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sdt328606

Deep Research

by sdT328606 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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/install deep-research-framework
Description
Structured deep research framework with explicit feedback loops and mental model evolution. Inspired by VeriTrace and Agentic System Scaling papers.
README (SKILL.md)

Deep Research Framework 🔬

用结构化方法做深度研究，而不是"搜一堆链接然后总结"。

核心理念

研究不只是信息收集，是心智模型的持续演化。

三个调节循环（来自 VeriTrace）：

  1. 解释性更新（Interpretive Update）— 新信息如何改变我对问题的理解？
  2. 偏差反馈（Deviation Feedback）— 我的假设和实际发现之间有多大偏差？
  3. 图式修订（Schema Revision）— 我的整体认知框架需要重构吗？

研究流程

Phase 1: 定义研究空间

## 研究问题
- 核心问题：[一句话]
- 子问题：[3-5 个分解]
- 已知：[我已经知道什么]
- 假设：[我预期会发现什么]
- 边界：[不研究什么]

Phase 2: 扫描与收集

广度优先，然后深度优先。

  1. 广度扫描（5-8 个方向并行搜索）

    • 每个方向 3-5 条结果
    • 快速过滤：相关性评分 1-5
    • 低于 3 分的直接跳过

  2. 深度挖掘（对高分方向深入）

    • 阅读原文/论文摘要
    • 追踪引用和相关工作
    • 寻找实际代码/工具

搜索策略：

  • 学术：arxiv.org, paperswithcode.com
  • 工具：github.com/trending, producthunt.com
  • 新闻：the-decoder.com, simonwillison.net, techcrunch.com
  • 社区：reddit r/MachineLearning, HackerNews

Phase 3: 心智模型演化

每收集一批信息，执行演化检查点：

## 演化检查点 [时间]

### 解释性更新
- 新信息如何改变我的理解？[具体说明]
- 哪些假设被证实？哪些被推翻？

### 偏差反馈
- 预期 vs 实际发现：[对比]
- 偏差原因分析：[为什么我之前会那样想？]

### 图式修订
- 整体认知框架是否需要调整？[是/否]
- 如果是：旧框架 → 新框架的变化

Phase 4: 评估与分级

对每个发现用 RAPID 框架评估：

维度 问题 评分 (1-5)
Relevance 对我的核心问题有多相关？
Actionability 我能立刻采取行动吗？
Practicality 实施难度有多大？
Impact 成功后影响有多大？
Durability 这个发现长期有价值吗？

总分 ≥ 20: t0（立刻行动） 总分 15-19: t1（本周内行动） 总分 10-14: t2（了解即可） 总分 \x3C 10: 跳过

Phase 5: 行动与验证

研究必须产出行动，否则是浪费。

可能的行动：

  • 写一个新 skill
  • 优化现有工作流
  • 安装/试用新工具
  • 写技术笔记给老板
  • 更新 MEMORY.md

验证标准：

  • 行动完成后，回溯检查：这个行动是否真的解决了研究问题？
  • 如果没有，记录偏差，调整下次研究方向

输出格式

每次深度研究产出一份报告：

# 🔬 深度研究报告 | YYYY-MM-DD

## 研究问题
[一句话]

## 扫描概览
- 搜索方向：X 个
- 初始结果：Y 条
- 深度挖掘：Z 条
- 有效发现：W 条

## 心智模型演化
[本次研究如何改变了我的理解]

## 关键发现（按 RAPID 评分排序）
1. [发现] — RAPID: XX/25 — t0/t1/t2
2. ...

## 行动项
- [已完成] ...
- [待执行] ...
- [需老板批准] ...

## 偏差记录
[预期 vs 实际的差异，用于改进下次研究]

与自我进化集成

  • 每日进化任务使用本框架
  • 每周回顾：哪些研究方向产出了最大价值？
  • 每月总结：研究能力本身有何提升？

参考

  • VeriTrace: Evolving Mental Models for Deep Research Agents (arXiv:2605.26081)
  • From Model Scaling to System Scaling (arXiv:2605.26112)
  • Claw-Anything: Benchmarking Always-On Personal Assistants (arXiv:2605.26086)
Usage Guidance
Install if you want a Chinese-language structured research workflow. Before using it for autonomous research, make sure the agent asks before installing tools, writing new skills, or updating MEMORY.md, and ask it to respond in your preferred language if needed.
Capability Assessment
✓ Purpose & Capability
The artifact is a Markdown-only deep research framework that defines research phases, evaluation criteria, and report templates; its search, source-review, and action-item guidance fits the stated purpose.
ℹ Instruction Scope
Instructions are mostly procedural and bounded to research workflow, but the content is written primarily in Chinese and does not explicitly tell the agent to mirror the user's language.
✓ Install Mechanism
The package contains only SKILL.md, no executable scripts, no dependencies, no install hooks, and no declared package registry dependencies.
✓ Credentials
Suggested use of web sources, papers, GitHub, Reddit, and Hacker News is proportionate for deep research; no hidden credential, local indexing, or broad private-data access is requested.
ℹ Persistence & Privilege
The skill lists updating MEMORY.md as a possible research action and mentions recurring daily/weekly/monthly review integration, but this is disclosed as an optional output/action pattern rather than an automatic background process.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install deep-research-framework
  3. After installation, invoke the skill by name or use /deep-research-framework
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Deep Research Framework - Introduces a structured research approach focused on evolving mental models, not just collecting information. - Implements explicit feedback loops: interpretive update, deviation feedback, and schema revision. - Defines a step-by-step workflow: research space definition, breadth/depth scanning, mental model checkpoints, RAPID evaluation, and action/verification. - Provides clear output/report templates to guide deep research and ensure actionable results. - Inspired by recent research (VeriTrace, Agentic System Scaling) and integrates references for further reading.
Metadata
Slug deep-research-framework
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Deep Research?

Structured deep research framework with explicit feedback loops and mental model evolution. Inspired by VeriTrace and Agentic System Scaling papers. It is an AI Agent Skill for Claude Code / OpenClaw, with 9 downloads so far.

How do I install Deep Research?

Run "/install deep-research-framework" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Deep Research free?

Yes, Deep Research is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Deep Research support?

Deep Research is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Deep Research?

It is built and maintained by sdT328606 (@sdt328606); the current version is v1.0.0.

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