/deep-research

name: deep-research
description: This skill should be used when conducting comprehensive research on any topic using the OpenAI Deep Research API. It automates prompt enhancement through interactive clarifying questions, saves research parameters, and executes deep research with web search capabilities. Use when the user asks for in-depth analysis, investigation, research summaries, or topic exploration.
disable-model-invocation: true

Deep Research Skill

Purpose

This skill enables comprehensive, internet-enabled research on any topic using OpenAI's Deep Research API (o4-mini-deep-research model). It intelligently enhances user research prompts through interactive clarifying questions, ensures research parameters are saved for reproducibility, and executes deep research with full web search capabilities.

When to Use This Skill

Trigger this skill when:

• User requests research on a specific topic
• User asks for analysis, investigation, or comprehensive information gathering
• User wants exploration of a subject with web search and reasoning
• User provides a brief research query that could be refined
• User wants to understand current state, trends, or comparisons in a field

Example user requests:

• "Research the most effective open-source RAG solutions with high benchmark performance"
• "What are the latest AI developments in 2025?"
• "I need a comprehensive analysis of distributed database systems"
• "Find best practices for implementing vector search"
• "Investigate how AI is impacting the software engineering industry"

Workflow Overview

User Input
    ↓
Assessment: Prompt too brief?
    ↓
YES → Ask Enhancement Questions → Collect Answers
    ↓                               ↓
    └───────→ Construct Enhanced Prompt ←──┘
                    ↓
            Save to Timestamped File
                    ↓
            Execute deep_research.py
                    ↓
            Output Report + Sources
                    ↓
            Present to User

How Claude Should Use This Skill

**Important for Token Efficiency:** Deep research takes 10-20 minutes to complete. The skill is designed to run synchronously (blocking) without intermediate status checks. This approach minimizes token usage during the wait. Claude should: 1. Start the research 2. Wait for completion (subprocess blocks automatically) 3. Present final results once complete

No need for periodic polling or status updates during execution.

Step 1: Accept Research Request

Receive the user's research prompt. This can range from brief ("Latest AI trends") to highly detailed ("Impact of language models on developer productivity with focus on 2024-2025").

Step 2: Execute the Orchestration Script

Run the skill's main orchestration script with the user's research prompt:

python3 scripts/run_deep_research.py "Your research prompt here"

The script is located at `scripts/run_deep_research.py` within the skill's installation.

Step 3: Script Execution Flow

The script automatically:

1. **Assesses prompt completeness**: Checks if prompt is too brief or generic (< 15 words or starts with "what is", "how to", etc.)

2. **Asks clarifying questions** (if needed):

• Presents 2-3 focused questions relevant to the research type
• Detects if research is technical or general based on keywords
• Allows users to select from predefined options (1-4) or provide custom text
• Questions cover: Scope/Timeframe, Depth level, Focus areas

3. **Enhances the prompt**: Combines original prompt with user's answers into structured research parameters

4. **Saves prompt file**: Writes enhanced prompt to `research_prompt_YYYYMMDD_HHMMSS.txt` for reproducibility

5. **Executes deep research**: Runs the core `deep_research.py` script with:

• Model: o4-mini-deep-research (configurable via `--model`)
• Timeout: 1800 seconds / 30 minutes (configurable via `--timeout`)
• Tools: Web search enabled by default

Step 4: Present Results to User

The script automatically:

• **Saves markdown file**: Research report with sources saved to `research_report_YYYYMMDD_HHMMSS.md`
• **Prints to terminal**: Complete research report with markdown formatting
• **Lists web sources**: Numbered URLs referenced in the research
• **Confirms completion**: Path where research files were saved

**Token Efficiency Note**: Deep research takes 10-20 minutes. The script runs synchronously (blocking) without intermediate polling, minimizing token usage during the wait.

Bundled Resources

Scripts

`scripts/run_deep_research.py` (Main Entry Point)

The orchestration script that handles:

• Prompt quality assessment
• Interactive enhancement questions (with smart detection for technical vs. general research)
• Prompt saving and timestamping
• Execution of core deep research

**Key Features:**

• Smart enhancement: Only asks questions if prompt is brief/generic
• Template-based questions: Different question sets for technical vs. general research
• Flexible input: Numbered options + custom text input
• Error handling: Helpful messages if deep_research.py is not found

**Available options:**

python3 run_deep_research.py <prompt> [OPTIONS]
  --no-enhance              Skip enhancement questions
  --model <model>           Model to use (default: o4-mini-deep-research)
  --timeout <seconds>       Timeout in seconds (default: 1800)
  --output-dir <path>       Where to save prompt file

`assets/deep_research.py`

Core script that interfaces with OpenAI's Deep Research API. Handles:

• API authentication via OPENAI_API_KEY
• Request creation and execution
• **Automatic markdown saving**: Saves timestamped report files by default
• Output formatting (report + sources with metadata)
• Error handling and retries

**New command-line options:**

--output-file <path>      Custom output file path
--no-save                 Disable automatic markdown saving

References

`references/workflow.md`