davila7/deep-research
cli-tool/components/skills/ai-research/deep-research/SKILL.md
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
$ install --global
skillsauthnpx skillsauth add davila7/claude-code-templates deep-researchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
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OWASP Dep-Check
50%
Last scanned: Apr 29, 2026, 2:24 PM27.5s1 file scanned
SKILL.md
- name:
- deep-research
- description:
- Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
- risk:
- safe
- source:
- https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research
- date_added:
- 2026-02-27
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
- Get a Gemini API key from Google AI Studio
- Set the environment variable:
Or create aexport GEMINI_API_KEY=your-api-key-here.envfile 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
--streamor poll with--status - Return formatted results
- Use
--continuefor follow-up questions
Exit Codes
- 0: Success
- 1: Error (API error, config issue, timeout)
- 130: Cancelled by user (Ctrl+C)
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