القائمة
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 ...
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Full codebase audit: architecture health, dependencies, complexity, tech debt. Use when: "audit", "health check", "codebase analysis", "tech debt", "architecture review", "dependency check", "code quality"
Structured brainstorming with multiple approaches and tradeoffs. Use when: "brainstorm", "explore options", "what are my options", "compare approaches", "pros and cons", "which is better", "how should I", "alternatives"
Build error diagnosis and resolution. Use when: "build failed", "compile error", "bundler error", "fix build", "npm error", "type error", "won't compile"
Scaffold a new application with interactive dialogue. Use when: "create", "build me", "make an app", "new project", "scaffold", "generate app", "start new"
Generate a new custom skill from natural language description. Use when: "create skill", "new skill", "make a skill", "add command", "custom workflow", "skill template", "generate skill"
Systematic debugging with hypothesis-driven investigation. Use when: "debug", "not working", "bug", "broken", "error", "unexpected behavior", "fix issue", "investigate"
| name | deep-research |
| description | 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 ... |
| source | https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research |
| risk | safe |
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
Use this skill when:
pip install -r requirements.txtexport GEMINI_API_KEY=your-api-key-here
.env file in the skill directory.python3 scripts/research.py --query "Research the history of Kubernetes"
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
python3 scripts/research.py --query "Analyze EV battery market" --stream
python3 scripts/research.py --query "Research topic" --no-wait
python3 scripts/research.py --status <interaction_id>
python3 scripts/research.py --wait <interaction_id>
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
python3 scripts/research.py --list
--json): Structured data for programmatic use--raw): Unprocessed API response| Metric | Value |
|---|---|
| Time | 2-10 minutes per task |
| Cost | $2-5 per task (varies by complexity) |
| Token usage | ~250k-900k input, ~60k-80k output |
--query "..."--stream or poll with --status--continue for follow-up questions