deep-research Skill

English · 中文

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Deep Research Report Generation Skill — One command, ten minutes, institutional-grade deep research report

Multi-agent autonomous search, scrape, write, and QA — feed it a topic, get a citable, browsable, exportable research report in Chinese or 19 other languages.

Benchmarked against institutional research structure: conclusions-first, traceable sources, counter-arguments, scenario forecasting. Supports quick / standard / deep tiers, with automatic language detection across 19 languages.

Built for industry research, trend foresight, competitive scanning, policy analysis, technology deep-dives, and investment memos — not a handful of search summaries, but a report you can actually use.

Current version: View updates

📂 Browse all sample reports → H33研报· 深度调研报告集 — filter, sort, and browse by language and depth.

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✨ At a Glance

It interacts with you entirely in the language you set and searches for materials in that target language — not a simple translation pipeline.

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1. Why You Need This

If you’ve ever asked AI to do research, you’ve likely hit these walls:

• Search + summarize → too shallow, just a few bullet points
• Industry reports at $50–500+ each → too expensive for individuals
• Overseas tools → can’t search Chinese sources like Baidu Baike, Zhihu, 199IT, iResearch
• AI fabricates numbers → looks reasonable but has no traceable source

This skill follows a 4-stage pipeline before delivering a report. Not search-and-dump — it’s analyze → search+verify → write → verify.

2. Who It’s For

Indie developers, independent researchers, small teams. People who need professional-grade research capabilities without relying on paid databases or research institutions.

3. Typical Output (Standard Mode)

Above ranges for standard mode. Actual times vary by topic complexity and data availability.

📖 Featured Reports

All sample reports have moved to H33研报· 深度调研报告集 — filter, sort, and browse by language and type. Some featured topics:

Click a report title to jump to the H33 report collection and search for the topic.

4. Cost

Estimates based on DeepSeek v4 Flash ($0.14/1M input, $0.28/1M output, source: https://api-docs.deepseek.com/quick_start/pricing). Actual costs vary by cache hit rate and topic complexity.

5. How It Works

The pipeline runs in 4 automated stages:

① Analyze outline — Analyze topic, generate research framework and search plan
         ↓
② Collect data — ╭─ Online: four-layer parallel search (tool built-in engine → suggested sources → sources.json → free fallback) → Scrapling batch fetch → data pool
                  ╰─ Offline: read local files directly (PDF/DOCX/TXT/MD) → data pool
         ↓
③ Parallel writing — All chapters in parallel (auto-sequential without multi-agent), facts embedded directly in prompts, no tool calls
         ↓
④ Validate & assemble — Batch validate → assemble-report → convert-citations → escape-currency → qa-report

6. Search Pipeline & Built-in Resources

Search uses a four-layer priority strategy, all issued in parallel:

Layer 0 — Tool built-in engine (auto-detected at runtime, e.g., `websearch` / `web_search`)
Layer 1 — Outline-suggested sources (topic-targeted recommendations, e.g., arctic-council.org)
Layer 2 — sources.json (30+ curated quality sources, health-checked on startup)
Layer 3 — Free source reinforcement (A/B class search fallback)

All layers are merged and deduplicated, then batch-fetched by Scrapling. Free source reinforcement is triggered only when Layer 0-2 fail the per-sub-question quality gate (URL < 3 / outdated year / no authoritative source for high-priority sub-questions).

sources.json covers academic (Semantic Scholar / arXiv / PubMed / Nature), data (World Bank / IMF / Our World in Data), news (Reuters / BBC / Guardian), and Chinese sources (Baidu Baike / Zhihu / 36Kr / The Paper / iResearch / East Money / CSDN) — 30+ sources, auto health-checked on startup with dead sources skipped.

7. Report Highlights

8. Three Depth Modes

Parameters in profiles.json, restart to apply. Char count excludes whitespace and Markdown syntax.

9. Installation

deep-research is an all-platform skill: one codebase, drop it into any AI tool that supports skill/command mechanisms — no per-tool rewrite needed.

🧠 Method 1: AI Auto-Install (Recommended)

Copy this prompt into your AI tool’s chat, the AI will do everything automatically:

Please read the https://github.com/hoolulu/deep-research project and follow the documentation to:
1. Install prerequisites (determine method based on Scrapling docs and your OS)
2. Register the Scrapling MCP Server, verify it works after restart
3. Register the /research and /research-update entry points for your current tool
Confirm each step, then read VERSION and summarize the installation status.

