deep-research by bytedance/deer-flow
npx skills add https://github.com/bytedance/deer-flow --skill deep-research本技能提供了一套进行彻底网络研究的系统性方法。在任何内容生成任务开始前加载此技能,以确保您能从多个角度、深度和来源收集足够的信息。
在以下情况时始终加载此技能:
切勿仅基于一般知识生成内容。 您输出的质量直接取决于事先进行的研究的质量和数量。单一的搜索查询永远不够。
从广泛的搜索开始以了解整体情况:
示例:
主题:"医疗保健中的AI"
初始搜索:
- "AI 医疗保健应用 2024"
- "人工智能 医疗诊断"
- "医疗保健 AI 市场趋势"
识别出的维度:
- 诊断AI(放射学、病理学)
- 治疗推荐系统
- 行政自动化
- 患者监护
- 监管环境
- 伦理考量
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
针对识别出的每个重要维度,进行有针对性的研究:
web_fetch 完整阅读重要来源,而不仅仅是摘要片段示例:
维度:"放射学中的诊断AI"
针对性搜索:
- "AI 放射学 FDA 批准系统"
- "胸部X光 AI 检测准确性"
- "放射学 AI 临床试验结果"
然后获取并阅读:
- 关键研究论文或摘要
- 行业报告
- 真实世界案例研究
通过寻找不同类型的信息来确保全面覆盖:
| 信息类型 | 目的 | 示例搜索 |
|---|---|---|
| 事实与数据 | 具体证据 | "统计数据"、"数据"、"数字"、"市场规模" |
| 示例与案例 | 实际应用 | "案例研究"、"示例"、"实施" |
| 专家观点 | 权威视角 | "专家分析"、"访谈"、"评论" |
| 趋势与预测 | 未来方向 | "2024年趋势"、"预测"、"未来" |
| 比较 | 背景与替代方案 | "对比"、"比较"、"替代方案" |
| 挑战与批评 | 平衡的观点 | "挑战"、"局限性"、"批评" |
在进入内容生成之前,请验证:
如果任何答案为否,请在生成内容前继续研究。
# 结合上下文具体化
❌ "AI 趋势"
✅ "2024年企业AI采用趋势"
# 包含权威来源提示
"[主题] 研究论文"
"[主题] 麦肯锡报告"
"[主题] 行业分析"
# 搜索特定内容类型
"[主题] 案例研究"
"[主题] 统计数据"
"[主题] 专家访谈"
# 使用时间限定词 — 始终使用 <current_date> 中的实际当前年份
"[主题] 2026" # ← 替换为真实的当前年份,切勿硬编码过去的年份
"[主题] 最新"
"[主题] 近期发展"
在形成任何搜索查询之前,务必检查您上下文中的 <current_date>。
<current_date> 为您提供完整的日期:年、月、日和星期几(例如 2026-02-28, Saturday)。根据用户的询问,使用适当的时间精度:
| 用户意图 | 所需时间精度 | 示例查询 |
|---|---|---|
| "今天 / 今天早上 / 刚刚发布" | 月 + 日 | "2026年2月28日科技新闻" |
| "本周" | 周范围 | "2026年2月24日当周技术发布" |
| "最近 / 最新 / 新" | 月 | "2026年2月AI突破" |
| "今年 / 趋势" | 年 | "2026年软件趋势" |
规则:
"2026年科技新闻" 将不会显示今天的新闻2026-02-28)、书面形式(2026年2月28日)以及不同查询中的相对术语(今天、本周)❌ 用户询问"今天科技界有什么新消息" → 搜索 "2026年新技术" → 错过今天的新闻
✅ 用户询问"今天科技界有什么新消息" → 搜索 "2026年2月28日新技术" + "今天2月28日科技新闻" → 获取今天的结果
在以下情况下使用 web_fetch 阅读完整内容:
研究是迭代的。在初始搜索之后:
当您能自信地回答以下问题时,您的研究就足够了:
完成研究后,您应该拥有:
只有到那时才进行内容生成,利用收集到的信息创建高质量、信息充分的内容。
每周安装量
175
代码仓库
GitHub Stars
26.2K
首次出现
Feb 17, 2026
安全审计
安装于
gemini-cli172
github-copilot172
opencode172
kimi-cli171
amp171
codex171
This skill provides a systematic methodology for conducting thorough web research. Load this skill BEFORE starting any content generation task to ensure you gather sufficient information from multiple angles, depths, and sources.
