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deep-research

@w95 · 收录于 1 周前

Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.

场景市场调研竞品调研文献综述技术调研

适合你，如果需要快速获取结构化的深度研究报告

/ 下载安装
deep-research.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add w95/awesome-claude-corporate-skills/deep-research
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- w95/awesome-claude-corporate-skills/deep-research
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify w95/awesome-claude-corporate-skills/deep-research
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
92GitHub stars
~570上下文体积 · 单文件
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怎么用

技能原文 SKILL.md作者撰写 · MIT · 78dbc7c

Gemini Deep Research Skill

Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.

Requirements
  • Python 3.8+
  • httpx: pip install -r requirements.txt
  • GEMINI_API_KEY environment variable
Setup
  1. Get a Gemini API key from Google AI Studio
  2. Set the environment variable: ```bash export GEMINI_API_KEY=your-api-key-here ``` Or create a .env file in the skill directory.
Usage
Start a research task
python3 scripts/research.py --query "Research the history of Kubernetes"
With structured output format
python3 scripts/research.py --query "Compare Python web frameworks" \
  --format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
Stream progress in real-time
python3 scripts/research.py --query "Analyze EV battery market" --stream
Start without waiting
python3 scripts/research.py --query "Research topic" --no-wait
Check status of running research
python3 scripts/research.py --status <interaction_id>
Wait for completion
python3 scripts/research.py --wait <interaction_id>
Continue from previous research
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
List recent research
python3 scripts/research.py --list
Output Formats
  • Default: Human-readable markdown report
  • JSON (--json): Structured data for programmatic use
  • Raw (--raw): Unprocessed API response
Cost & Time

| Metric | Value | |--------|-------| | Time | 2-10 minutes per task | | Cost | $2-5 per task (varies by complexity) | | Token usage | ~250k-900k input, ~60k-80k output |

Best Use Cases
  • Market analysis and competitive landscaping
  • Technical literature reviews
  • Due diligence research
  • Historical research and timelines
  • Comparative analysis (frameworks, products, technologies)
Workflow
  1. User requests research → Run --query "..."
  2. Inform user of estimated time (2-10 minutes)
  3. Monitor with --stream or poll with --status
  4. Return formatted results
  5. Use --continue for follow-up questions
Exit Codes
  • 0: Success
  • 1: Error (API error, config issue, timeout)
  • 130: Cancelled by user (Ctrl+C)
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

评论

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