deep-research
Run Gemini Deep Research from Claude Code. Optimizes prompts for depth and structure, then executes multi-step research via the Interactions API. Use when you need in-depth analysis, competitive landscaping, literature reviews, market research, or any question requiring real-time web research across many sources.
Best use case
deep-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run Gemini Deep Research from Claude Code. Optimizes prompts for depth and structure, then executes multi-step research via the Interactions API. Use when you need in-depth analysis, competitive landscaping, literature reviews, market research, or any question requiring real-time web research across many sources.
Teams using deep-research should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deep-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deep-research Compares
| Feature / Agent | deep-research | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Run Gemini Deep Research from Claude Code. Optimizes prompts for depth and structure, then executes multi-step research via the Interactions API. Use when you need in-depth analysis, competitive landscaping, literature reviews, market research, or any question requiring real-time web research across many sources.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Gemini Deep Research Delegate deep, multi-source research to Gemini's Deep Research agent. This skill optimizes the user's research question into a structured prompt, fires it off via the Interactions API, and returns the full report. ## When to Use - Market research, competitive analysis, due diligence - Literature reviews, state-of-the-art surveys - Historical deep dives on people, movements, or ideas - "What does the current landscape look like for X?" - Any question that benefits from searching 50-100+ web sources - When the user explicitly asks for "deep research" or wants comprehensive analysis ## When NOT to Use - Quick factual lookups (use WebSearch instead) - Code questions or debugging - Tasks requiring real-time interaction or low latency - Questions about the user's local codebase ## Workflow ### Phase 1: Optimize the Research Prompt Before sending to Gemini, transform the user's raw request into a well-structured research prompt. This is critical — Deep Research quality scales with prompt quality. **Prompt optimization checklist:** 1. **Scope the question clearly** — What specifically should be researched? Add boundaries (time period, geography, industry, etc.) 2. **Define the output structure** — Tell Gemini exactly what sections/format you want 3. **Specify depth expectations** — "Include specific examples," "cite primary sources," "compare at least 5 competitors" 4. **Add anti-hallucination guardrails** — "Only include claims with verifiable sources," "distinguish between confirmed facts and speculation" 5. **Request citations** — "Include source URLs for all major claims" **Template for optimized prompts:** ``` Research [TOPIC] with the following scope and structure: ## Scope [Boundaries: time period, geography, domain, exclusions] ## Required Sections 1. [Section with specific instructions] 2. [Section with specific instructions] 3. [Section with specific instructions] ## Output Requirements - Include source URLs for all major claims - Distinguish between confirmed facts and projections/speculation - Include a summary table comparing [key dimensions] - Target depth: [comprehensive / focused overview / executive summary] ``` **Show the user the optimized prompt** before executing. Ask for approval or modifications. ### Phase 2: Execute the Research Use the bundled script to run the research. The script handles the async polling loop. **For research that should run while the user continues working:** ```bash # Start async — returns interaction ID immediately bash "/Users/charliedeist/Desktop/New Root Docs/.claude/skills/deep-research/scripts/deep-research.sh" --start "OPTIMIZED_PROMPT" ``` Then later: ```bash # Poll for results bash "/Users/charliedeist/Desktop/New Root Docs/.claude/skills/deep-research/scripts/deep-research.sh" --poll INTERACTION_ID ``` **For research where we wait for results (typical):** Run the script in the background using Bash tool's `run_in_background` parameter, since research takes 2-15 minutes: ```bash bash "/Users/charliedeist/Desktop/New Root Docs/.claude/skills/deep-research/scripts/deep-research.sh" "OPTIMIZED_PROMPT" ``` **Important execution notes:** - Deep Research takes 2-15 minutes. ALWAYS use `run_in_background: true` or `--start` mode - Tell the user the estimated wait time and that they can keep working - The script polls every 10 seconds and prints status to stderr - Results (the report text) go to stdout ### Phase 3: Present Results When results arrive: 1. **Save the raw report** to a markdown file in the relevant project directory (e.g., `references/deep-research-TOPIC-YYYY-MM-DD.md`) 2. **Summarize key findings** for the user in chat — don't just dump the whole report 3. **Flag any gaps** where Gemini couldn't find information or noted uncertainty 4. **Suggest next steps** if the research opens up new questions ## API Details - **Agent:** `deep-research-pro-preview-12-2025` - **API:** Gemini Interactions API (REST) - **Auth:** `GEMINI_API_KEY` env var (set in `~/.zshrc`) - **Cost:** ~$2-5 per research task depending on depth - **Max time:** 60 minutes (most complete in 5-15 minutes) - **Script location:** `.claude/skills/deep-research/scripts/deep-research.sh` ## Example Prompt Optimizations **User says:** "Research competency-based education trends" **Optimized to:** ``` Research the current state and emerging trends in Competency-Based Education (CBE) in K-12 and higher education in the United States, focusing on 2024-2026. ## Required Sections 1. Executive Summary (3-5 key takeaways) 2. Current Adoption — Which states, districts, and institutions have adopted CBE? Include specific programs and scale. 3. Policy Landscape — Federal and state policy developments affecting CBE (ESSA flexibility, accreditation changes, transcript reform) 4. Technology & Assessment — Tools, platforms, and assessment approaches enabling CBE at scale 5. Criticism & Challenges — Major objections, implementation barriers, equity concerns 6. Key Players — Organizations, thought leaders, and companies driving CBE forward (include a comparison table) 7. Outlook — Where is CBE heading in the next 2-3 years? ## Output Requirements - Include source URLs for all major claims - Distinguish confirmed adoption from pilot programs - Include at least one data table comparing state-level CBE policies - Target depth: comprehensive ``` **User says:** "What's the deal with micro-schools?" **Optimized to:** ``` Research the micro-school movement in the United States as of early 2026. ## Required Sections 1. Definition & Landscape — What counts as a micro-school? How many exist? What's the growth trajectory? 2. Major Networks & Operators — Prenda, Acton Academy, KaiPod, and others. Compare their models, scale, and funding. 3. Funding & Business Models — How are micro-schools funded? (Tuition, ESAs, hybrid models, venture capital) 4. Regulatory Environment — How do states classify and regulate micro-schools? 5. Parent & Student Demographics — Who is choosing micro-schools and why? 6. Outcomes & Evidence — What do we know about academic and social outcomes? 7. Criticism & Risks — Accountability concerns, equity gaps, sustainability questions ## Output Requirements - Include source URLs for all major claims - Include a comparison table of major micro-school networks (model, # of locations, grade levels, cost, funding) - Separate established facts from projections - Target depth: comprehensive ```
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