The AI reads the docs → understands your system → installs step by step → verifies. No manual commands needed.

📋 Method 2: Manual Entry Registration

Each tool registers skills/commands differently — drop the whole project into the right directory (common tools below):

This skill’s SKILL.md is already written as all-platform instructions: no dependency on any tool-specific task() multi-agent syntax. Chapter writing is parallel by default (when a multi-agent tool is detected); tools without multi-agent support automatically fall back to sequential writing — same output, slightly slower. Search and scraping logic (Scrapling) is reused unchanged across platforms.

Prerequisites

Platform note: Multi-agent-capable tools (OpenCode, Claude Code, Codex, DSH, etc.) naturally write chapters in parallel; tools without multi-agent support automatically write sequentially. Offline mode only needs the LLM’s file-reading capability — no search/scraping components required.

10. Usage

After installation and restart, type in the chat:

Local file research: see FAQ §2 “How to use local materials for report generation?” for exact prompts.

What Happens After You Send It

The entire pipeline runs automatically — you don’t need to do anything:

① Analyze outline — Analyze topic, generate framework and search plan
② Collect data — tool built-in engine + sources.json parallel search → quality-triggered reinforcement → Scrapling batch fetch → data pool → quality check
③ Parallel writing — All chapters simultaneously, facts embedded in prompts
④ Validate & assemble — Batch validate → assemble → citations → QA

Total ~10-20 minutes. Complex topics may take longer, simple ones may be faster.

Output Files

Reports are saved as Markdown files in the skill’s reports/ directory, with date-timestamped filenames:

/deep-research/reports/

Open with any Markdown reader (Typora / Obsidian / VS Code etc.).

You can also specify a custom output path — ask AI to configure it.

Local report browser page: After each research run, AI auto-refreshes reports-browser/index.html. Open it directly in your browser (file:// protocol works) — all reports displayed in a searchable, filterable table with click-to-preview modal.

11. FAQ

1. Search quotas? How to ensure uninterrupted searching?

The system uses a four-layer priority search architecture (tool built-in engine → outline-suggested sources → sources.json → free source fallback), all issued in parallel, each layer auto-degrades on failure:

• Layer 0 — Tool built-in engine: Auto-detects the tool’s built-in search engine at runtime (e.g., websearch / web_search). If available, used as primary, runs in parallel with subsequent layers. No additional configuration needed.
• Layer 1 — Outline-suggested sources: Task 1 recommends authoritative domains per topic (e.g., arctic-council.org, stats.gov.cn), searched first.
• Layer 2 — sources.json quality sources: 30+ curated sources (Semantic Scholar / arXiv / Nature / World Bank / IMF / Reuters / BBC / Baidu Baike / Zhihu / 36Kr / iResearch / East Money etc.). Auto health check on startup, dead sources skipped.
• Layer 3 — Free source reinforcement (final fallback): triggered only when Layer 0-2 fail the per-sub-question quality gate (URL < 3 / outdated / no authoritative source for high-priority). DuckDuckGo / Bing / Brave / Mojeek / Semantic Scholar / GDELT / arXiv + 20+ Chinese sources. No API keys required, always available.

2. How to use local materials for report generation?

The skill has a built-in offline mode that generates fully-formatted reports (TOC, citations, metadata) from local files. Supported formats: MD / TXT (native read), PDF (AI auto-installs pypdf for text extraction), DOCX (AI auto-installs python-docx).

Choose your scenario:

Scenario 1: Local materials + online supplement (recommended for most complete research)

Use the deep-research skill with my local files in D:\notes\projectA to generate a research report on XX (quick mode). Prioritize local content, search online for anything missing.

Scenario 2: Local materials only, no internet (when you have sufficient data and don’t want online distractions)

Use the deep-research skill with my local files in D:\notes\projectA to generate a research report on XX (quick mode). Use only local materials, do not search online.

The system skips the search/scraping pipeline and reads local files directly. Task 3 (chapter writing) and Task 4 (assembly/QA) run normally. The final output includes metadata, [N] citations, and TOC.

