English · 中文
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.
| 🎯 One command | /research <topic> → fully automated research, zero manual intervention |
| ⏱ Report in ~10 min | quick mode ~8–12 min, standard ~10–15 min |
| 🌍 19 languages | Auto-detects topic language, generates report in the same language |
| 🔧 All platforms | Works with any AI coding tool (Claude Code, Codex CLI, Cursor, DSH, Windsurf, Cline and more) |
| 📁 Local file research | Also supports PDF/DOCX/TXT/MD, no internet needed, auto-parsed |
| 🖥️ Local report browser | Auto-refreshed as a local browser page after each runreports-browser/index.html — search, filter, sort, preview |
| 📄 PDF/DOCX export | Export reports as PDF or DOCX from the preview modal — fully client-side, no server required |
| Command | Result |
|---|---|
/research 中国新能源汽车产业发展现状 | 中文报告 |
/research Competitive landscape of AI cloud computing | English report |
/research Анализ рынка нефти и газа в России | Отчёт на русском |
/research 日本のアニメ産業のグローバル市場戦略 | 日本語レポート |
/research 한국 반도체 산업의 글로벌 경쟁력 분석 | 한국어 보고서 |
local file research, see FAQ for prompts | offline mode, read local files |
It interacts with you entirely in the language you set and searches for materials in that target language — not a simple translation pipeline.
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.
Indie developers, independent researchers, small teams. People who need professional-grade research capabilities without relying on paid databases or research institutions.
| Metric | Data (standard mode example) |
|---|---|
| Report length | 500-700 lines / ~12,000-20,000 chars (varies by language) |
| Data tables | 15-25, covering market size, competitive landscape, technical specs |
| Analysis paragraphs | 80-120 (each with conclusion + data + causation + judgment) |
| Unique sources cited | 15-25 (Chinese and international institutions) |
| Opposing viewpoints | 3-8, at least one controversy per chapter |
| Data collection | ~1-3 min |
| Report generation | ~8-15 min |
| Total time | ~10-20 min |
Above ranges for standard mode. Actual times vary by topic complexity and data availability.
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:
| Report | Tags |
|---|---|
| Global AI Chip Market Landscape and Competitive Dynamics 2026 | AI · Semiconductors |
| The Feasibility of Mars Colonization | Space · Technology |
| Electric Vehicle Battery Supply Chain and Raw Material Geopolitics 2026 | Energy · Geopolitics |
| GenAI Enterprise Adoption Trends & ROI Measurement in 2026 | AI · Enterprise |
| Cross-border E-commerce Logistics Trends in Southeast Asia 2026 | E-commerce · Logistics |
Click a report title to jump to the H33 report collection and search for the topic.
| Component | Cost |
|---|---|
| LLM (already using) | DeepSeek v4 Flash baseline: quick ~100–150k tokens / < $0.03, standard ~150–300k / < $0.06, deep ~300–500k / < $0.10 |
| Scrapling fetching | Runs locally, zero cost |
| Domestic sources | Direct connection, zero cost, no proxy needed |
| AI tool runtime | MIT open source, zero 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.
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
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.
| Dimension | Description |
|---|---|
| Multilingual native writing | Auto-detects topic language, writes directly in 19 languages, no translation pipeline |
| Every number has a source | (N) clickable citations in text, full reference list at end. No source = no number |
| Pros and cons coexist | Every chapter presents controversies and opposing views |
| Confidence grading | Final summary table (high/medium/low) shows what's reliable vs. disputed |
| Data anti-pitfall | Auto-detects common data errors — wrong units, fabricated trends, misattributed sources |
| Paragraphs over padding | 8-12 substantive paragraphs per chapter as core, tables can't pad the length |
| Command | Purpose | Min chapters | Min paragraphs/chapter | Target chars | Est. time |
|---|---|---|---|---|---|
/research <topic> |
standard (default) | 8 | ≥ 5 | ≈ 25,000 | ~10–15 min |
/research <topic> -quick |
Quick insight | 5 | ≥ 4 | ≈ 15,000 | ~8–12 min |
/research <topic> -deep |
Maximum depth | 10 | ≥ 6 | ≈ 45,000 | ~15–25 min |
Parameters in
profiles.json, restart to apply. Char count excludes whitespace and Markdown syntax.