Always load this skill when:
Never generate content based solely on general knowledge. The quality of your output directly depends on the quality and quantity of research conducted beforehand. A single search query is NEVER enough.
Start with broad searches to understand the landscape:
Example:
Topic: "AI in healthcare"
Initial searches:
- "AI healthcare applications 2024"
- "artificial intelligence medical diagnosis"
- "healthcare AI market trends"
Identified dimensions:
- Diagnostic AI (radiology, pathology)
- Treatment recommendation systems
- Administrative automation
- Patient monitoring
- Regulatory landscape
- Ethical considerations
For each important dimension identified, conduct targeted research:
web_fetch to read important sources in full, not just snippetsExample:
Dimension: "Diagnostic AI in radiology"
Targeted searches:
- "AI radiology FDA approved systems"
- "chest X-ray AI detection accuracy"
- "radiology AI clinical trials results"
Then fetch and read:
- Key research papers or summaries
- Industry reports
- Real-world case studies
Ensure comprehensive coverage by seeking diverse information types:
| Information Type | Purpose | Example Searches |
|---|---|---|
| Facts & Data | Concrete evidence | "statistics", "data", "numbers", "market size" |
| Examples & Cases | Real-world applications | "case study", "example", "implementation" |
| Expert Opinions | Authority perspectives | "expert analysis", "interview", "commentary" |
| Trends & Predictions | Future direction | "trends 2024", "forecast", "future of" |
| Comparisons | Context and alternatives | "vs", "comparison", "alternatives" |
| Challenges & Criticisms | Balanced view | "challenges", "limitations", "criticism" |
Before proceeding to content generation, verify:
If any answer is NO, continue researching before generating content.
# Be specific with context
❌ "AI trends"
✅ "enterprise AI adoption trends 2024"
# Include authoritative source hints
"[topic] research paper"
"[topic] McKinsey report"
"[topic] industry analysis"
# Search for specific content types
"[topic] case study"
"[topic] statistics"
"[topic] expert interview"
# Use temporal qualifiers — always use the ACTUAL current year from <current_date>
"[topic] 2026" # ← replace with real current year, never hardcode a past year
"[topic] latest"
"[topic] recent developments"
Always check<current_date> in your context before forming ANY search query.
<current_date> gives you the full date: year, month, day, and weekday (e.g. 2026-02-28, Saturday). Use the right level of precision depending on what the user is asking:
| User intent | Temporal precision needed | Example query |
|---|---|---|
| "today / this morning / just released" | Month + Day | "tech news February 28 2026" |
| "this week" | Week range | "technology releases week of Feb 24 2026" |
| "recently / latest / new" | Month | "AI breakthroughs February 2026" |
| "this year / trends" | Year | "software trends 2026" |
Rules:
"tech news 2026" will NOT surface today's news2026-02-28), written form (February 28 2026), and relative terms (today, this week) across different queries❌ User asks "what's new in tech today" → searching "new technology 2026" → misses today's news ✅ User asks "what's new in tech today" → searching "new technology February 28 2026" + "tech news today Feb 28" → gets today's results
Use web_fetch to read full content when:
Research is iterative. After initial searches:
Your research is sufficient when you can confidently answer:
After completing research, you should have:
Only then proceed to content generation , using the gathered information to create high-quality, well-informed content.
Weekly Installs
175
Repository
GitHub Stars
26.2K
First Seen
Feb 17, 2026
Security Audits
Gen Agent Trust HubPassSocketWarnSnykWarn
Installed on
gemini-cli172
github-copilot172
opencode172
kimi-cli171
amp171
codex171
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