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deep-research-pro
Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
自动收集社交媒体内容(X/Twitter、即刻、微信公众号、Reddit、知乎、Bilibili、Hacker News 等)并整理成结构化笔记存入飞书多维表格。 当用户发送链接、截图或混合内容时自动触发。支持平台检测、去重、AI 摘要、飞书云存储一体化流程。 Use when: user shares a link or screenshot from social media platforms and wants to save/collect/organize content into Feishu.
Converts Markdown to styled HTML with WeChat-compatible themes. Supports code highlighting, math, PlantUML, footnotes, alerts, and infographics. Use when user asks for "markdown to html", "convert md to html", "md转html", or needs styled HTML output from markdown.
Formats plain text or markdown files with frontmatter, titles, summaries, headings, bold, lists, and code blocks. Use when user asks to "format markdown", "beautify article", "add formatting", or improve article layout. Outputs to {filename}-formatted.md.
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.
| name | deep-research-pro |
| description | Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required. |
A powerful, self-contained deep research skill that produces thorough, cited reports from multiple web sources. No paid APIs required — uses DuckDuckGo search.
When the user asks for research on any topic, follow this workflow:
Ask 1-2 quick clarifying questions:
If the user says "just research it" — skip ahead with reasonable defaults.
Break the topic into 3-5 research sub-questions. For example:
For EACH sub-question, run the DDG search script:
# Web search
/home/clawdbot/clawd/skills/ddg-search/scripts/ddg "<sub-question keywords>" --max 8
# News search (for current events)
/home/clawdbot/clawd/skills/ddg-search/scripts/ddg news "<topic>" --max 5
Search strategy:
For the most promising URLs, fetch full content:
curl -sL "<url>" | python3 -c "
import sys, re
html = sys.stdin.read()
# Strip tags, get text
text = re.sub('<[^>]+>', ' ', html)
text = re.sub(r'\s+', ' ', text).strip()
print(text[:5000])
"
Read 3-5 key sources in full for depth. Don't just rely on search snippets.
Structure the report as:
# [Topic]: Deep Research Report
*Generated: [date] | Sources: [N] | Confidence: [High/Medium/Low]*
## Executive Summary
[3-5 sentence overview of key findings]
## 1. [First Major Theme]
[Findings with inline citations]
- Key point ([Source Name](url))
- Supporting data ([Source Name](url))
## 2. [Second Major Theme]
...
## 3. [Third Major Theme]
...
## Key Takeaways
- [Actionable insight 1]
- [Actionable insight 2]
- [Actionable insight 3]
## Sources
1. [Title](url) — [one-line summary]
2. ...
## Methodology
Searched [N] queries across web and news. Analyzed [M] sources.
Sub-questions investigated: [list]
Save the full report:
mkdir -p ~/clawd/research/[slug]
# Write report to ~/clawd/research/[slug]/report.md
Then deliver:
"Research the current state of nuclear fusion energy"
"Deep dive into Rust vs Go for backend services in 2026"
"Research the best strategies for bootstrapping a SaaS business"
"What's happening with the US housing market right now?"
When spawning as a sub-agent, include the full research request and context:
sessions_spawn(
task: "Run deep research on [TOPIC]. Follow the deep-research-pro SKILL.md workflow.
Read /home/clawdbot/clawd/skills/deep-research-pro/SKILL.md first.
Goal: [user's goal]
Specific angles: [any specifics]
Save report to ~/clawd/research/[slug]/report.md
When done, wake the main session with key findings.",
label: "research-[slug]",
model: "opus"
)
/home/clawdbot/clawd/skills/ddg-search/scripts/ddg