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.
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.
Each tool registers skills/commands differently — drop the whole project into the right directory (common tools below):
| Tool | skill/entry location | Command form |
|---|---|---|
| OpenCode | ~/.opencode/skills/deep-research/ |
/research, /research-update (included in command/) |
| Codex CLI | skill directory, command/ files included |
/research, /research-update |
| Claude Code | ~/.claude/skills/deep-research/ |
Use SKILL.md as an Agent Skill |
| Cursor | .cursor/skills/ or custom commands |
Custom command pointing at SKILL.md |
| DSH / others | Any directory that supports skill loading | Load SKILL.md, then enter a topic |
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.
| Component | Online mode | Offline mode | How to get |
|---|---|---|---|
| AI tool runtime (Claude Code / Codex CLI / Cursor / DSH / OpenCode etc.) | ✅ Required | ✅ Required | Pick your preferred tool |
| Scrapling | ✅ Required | ❌ Not needed | For web scraping; offline mode doesn't need it |
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.
After installation and restart, type in the chat:
| Command | Description | Est. time |
|---|---|---|
/research <topic> |
standard mode (online search) | ~10-15 min |
/research <topic> -quick |
quick mode (online search) | ~8-12 min |
/research <topic> -deep |
deep mode (online search) | ~15-25 min |
local file research |
offline mode (local files) | depends on file size |
/research-update |
Check for updates | — |
Local file research: see FAQ §2 "How to use local materials for report generation?" for exact prompts.
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.
Reports are saved as Markdown files in the skill's reports/ directory, with date-timestamped filenames:
<your skill install dir>/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.
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-runsgit pull - Manual:
cd <skill install dir>/deep-research && git pull
Check version: cat <skill install dir>/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.
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。
| 🎯 一个命令 | /research 你的主题 → 全自动调研，无需人工干预 |
| ⏱ 十分钟出报告 | quick 模式约 8–12 分钟，standard 约 10–15 分钟 |
| 🌍 19 种语言 | 主题用什么语言写，报告就用什么语言出，自动检测 |
| 🔧 所有平台通用 | 任何 AI 编程工具均可使用（Claude Code、Codex CLI、Cursor、DSH、Windsurf、Cline 等） |
| 📁 本地文件调研 | 也可支持本地 PDF/DOCX/TXT/MD，不联网，AI 自动解析 |
| 🖥️ 本地报告浏览页 | 每次报告生成后自动刷新为本地浏览器页面reports-browser/index.html，支持搜索/筛选/排序/弹窗预览 |
| 📄 PDF/DOCX 导出 | 本地浏览页弹窗中可导出 PDF、DOCX 格式，浏览器端直接转换下载 |
| 命令 | 说明 |
|---|---|
/research 中国新能源汽车产业发展现状 | 中文报告 |
/research Competitive landscape of AI cloud computing | English report |
/research Анализ рынка нефти и газа в России | Отчёт на русском |
/research 日本のアニメ産業のグローバル市場戦略 | 日本語レポート |
/research 한국 반도체 산업의 글로벌 경쟁력 분석 | 한국어 보고서 |
本地资料调研，详细命令见 FAQ | 离线模式，读本地文件 |
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。
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。
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。
| 指标 | 数据（standard 模式示例） |
|---|---|
| 报告长度 | 500-700 行 / 约 12,000-20,000 字（视语言浮动） |
| 数据表 | 15-25 张，覆盖市场规模、竞争格局、技术参数等多个维度 |
| 分析段落 | 80-120 段（每段含结论 + 数据 + 因果 + 判断） |
| 引用的独立机构 | 15-25 家（中国信通院、艾瑞咨询、国家统计局、百度百科、知乎、36氪、澎湃新闻等） |
