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deep-research

★ 326 repomlSafeIntermediateGemini Claude
🤖 AI Summary

This skill autonomously plans and executes multi-step web research, then synthesizes findings into a structured report. It handles search, content retrieval, and iterative refinement without manual intervention.

How to Install

Claude Code:
git clone --depth 1 https://github.com/sanjay3290/ai-skills.git && cp ai-skills/skills ~/.claude/skills/deep-research -r

Gemini Deep Research Skill

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

When to Use This Skill

Use this skill when: - Performing market analysis - Conducting competitive landscaping - Creating literature reviews - Doing technical research - Performing due diligence - Need detailed, cited research 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)

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Details

Category AI/ML → ml
Sourcesanjay3290/ai-skills
SKILL.mdView on GitHub →
Repo Stars★ 326
Est. per Skill16 (shared across 20 skills from this repo)
DifficultyIntermediate
Risk LevelSafe

Related Skills

Works Well With

Skills from the same repository — often designed to work together