Google Deep Research

Google Deep Research Skill

This skill provides access to Google's Deep Research API, which runs comprehensive research queries server-side for 5-20 minutes, searching multiple web sources and synthesizing findings into detailed reports.

When to Use This Skill

Use Google Deep Research when:

• User explicitly asks for "deep research" on a topic
• Research requires comprehensive multi-source investigation
• Topic needs 10+ web sources synthesized
• User wants a detailed report (5,000-40,000 chars)
• Standard web searches would be insufficient

Do NOT use for:

• Quick factual lookups (use WebSearch instead)
• Single-source questions
• Time-sensitive queries needing instant answers

API Configuration

Endpoint

POST https://generativelanguage.googleapis.com/v1beta/interactions

Required Headers

Content-Type: application/json
x-goog-api-key: <API_KEY>

Request Payload

{
  "input": "Your detailed research query here",
  "agent": "deep-research-pro-preview-12-2025",
  "background": true
}

Response (Success)

{
  "id": "v1_ChdXXXXXXXXX...",
  "status": "pending"
}

Workflow

Step 1: Submit Research Query

Use Python to submit the query:

import requests
import json

API_KEY = "<user's API key>"
URL = "https://generativelanguage.googleapis.com/v1beta/interactions"

headers = {
    "Content-Type": "application/json",
    "x-goog-api-key": API_KEY
}

payload = {
    "input": "Research query here - be specific and detailed",
    "agent": "deep-research-pro-preview-12-2025",
    "background": True
}

response = requests.post(URL, headers=headers, json=payload)
data = response.json()
interaction_id = data.get("id")
print(f"Started research: {interaction_id}")

Step 2: Poll for Completion

Research takes 5-20 minutes. Poll the status:

import time

def check_status(interaction_id, api_key):
    url = f"https://generativelanguage.googleapis.com/v1beta/interactions/{interaction_id}"
    headers = {"x-goog-api-key": api_key}

    while True:
        resp = requests.get(url, headers=headers)
        data = resp.json()
        status = data.get("status") or data.get("state")

        if status == "completed":
            # Get the report text
            outputs = data.get("outputs", [])
            if outputs:
                return outputs[-1].get("text", "")
            return None
        elif status in ["failed", "cancelled"]:
            return None
        else:
            print(f"Status: {status} - waiting...")
            time.sleep(30)

Step 3: Save Results

Save the report to a markdown file with metadata:

from datetime import datetime
from pathlib import Path

def save_report(name, report_text, output_dir="research_results"):
    Path(output_dir).mkdir(parents=True, exist_ok=True)
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"{name}_{timestamp}.md"
    filepath = Path(output_dir) / filename

    with open(filepath, "w") as f:
        f.write(f"# Deep Research Report: {name}\n\n")
        f.write(f"**Generated:** {datetime.now().isoformat()}\n\n")
        f.write("---\n\n")
        f.write(report_text)

    return filepath

Query Design Best Practices

Good Queries

Be specific with names, locations, and what you want:

Research solar panel installations at Norwegian shopping centers including
Citycon properties, Steen & Strøm centers, and AMFI centers. For each,
find: building name, address, installation size (kWp), installer company,
and completion year. Search ESG reports, press releases, and sustainability reports.

Bad Queries

Too vague:

Tell me about solar panels in Norway

Include in Your Queries

1. Specific entities - Company names, building names, locations
2. Data fields needed - Address, size, date, installer, etc.
3. Source hints - "Search ESG reports, press releases, LinkedIn..."
4. Geographic scope - Cities, regions, countries
5. Time frame - "2020-2024", "recent", etc.

Quota Management

Tier 1 Limits

• ~3 Deep Research queries per API key per day
• Rate limit resets at midnight Pacific Time

Multi-Key Strategy

If you have multiple API keys, rotate through them:

API_KEYS = [
    "AIzaSy...",  # Key 1
    "AIzaSy...",  # Key 2
    "AIzaSy...",  # Key 3
]

for key in API_KEYS:
    response = submit_query(query, key)
    if response.status_code == 200:
        break
    elif response.status_code == 429:
        continue  # Try next key

Tracking Interactions

Maintain a JSON log to track all research jobs:

{
  "started": "2025-01-01T00:00:00",
  "interactions": [
    {
      "name": "research_topic_name",
      "id": "v1_ChdXXXX...",
      "started_at": "2025-01-01T00:00:00",
      "status": "completed",
      "api_key_used": "...last8chars",
      "output_file": "path/to/report.md",
      "report_length": 25000
    }
  ]
}

Error Handling

Common Errors

Invalid API Key Response

{
  "error": {
    "code": 400,
    "message": "API key not valid",
    "status": "INVALID_ARGUMENT"
  }
}

Getting API Keys

1. Go to https://aistudio.google.com/apikey
2. Create a new API key
3. Store securely (environment variable or secure storage)

Example: Full Research Script

See ~/.claude/skills/google-deep-research/deep_research.py for a complete implementation.

Output Format

Deep Research reports typically include:

• Executive summary
• Detailed findings organized by topic
• Source citations with URLs
• Data tables where applicable
• Recommendations or conclusions

Reports range from 5,000 to 40,000 characters depending on topic complexity.