| 反方观点 | 3-8 处，每章至少呈现一个争议或反对角度 |
| 数据收集 | ~1-3 分钟 |
| 报告生成 | ~8-15 分钟 |
| 总耗时 | ~10-20 分钟 |
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。|
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：
| 报告主题 | 话题标签 |
|---|---|
| 长江三角洲与珠江三角洲：中国两大经济引擎的地理比较 | 地理 · 经济 |
| 郑和下西洋：为什么中国在 15 世纪放弃了海洋？ | 历史 · 航海 |
| 玛雅文明崩溃之谜：干旱、战争还是生态超载？ | 历史 · 文明 |
| 2026年中国新能源汽车行业展望 | 汽车 · 产业 |
| 火星移民的工程现实：从 SpaceX 到 ISRU 到辐射防护 | 航天 · 科技 |
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。
| 组件 | 费用 |
|---|---|
| LLM（你已经在用的） | DeepSeek v4 Flash 基准：quick 约 10–15 万 token / < 0.2 元，standard 约 15–30 万 / < 0.4 元，deep 约 30–50 万 / < 0.7 元 |
| Scrapling 抓取 | 纯本地运行，零费用 |
| 国内源（百度百科/维基百科/知乎/36氪/澎湃/199IT/艾瑞/东方财富/国统局等） | 直连零费用，不要代理 |
| AI 工具运行时 | 开源免费，零费用 |
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。
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
↓
④ 验收装配 — 批量 validate → assemble-report → convert-citations → escape-currency → qa-report
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。
| 维度 | 说明 |
|---|---|
| 多语言专业行文 | 自动检测主题语言，以 19 种语言直接撰写报告，非翻译模式 |
| 每个数字有来源 | 正文标注 (N) 可点击引用，文末附参考来源列表。找不到来源的数字不写 |
| 正反观点并存 | 每章呈现争议和反对观点，不回避矛盾 |
| 置信度分级 | 末章汇总表（高/中/低），什么可靠什么有争议一目了然 |
| 数据防坑机制 | 自动识别常见数据错误——单位搞混、数据造假、张冠李戴，不让有问题的数据混进报告 |
| 段落重于行数 | 每章 8-12 段正文为核心，表格和空行灌不了水 |
| 命令 | 用途 | 最少章数 | 最少段落/章 | 参考字数（字符） | 参考耗时 |
|---|---|---|---|---|---|
/research 主题 |
standard 默认 | 8 | ≥ 5 | ≈ 25,000 | ~10–15 min |
/research 主题 -quick |
快速洞察 | 5 | ≥ 4 | ≈ 15,000 | ~8–12 min |
/research 主题 -deep |
极致深度 | 10 | ≥ 6 | ≈ 45,000 | ~15–25 min |
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。
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。
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。
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）：
| 工具 | skill/入口位置 | 命令形式 |
|---|---|---|
| OpenCode | ~/.opencode/skills/deep-research/ |
/research、/research-update（command/ 已含） |
| Codex CLI | skill 目录，command/ 已含命令文件 |
/research、/research-update |
| Claude Code | ~/.claude/skills/deep-research/ |
用 SKILL.md 作为 Agent Skill |
| Cursor | .cursor/skills/ 或自定义命令 |
自定义命令指向 SKILL.md |
| DSH / 其他 | 任意支持 skill 加载的目录 | 加载 SKILL.md 后输入主题 |
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。
| 组件 | 在线模式 | 离线模式 | 获取方式 |
|---|---|---|---|
| AI 工具运行时（Claude Code / Codex CLI / Cursor / DSH / OpenCode 等） | ✅ 必须 | ✅ 必须 | 选择你习惯的工具即可 |
| Scrapling | ✅ 必须 | ❌ 不需要 | 网页抓取用，离线模式不涉及 |
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。
安装并重启你的 AI 工具后，在聊天框输入：
| 命令 | 说明 | 参考耗时 |
|---|---|---|
/research 你的主题 |
standard 模式（在线搜索） | ~10-15 min |
/research 你的主题 -quick |
quick 模式（在线搜索） | ~8-12 min |
/research 你的主题 -deep |
deep 模式（在线搜索） | ~15-25 min |
本地资料调研 |
离线模式（读本地文件） | 取决于文件大小 |
/research-update |
检查更新 | — |
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？》。
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。
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：
<你的 skill 安装目录>/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。
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 <skill 安装目录>/deep-research && git pull
版本号可通过 cat <skill 安装目录>/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。
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。
Created by hoolulu · github.com/hoolulu/deep-research
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