Scenario 3: Pure local, no skill (lightweight, no professional format needed)

Help me organize the materials in D:\notes\projectA into a structured research report with table of contents and chapter headings.

Scenario guide: Incomplete materials → Scenario 1 (online supplement); Sufficient materials + need professional format → Scenario 2 (offline mode); Quick summary only → Scenario 3 (lightweight).

3. How to update to the latest version?

Version strategy: main branch always has the latest code. GitHub Releases are only for milestone markers.

All tools:

• Auto: Type /research-update, AI auto-runs git pull
• Manual: cd /deep-research && git pull

Check version: cat /deep-research/VERSION

4. Can other tools auto-update?

Yes. All tools share the same all-platform codebase — just git pull in the install directory (or ask your AI to do it). There are no platform-specific changes to preserve, so updates never conflict.

5. Is my data safe?

All processing is done locally. No data is collected or uploaded.

6. How do I view my generated reports?

After each research run, the AI outputs both the report file path and the local report browser page path.

• Report file: {SKILLDIR}/reports/{LANG}/xxx.md — open with any Markdown reader
• Local report browser page: {SKILLDIR}/reports-browser/index.html — open directly in your browser (works with file:// protocol). All reports are displayed in a table with search, language/depth filtering, sorting, and click-to-preview in a modal

You can also manually refresh the browser page anytime by running python tools/generate_pages.py --local in the skill directory.

12. Screenshot

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深度调研报告生成 Skill — 一条命令，十分钟出券商级深度调研报告

多 Agent 自动搜索、抓取、撰写、质检——输入主题，输出可引用、可浏览、可导出的中文/多语言调研报告。

对标券商/第三方研究机构结构：结论先行、来源可追溯、反方视角、情景预测。支持 quick / standard / deep 三档模式，19 种语言随主题自动切换。

适合行业研究、趋势前瞻、竞品扫描、政策解读、技术专题、投研备忘——不是搜几条摘要，是交付一份能拿去用的报告。

当前版本： 查看更新

📂 浏览所有示例报告 → H33研报· 深度调研报告集 可筛选、排序、按语言和类型浏览所有示例报告。

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✨ 一分钟看懂

是全程以设定语言与你交互，并搜索目标语言的资料，不是简单的翻译输出。

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一、为什么你需要这个

让 AI 帮你做调研，你大概率碰过这些坑：

• 搜索 + 总结 → 太浅，出来几条摘要，没有纵深
• 行业报告按份收费 $50–500+ → 太贵，个人用不起
• 海外工具 → 搜不到国内资源如：百度百科、知乎、199IT、艾瑞
• AI 编数字 → 看起来合理，但找不到来源

这个 skill 走完 4 层流程才交报告。不是搜完就出，是析→搜验→写→验。

二、谁适合用

独立开发者、独立研究者、小团队。 需要专业级调研能力，但不想依赖付费数据库或研究机构的人。

三、一次标准模式调研的输出

以上为 standard 模式典型范围，实际因主题复杂度、数据可获取性、搜索引擎响应等因素有所浮动。|

📖 精选报告展示

所有示例报告已迁移至 H33研报· 深度调研报告集，可筛选、排序、按语言和类型浏览。以下为部分精选主题：

点击报告标题跳转到 H33 研报集，在站内搜索对应主题即可阅读。

四、成本

以上估算基于 DeepSeek v4 Flash（$0.14/百万输入、$0.28/百万输出，来源：https://api-docs.deepseek.com/quick_start/pricing）。实际因缓存命中率与主题复杂度浮动。

五、工作逻辑

整个流程分 4 个阶段，按顺序自动执行：

① 分析大纲 — 分析主题，生成调研框架和搜索计划
         ↓
② 采集数据 — ╭─ 在线模式：四层搜索并行（工具内置引擎 → 建议源 → sources.json → 免费源）→ Scrapling 批量抓取 → 数据池
               ╰─ 离线模式：直接读取本地文件（PDF/DOCX/TXT/MD）→ 数据池
         ↓
③ 并行撰写 — 所有章节同时撰写，事实直接嵌入 prompt，不做工具调用
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④ 验收装配 — 批量 validate → assemble-report → convert-citations → escape-currency → qa-report

