Deep Research

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

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.