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Auto Research Loop

Community
Dominic789654
auto-research-loop

自动调研循环执行清单:plan → search → read → synthesize 的有界迭代,每步用 research_log 落盘(可跨压缩恢复),带明确的停止条件与来源纪律。适用于"帮我调研 X / 综述一下 X / 对比几个方案"类多步信息收集任务。

Overview

PublisherDominic789654
Repositoryawesome-deepseek-harness
Skill nameauto-research-loop
Stars
397
Forks
199
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Dominic789654 on GitHub. Read the source before you install it.

Installation

Install the Auto Research Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Dominic789654/awesome-deepseek-harness/tree/main/plugins/dsh-research-loop/skills/auto-research-loop
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dominic789654/awesome-deepseek-harness.git /tmp/awesome-deepseek-harness
mkdir -p .claude/skills
cp -r /tmp/awesome-deepseek-harness/plugins/dsh-research-loop/skills/auto-research-loop .claude/skills/auto-research-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto Research Loop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Auto Research Loop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Auto Research Loop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Auto-Research Loop

执行清单,不是教材。核心:有界循环 + 每步落盘 + 来源可溯。

0. 恢复

开始前先 research_log(action: read, topic: <主题>)。已有记录 → 从上次 phase 接着做,不重跑已完成的搜索。

1. Plan

  1. 把任务拆成 3-6 个可回答的子问题;定义"做完"的标准(要回答什么、给谁看、多长)。
  2. research_log(append, phase: plan) 记下子问题清单和停止条件。

2. Search(每轮 ≤3 个查询)

  1. 每个子问题设计一个具体查询(关键词 + 限定词),用可用的搜索/抓取工具执行。
  2. 只挑权威或一手来源进入 read 列表:官方文档 > 原始仓库/论文 > 高质量二手。
  3. research_log(append, phase: search, sources: [...]) 记录查询词、命中和弃选原因。

3. Read

  1. 逐个读取入选来源,抽取与子问题直接相关的事实(数字、日期、结论),标注出处 URL。
  2. 事实间冲突 → 两个说法都记,标注各自来源与日期,不要静默择一。
  3. research_log(append, phase: read, sources: [url]),每个来源一条,note 写抽取到的事实而非"读了这篇"。

4. Synthesize & 循环判定

  1. 对照 plan 里的子问题:哪些已有足够证据,哪些还缺。
  2. research_log(append, phase: synthesize) 记录当前结论草稿和缺口。
  3. 停止条件(任一满足即进入 conclude):
    • 所有子问题有 ≥2 个独立来源支撑;
    • 连续一轮搜索没有产生新信息(收益递减);
    • 达到迭代上限(默认 3 轮 search→read→synthesize)。
  4. 未满足 → 带着缺口回到步骤 2,只搜缺口,不重搜已覆盖的问题。

5. Conclude

  1. 输出最终综述:按子问题组织,每个结论后附来源;冲突信息并列呈现。
  2. 明确列出"未能确认"的项,不要用推测填补。
  3. research_log(append, phase: conclude) 记录最终结论摘要。

红线

  • 无来源的断言不进结论;引用只用自己真的打开过的 URL。
  • 循环必须有界:到达上限就收敛汇报,不"再搜一轮"。
  • 长任务中途压缩后,以 research_log 为准恢复状态,不凭记忆续写。

Frequently asked questions

What does the Auto Research Loop AI skill do?

自动调研循环执行清单:plan → search → read → synthesize 的有界迭代,每步用 research_log 落盘(可跨压缩恢复),带明确的停止条件与来源纪律。适用于"帮我调研 X / 综述一下 X / 对比几个方案"类多步信息收集任务。

Why use Auto Research Loop on TypingMind?

Because you install it once and use it with any model. Auto Research Loop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Auto Research Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dominic789654/awesome-deepseek-harness/tree/main/plugins/dsh-research-loop/skills/auto-research-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Auto Research Loop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Auto Research Loop?

As many as you like. As long as a model supports skills, you can use Auto Research Loop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Auto Research Loop AI skill free?

It is published on GitHub by Dominic789654. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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