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Deep Research

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feiskyer
deep-research

用 Codex CLI 子任务完成多来源深度调研并综合成带出处的报告。用于确实需要分头取证的研究,不用于单次查证或已给定材料的总结。

Overview

Publisherfeiskyer
Repositorycodex-settings
Skill namedeep-research
Stars
247
Forks
35
Bundled files
5
LicenseMIT
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Deep Research 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/feiskyer/codex-settings/tree/main/skills/deep-research
  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/feiskyer/codex-settings.git /tmp/codex-settings
mkdir -p .claude/skills
cp -r /tmp/codex-settings/skills/deep-research .claude/skills/deep-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Research 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 Deep Research 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 Deep Research 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.

Deep Research

主控负责界定问题、拆分、核验与综合;子任务负责互不重叠的采集或局部分析。完成标准是用户需要的报告和证据覆盖,不是子进程退出或材料聚合成功。

核心约束

  1. 保持用户当前模型和推理配置,不传 --model,不覆盖无关配置。
  2. 子进程默认使用 workspace-write;只有确实需要 shell 网络访问时才启用 runner 的 --network。
  3. 先检查当前会话可用的 Skills、连接器和 MCP,再按来源适配能力;不要假设固定服务或工具名存在。
  4. 不使用 --dangerously-bypass-approvals-and-sandbox。
  5. 所有运行产物写入独立的 .research/<name>/ 目录。
  6. 只在委派已获授权时启动子任务。执行前简述拆分与成本;超出已有预算、访问范围或权限时再征求同意,不因正常使用已授权的研究来源重复暂停。

按需加载

准备运行子任务时读 references/runner.md,其中包含目录、manifest、命令与恢复约束。两个脚本均只依赖 Python 标准库;--help 给出参数真源。

Workflow

  1. 从用户请求中提取目标、时间范围、来源边界和交付格式,只追问会改变研究结论或执行范围的缺口。用代表性真实样本校准拆分,复用已有材料。
  2. 为每个子任务明确问题、输入、允许访问的范围、输出和证据要求。要求结论旁附来源,无法确认的内容说明缺口。将采集材料保存在运行目录内,避免重复抓取。
  3. 选择与任务成本相称的并发和超时,陌生或高成本批次先小规模验证。主控同步推进非重叠工作,不重复子任务正在做的采集。
  4. 检查 results.json、子报告和关键原始来源。失败、空输出、缺失引用或未覆盖的问题仍是未完成;按 runner 参考中的恢复约束有限重试,不重跑已验证成功的任务。缺少权限或需要扩大范围时停止相应操作并说明阻塞。
  5. 使用 aggregate.py 整理内部材料,再综合为 polished_report.md(用户指定位置优先)。消除重复、核对关键数据、区分来源冲突和证据强弱;aggregated_raw.md 不是成品。

交付报告绝对路径、关键结论和剩余缺口,说明实际使用的权限例外。必要证据仍缺失时,报告已完成范围、具体阻塞及恢复条件,不把部分材料称为完整报告;不要发送内部 Prompt 或调度日志充当成品。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deep Research AI skill do?

用 Codex CLI 子任务完成多来源深度调研并综合成带出处的报告。用于确实需要分头取证的研究,不用于单次查证或已给定材料的总结。

Why use Deep Research on TypingMind?

Because you install it once and use it with any model. Deep Research 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 Deep Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/feiskyer/codex-settings/tree/main/skills/deep-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deep Research?

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 Deep Research?

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

Is the Deep Research AI skill free?

Yes. It is published on GitHub by feiskyer under the MIT license. 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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