gemini-deep-research
Run autonomous multi-step research with Google's Gemini Deep Research Agent: kick off a query, poll progress, and collect a cited report for market analysis or literature reviews.
#research
Install
npx skills add https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/gemini-deep-research
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install sickn33-agentic-awesome-skills@llmmart
git clone https://github.com/sickn33/agentic-awesome-skills.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole sickn33/agentic-awesome-skills collection as a plugin from our marketplace. Git is the plain clone.
README
Gemini Deep Research Skill
Execute autonomous multi-step research tasks using Google's Gemini Deep Research Agent. Unlike standard LLM queries that respond in seconds, Deep Research is an "analyst-in-a-box" that plans, searches, reads, and synthesizes information into comprehensive, cited reports.
Overview
The Deep Research Agent (deep-research-pro-preview-12-2025) powered by Gemini 3 Pro:
- Plans research strategy based on your query
- Searches the web and analyzes sources
- Reads and extracts relevant information
- Iterates through multiple search/read cycles
- Outputs detailed, cited reports
This process takes 2-10 minutes but produces thorough analysis that would take a human researcher hours.
Installation
# Navigate to skill directory
cd skills/deep-research
# Install dependencies
pip install -r requirements.txt
# Set up API key
cp .env.example .env
# Edit .env and add your GEMINI_API_KEY
Getting a Gemini API Key
- Go to Google AI Studio
- Click "Get API key"
- Create a new key or use an existing one
- Copy the key to your
.envfile
Quick Start
# Basic research query
python3 scripts/research.py --query "Research the competitive landscape of cloud providers in 2024"
# Stream progress in real-time
python3 scripts/research.py --query "Compare React, Vue, and Angular frameworks" --stream
# Get structured JSON output
python3 scripts/research.py --query "Analyze the EV market" --json
Commands
--query / -q
Start a new research task.
# Basic query
python3 scripts/research.py -q "Research the history of containerization"
# With output format specification
python3 scripts/research.py -q "Compare database solutions" \
--format "1. Executive Summary\n2. Comparison Table\n3. Pros/Cons\n4. Recommendations"
# Start without waiting for results
python3 scripts/research.py -q "Research topic" --no-wait
--stream
Stream research progress in real-time. Shows thinking steps and builds the report as it's generated.
python3 scripts/research.py -q "Analyze market trends" --stream
--status / -s
Check the status of a running research task.
python3 scripts/research.py --status abc123xyz
--wait / -w
Wait for a specific research task to complete.
python3 scripts/research.py --wait abc123xyz
--continue
Continue a conversation from previous research. Useful for follow-up questions.
# First, run initial research
python3 scripts/research.py -q "Research Kubernetes architecture"
# Output: Interaction ID: abc123xyz
# Then ask follow-up
python3 scripts/research.py -q "Elaborate on the networking section" --continue abc123xyz
--list / -l
List recent research tasks from local history.
python3 scripts/research.py --list
python3 scripts/research.py --list --limit 20
Output Options
| Flag | Description |
|---|---|
| (default) | Human-readable markdown report |
--json / -j |
Structured JSON output |
--raw / -r |
Raw API response |
Configuration
Environment Variables
| Variable | Default | Description |
|---|---|---|
GEMINI_API_KEY |
(required) | Your Google Gemini API key |
DEEP_RESEARCH_TIMEOUT |
600 |
Max wait time in seconds |
DEEP_RESEARCH_POLL_INTERVAL |
10 |
Seconds between status polls |
DEEP_RESEARCH_CACHE_DIR |
~/.cache/deep-research |
Local history cache directory |
.env File
GEMINI_API_KEY=your-api-key-here
DEEP_RESEARCH_TIMEOUT=600
DEEP_RESEARCH_POLL_INTERVAL=10