六、搜索链路与内置资源

搜索采用 四层优先级 策略，全部并行发出：

Layer 0 — 工具内置引擎（如 `websearch` / `web_search`，运行时自适应）
Layer 1 — 大纲建议源（按主题定向推荐，如 arctic-council.org）
Layer 2 — sources.json（skill 内置 30+ 优质源，启动时健康检测）
Layer 3 — 免费源补强（A/B 类搜索兜底）

所有层的结果合并去重，由 Scrapling 统一抓取全文。免费源补强仅在 Layer 0-2 结果未通过逐子问题质量门时触发（子问题可用 URL < 3 / 结果年份过旧 / high 优先级子问题无权威来源）。

sources.json 覆盖学术（Semantic Scholar / arXiv / PubMed / Nature）、数据（World Bank / IMF / Our World in Data）、新闻（Reuters / BBC / Guardian）、中文（百度百科 / 知乎 / 36氪 / 澎湃 / 艾瑞 / 东方财富 / CSDN 等）30+ 个源，启动时自动健康检测，死源跳过。

七、报告独特亮点

八、三种深度

参数见 profiles.json，修改后重启生效。字数为去空格和 Markdown 语法的纯字符数。

九、安装

deep-research 是所有平台通用的 skill：一套文件，放入任意支持 skill/命令机制的 AI 工具即可使用，无需按工具改写流程。

🧠 方式一：AI 傻瓜安装（推荐）

把下面这段提示词复制到你的 AI 工具聊天框发送，AI 会自动完成一切：

请调研 https://github.com/hoolulu/deep-research 项目，按照文档要求依次完成：

1. 安装前置依赖（根据 Scrapling 官方文档和你的操作系统确定安装方式）
2. 注册 Scrapling MCP Server，确保重启工具后正常使用
3. 把本 skill 注册为当前工具的 skill / 命令入口（/research 和 /research-update）

每完成一步都确认结果，完成后读取 VERSION 确认版本号，并总结安装状态。

AI 会读取项目文档→理解系统类型→逐项安装→验证可用性。不需要手动执行任何命令。

📋 方式二：手动注册命令入口

各工具的 skill/命令注册方式不同，把整个项目放进对应目录即可（以下为常见工具）：

本 skill 的 SKILL.md 已写成所有平台通用指令：不再依赖任何工具专属的 task() 等多 agent 语法。章节撰写默认并行（探测到多 agent 工具时），没有多 agent 能力的工具会自动降级为串行撰写，产物一致，仅耗时略增。搜索与抓取逻辑（Scrapling）各平台原样复用。

前置依赖

平台说明：支持多 agent 的工具（OpenCode、Claude Code、Codex、DSH 等）天然并行撰写章节；不支持多 agent 的工具自动串行撰写。离线模式下仅依赖 LLM 的文件读取能力，无需搜索/抓取组件。

十、使用方法

安装并重启你的 AI 工具后，在聊天框输入：

本地资料调研：具体指令词见 FAQ 第 2 节《如何使用本地资料生成报告？》。

发送后会发生什么

整个流程自动运行，你不需要做任何操作：

① 分析大纲 — 分析主题，生成调研框架和搜索计划（含 source_suggestions 定向源推荐）
② 采集数据 — 四层搜索并行（工具内置引擎→建议源→sources.json→免费源）→ Scrapling 批量抓取 → 数据池提取 → 数据质检
③ 并行撰写 — 所有章节同时撰写，事实直接嵌入 prompt，不做额外工具调用
④ 装配验收 — 批量 validate → assemble-report → convert-citations → escape-currency → qa-report

以上累计 ~10-20 分钟。复杂主题可能延长，简单主题可能缩短。

输出文件

报告以 Markdown 格式保存到 skill 目录下的 reports/ 文件夹，文件名包含日期时间戳：

/deep-research/reports/

可以用任何 Markdown 阅读器（Typora / Obsidian / VS Code 等）打开。

你也可以指定报告的存放路径，让 AI 帮你修改。

本地报告列表页：每次调研完成后，AI 自动刷新 reports-browser/index.html。直接用浏览器打开（支持 file:// 协议），所有报告以表格展示，支持搜索、按语言/深度筛选、排序，点击标题在弹窗中预览。

