。
4. 智能体执行研究计划，收集数据并进行必要的实验。
5. 研究结果被综合成一份结构化报告，具有透明的推理和来源引用。
6. 人类验证方法论和最终产出，以确保技术准确性。

Vibe Research 配置指南

Vibe Research 技能旨在配置为运行 Openclaw Skills 的环境中工作。

# 通过智能体的命令行界面安装技能
clawdbot install vibe-research

安装完成后，您可以通过在工作区中创建一个 pipeline.md 文件来初始化研究周期，以跟踪智能体的进度。

Vibe Research 数据架构与分类体系

Vibe Research 将其数据组织在特定的 Markdown 文件和目录中，以确保 Openclaw Skills 框架内的透明度和可重复性。

name: Vibe Research
slug: vibe-research
version: 1.0.0
description: Conduct AI-led research with autonomous literature review, hypothesis generation, analysis, and synthesis while human provides vision.
metadata: {"clawdbot":{"emoji":"??","requires":{"bins":[]},"os":["linux","darwin","win32"]}}

When to Use

User has a research question or knowledge gap. Agent takes ownership of the full research cycle: scanning literature, generating hypotheses, running analyses, synthesizing findings. Human provides direction and oversight, AI executes.

Quick Reference

Core Concept

Traditional research: Human-led, human-executed Deep research: Human-led, AI-assisted
Vibe research: Human-directed, AI-led

The human sets the question and validates outputs. The agent handles literature synthesis, hypothesis generation, data analysis, and write-up autonomously.

Core Rules

1. Full-Cycle Ownership

Agent executes the complete pipeline:

1. Gap identification — What's unknown or contested?
2. Literature synthesis — Scan, summarize, cross-reference sources
3. Hypothesis generation — Propose testable claims
4. Analysis design — Define methodology
5. Execution — Run analyses, gather data
6. Synthesis — Write findings with citations

2. Vision from Human, Execution from Agent

• Human provides: research question, domain constraints, success criteria
• Agent handles: reading papers, connecting ideas, running experiments, drafting
• Human validates: key decisions, final outputs, methodology choices

3. Transparent Reasoning

• Cite every claim: source, page, quote
• Show reasoning chain for hypotheses
• Log all analytical steps for reproducibility
• Flag confidence levels (high/medium/low)

4. Proactive Gap Detection

Don't wait for instructions. When analyzing a topic:

• Identify contradictions in literature
• Spot under-explored areas
• Suggest follow-up experiments if results are ambiguous
• Pull additional sources when context is insufficient

5. Hallucination Prevention

• Only claim what sources support
• Distinguish: "Source X says..." vs "I infer..."
• When uncertain, say so explicitly
• Cross-verify critical facts across multiple sources

Vibe Research Traps

• Treating AI output as ground truth → always require human validation of key findings
• Skipping methodology transparency → document every step for reproducibility
• Overwhelming human with raw output → synthesize into actionable insights
• Losing the human's analytical skills → keep them engaged in critical thinking