Cost & Performance
Estimated Costs
Deep Research uses a pay-as-you-go model based on token usage:
| Task Type | Search Queries | Input Tokens | Output Tokens | Estimated Cost |
|---|---|---|---|---|
| Standard | ~80 | ~250k (50-70% cached) | ~60k | $2-3 |
| Complex | ~160 | ~900k (50-70% cached) | ~80k | $3-5 |
Time Expectations
- Simple queries: 2-5 minutes
- Complex analysis: 5-10 minutes
- Maximum: 60 minutes (API limit)
Use Cases
Market Analysis
python3 scripts/research.py -q "Analyze the competitive landscape of \
EV battery manufacturers, including market share, technology, and supply chain"
Technical Research
python3 scripts/research.py -q "Compare Rust vs Go for building \
high-performance backend services" \
--format "1. Performance Benchmarks\n2. Memory Safety\n3. Ecosystem\n4. Learning Curve"
Due Diligence
python3 scripts/research.py -q "Research Company XYZ: recent news, \
financial performance, leadership changes, and market position"
Literature Review
python3 scripts/research.py -q "Review recent developments in \
large language model efficiency and optimization techniques"
Error Handling
| Error | Cause | Solution |
|---|---|---|
GEMINI_API_KEY not set |
Missing API key | Set in .env or environment |
API error 429 |
Rate limited | Wait and retry |
Research timed out |
Task took too long | Simplify query or increase timeout |
Failed to parse result |
Unexpected response | Use --raw to see actual output |
Exit Codes
| Code | Meaning |
|---|---|
| 0 | Success |
| 1 | Error (API, config, timeout) |
| 130 | Cancelled by user (Ctrl+C) |
Architecture
┌─────────────────┐ ┌──────────────────────┐
│ CLI Script │──────│ DeepResearchClient │
│ (research.py) │ │ │
└─────────────────┘ └──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Gemini Deep │
│ Research API │
│ │
│ POST /interactions │
│ GET /interactions │
└──────────────────────┘
│
▼
┌──────────────────────┐
│ HistoryManager │
│ (~/.cache/deep- │
│ research/) │
└──────────────────────┘
Safety & Privacy
- Read-only: This skill only reads/researches; no file modifications
- No secrets in queries: Avoid including sensitive data in research queries
- Source verification: Always verify citations in the output
- Cost awareness: Each task costs $2-5; be mindful of usage
Limitations
- No custom tools: Cannot use MCP or function calling
- No structured output enforcement: JSON formatting relies on prompt engineering
- Web-only research: Cannot access private/authenticated sources
- 60-minute max: Very complex tasks may time out
References
Skill manifest
Gemini Deep Research Skill
When to Use
- Use when a question needs autonomous multi-step research with cited sources (market analysis, literature reviews, competitive scans)
- Use when you want to start a Gemini Deep Research run, poll its progress, and collect the final report
- Use when a quick web search is not enough and a structured, source-grounded report is required
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive 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.
Safety Gate
Before starting a research job, show the user the exact query, the fact that it will be sent to Google's Gemini service, the expected cost range, and the output destination. Start a job only after explicit approval. Do not include private workspace material, credentials, personal data, or confidential customer information in a query.
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)
Limitations
- Each research job is a paid, third-party API request; costs and availability can change, and the listed estimate is not a spending authorization.
- Reports may contain incomplete, stale, or incorrect citations. Verify consequential claims against primary sources.
- This skill cannot guarantee that a prompt is safe to disclose; redact proprietary or personal material before requesting user approval.
- An API key must remain local and must never be committed, printed, or sent in a query.