十一、FAQ

1. 搜索额度？怎么保证搜索不中断？

系统采用 四层搜索 + 质量触发补强 架构：

• Layer 0 — 工具内置引擎（新增）：运行时自动探测当前工具的内置搜索引擎（如 websearch / web_search）。如果可用，以此为主力搜索引擎，与后续层并行发出。无需额外配置。
• Layer 1 — 大纲建议源：阶段1 根据主题定向推荐权威域名（如 moe.gov.cn、stats.gov.cn），用内置引擎做 site: 定向搜索。
• Layer 2 — sources.json 优质源：skill 内置 30+ 精选源（Semantic Scholar / arXiv / Nature / World Bank / IMF / Reuters / BBC / 百度百科 / 知乎 / 36氪 / 艾瑞 / 东方财富 等）。启动时自动健康检测，死源跳过。
• Layer 3 — 免费源补强（兜底）：当 Layers 0-2 合计结果质量不足（URL < 3 / 年份过旧 / 来源过少）时触发。DuckDuckGo / Bing / Brave / Mojeek / Semantic Scholar / GDELT / arXiv + 百度百科 / 知乎 / 199IT / 艾瑞 / 36氪 / 澎湃 / 东方财富 / 微博 / CSDN / 虎嗅 / 豆瓣 等 20+ 源。不依赖任何 API Key，永远可用。

2. 如何使用本地资料生成报告？

Skill 内置了离线模式，可以根据本地文件直接生成带有完整格式（目录/引用/元数据）的调研报告。支持的文件格式：MD / TXT（原生读取）、PDF（AI 自动安装 pypdf 提取文本）、DOCX（AI 自动安装 python-docx 解析）。

根据你的需要选择以下场景：

场景 1：本地资料 + 联网补充（推荐，调研最完整）

请使用 deep-research 这个 skill，根据 D:\我的笔记\项目A 的本地资料，生成一份关于 XX 的研究报告（quick 模式）。本地资料里的内容优先作为素材，不够的你在网上搜索补充。

场景 2：只用本地资料，不联网（适合资料足够、担心联网干扰主题的情况）

请使用 deep-research 这个 skill，根据 D:\我的笔记\项目A 的本地资料，生成一份关于 XX 的研究报告（quick 模式）。只看本地资料，不要联网搜索。

系统会跳过搜索/抓取流程，直接从指定文件提取数据，后续的章节撰写和装配 QA 正常执行。最终输出带有元数据、[N] 引用、目录的标准报告。

场景 3：纯本地，不用 skill（最轻量，适合不需要专业报告格式的快速总结）

根据 D:\我的笔记\项目A 的资料，帮我整理成一份结构化的研究报告，要有目录和章节标题。

场景选择建议：资料不够全 → 场景 1（联网补充）；资料足够且需要专业报告格式 → 场景 2（离线模式）；只需快速总结 → 场景 3（最轻量）。

3. 如何更新到最新版本？

版本策略：main 分支始终是最新代码，日常小修改直接推送。GitHub Releases 仅用于里程碑版本标记（如 v2.1.0 → v2.2.0），不必等到新 Release 才更新。

任意工具的用户：

• 自动：输入 /research-update，AI 自动执行 git pull 获取最新
• 手动：cd /deep-research && git pull

版本号可通过 cat /deep-research/VERSION 查看。

4. 其他工具能自动更新吗？

可以。所有工具共用同一套所有平台通用代码，直接在安装目录 git pull 即可（或让 AI 帮你执行）。没有平台特定的适配改动需要保留，因此更新不会产生冲突。

5. 数据安全吗？

所有处理在本地完成。不收集、不上传任何用户数据。

十二、运行截图

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License / 协议

MIT

This project uses MIT instead of GPL/CC because its core value is a portable methodology and pipeline design, not a copyrighted product. MIT maximizes reuse and adaptation across different platforms and toolchains, consistent with the “all-platform” positioning.

本项目采用 MIT 协议。选择 MIT 而非 GPL/CC 等更严格的协议，是因为本项目的核心是一套可移植的方法论和管道设计，而非需要保护版权的成品库。MIT 能让它在不同平台和工具链中被最大化地复用和改造，与"所有平台通用"的定位一致。

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Star History / 星标历史

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Created by hoolulu · github.com/hoolulu/deep-research

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