Gemini深度研究技能：AI自主研究工具，自动生成市场分析、技术报告

deep-research by sanjay3290/ai-skills

154 周安装量

169 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/sanjay3290/ai-skills --skill deep-research

AI/机器学习自动化科研工具

🇨🇳中文介绍

Gemini 深度研究技能

运行自主研究任务，规划、搜索、阅读并将信息综合成全面的报告。

要求

Python 3.8+
httpx: pip install -r requirements.txt
GEMINI_API_KEY 环境变量

设置

从 Google AI Studio 获取 Gemini API 密钥

设置环境变量：

export GEMINI_API_KEY=your-api-key-here

或者在技能目录中创建一个 .env 文件。

使用方法

启动一个研究任务

python3 scripts/research.py --query "Research the history of Kubernetes"

使用结构化输出格式

python3 scripts/research.py --query "Compare Python web frameworks" \
  --format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"

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实时流式传输进度

python3 scripts/research.py --query "Analyze EV battery market" --stream

python3 scripts/research.py --query "Research topic" --no-wait

检查正在运行的研究状态

python3 scripts/research.py --status <interaction_id>

python3 scripts/research.py --wait <interaction_id>

从先前的研究继续

python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>

列出最近的研究

python3 scripts/research.py --list

默认 : 人类可读的 Markdown 报告
JSON (--json): 用于程序化使用的结构化数据
原始 (--raw): 未经处理的 API 响应

指标	数值
时间	每个任务 2-10 分钟
成本	每个任务 $2-5 (根据复杂度变化)
令牌使用量	输入约 250k-900k，输出约 60k-80k

市场分析和竞争格局分析
技术文献综述
尽职调查
历史研究和时间线梳理
比较分析 (框架、产品、技术)

用户请求研究 → 运行 --query "..."
告知用户预计时间 (2-10 分钟)
使用 --stream 监控或使用 --status 轮询
返回格式化结果
使用 --continue 进行后续问题跟进

0 : 成功
1 : 错误 (API 错误、配置问题、超时)
130 : 用户取消 (Ctrl+C)

🇺🇸English

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

Get a Gemini API key from Google AI Studio

Set the environment variable:

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

User requests research → Run --query "..."
Inform user of estimated time (2-10 minutes)
Monitor with --stream or poll with --status
Return formatted results
Use --continue for follow-up questions

Exit Codes

0 : Success
1 : Error (API error, config issue, timeout)
130 : Cancelled by user (Ctrl+C)

Weekly Installs

154

Repository

sanjay3290/ai-skills

GitHub Stars

169

First Seen

Jan 22, 2026

Security Audits

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