Files (agentic-awesome-skills)
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scripts
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research.py 23.6 KB
#!/usr/bin/env python3 """ Gemini Deep Research Skill - Execute autonomous multi-step research tasks. Uses Google's Deep Research Agent for comprehensive, cited research reports. Research tasks take 2-10 minutes but produce detailed analysis. """ import argparse import asyncio import json import os import re import sys from datetime import datetime, timezone from pathlib import Path from typing import Any, AsyncIterator, Callable, Dict, List, Optional try: import httpx except ImportError: print("Error: httpx not installed. Run: pip install httpx", file=sys.stderr) sys.exit(1) try: from dotenv import load_dotenv load_dotenv() except ImportError: pass # dotenv is optional class DeepResearchError(Exception): """Custom exception for Deep Research API errors.""" pass class HistoryManager: """Manage local research history cache.""" def __init__(self, cache_dir: Optional[str] = None): default_dir = os.path.expanduser("~/.cache/deep-research") self.cache_dir = Path(cache_dir or os.getenv("DEEP_RESEARCH_CACHE_DIR", default_dir)) self.history_file = self.cache_dir / "history.json" self._ensure_cache_dir() def _ensure_cache_dir(self): """Create cache directory if it doesn't exist.""" self.cache_dir.mkdir(parents=True, exist_ok=True) def _load_history(self) -> Dict: """Load history from file.""" if self.history_file.exists(): try: return json.loads(self.history_file.read_text()) except (json.JSONDecodeError, IOError): return {"interactions": []} return {"interactions": []} def _save_history(self, history: Dict): """Save history to file.""" self.history_file.write_text(json.dumps(history, indent=2)) def add_interaction(self, interaction_id: str, query: str, status: str = "started"): """Add or update an interaction in history.""" history = self._load_history() # Check if interaction already exists for item in history["interactions"]: if item["id"] == interaction_id: item["status"] = status item["updated_at"] = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") self._save_history(history) return # Add new interaction history["interactions"].insert(0, { "id": interaction_id, "query": query[:200] + "..." if len(query) > 200 else query, "started_at": datetime.now(timezone.utc).isoformat().replace("+00:00", "Z"), "status": status }) # Keep only last 50 interactions history["interactions"] = history["interactions"][:50] self._save_history(history) def update_status(self, interaction_id: str, status: str): """Update the status of an interaction.""" history = self._load_history() for item in history["interactions"]: if item["id"] == interaction_id: item["status"] = status item["updated_at"] = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") if status == "completed": item["completed_at"] = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") break self._save_history(history) def get_recent(self, limit: int = 10) -> List[Dict]: """Get recent interactions.""" history = self._load_history() return history["interactions"][:limit] def get_interaction(self, interaction_id: str) -> Optional[Dict]: """Get a specific interaction by ID.""" history = self._load_history() for item in history["interactions"]: if item["id"] == interaction_id: return item return None class DeepResearchClient: """Client for Gemini Deep Research API.""" BASE_URL = "https://generativelanguage.googleapis.com/v1beta/interactions" AGENT = "deep-research-pro-preview-12-2025" def __init__(self, api_key: Optional[str] = None): self.api_key = api_key or os.getenv("GEMINI_API_KEY") if not self.api_key: raise DeepResearchError( "GEMINI_API_KEY not set. Set it in .env or environment variables." ) self._client: Optional[httpx.AsyncClient] = None self.timeout = int(os.getenv("DEEP_RESEARCH_TIMEOUT", "600")) self.poll_interval = int(os.getenv("DEEP_RESEARCH_POLL_INTERVAL", "10")) self.history = HistoryManager() async def _get_client(self) -> httpx.AsyncClient: """Get or create the HTTP client.""" if self._client is None or self._client.is_closed: self._client = httpx.AsyncClient(timeout=60.0) return self._client async def close(self): """Close the HTTP client.""" if self._client and not self._client.is_closed: await self._client.aclose() def _build_prompt(self, query: str, format_spec: Optional[str] = None) -> str: """Build the research prompt with optional format specification.""" prompt = query if format_spec: prompt += f"\n\nFormat the output with the following structure:\n{format_spec}" return prompt async def start_research( self, query: str, format_spec: Optional[str] = None, previous_interaction_id: Optional[str] = None ) -> str: """ Start a research task. Args: query: The research query format_spec: Optional output format specification previous_interaction_id: Optional ID to continue from previous research Returns: Interaction ID for polling results """ prompt = self._build_prompt(query, format_spec) client = await self._get_client() payload: Dict[str, Any] = { "input": prompt, "agent": self.AGENT, "background": True } if previous_interaction_id: payload["previous_interaction_id"] = previous_interaction_id try: response = await client.post( self.BASE_URL, headers={ "Content-Type": "application/json", "x-goog-api-key": str(self.api_key) }, json=payload ) if response.status_code != 200: error_text = response.text raise DeepResearchError(f"API error {response.status_code}: {error_text}") data = response.json() interaction_id = data.get("id") or data.get("name", "").split("/")[-1] if not interaction_id: raise DeepResearchError("No interaction ID returned from API") # Track in history self.history.add_interaction(interaction_id, query, "started") return interaction_id except httpx.HTTPError as e: raise DeepResearchError(f"HTTP error: {e}") async def get_status(self, interaction_id: str) -> Dict[str, Any]: """ Get the status of a research task. Returns: Dict with 'status' and optionally 'result' or 'error' """ client = await self._get_client() try: response = await client.get( f"{self.BASE_URL}/{interaction_id}", headers={"x-goog-api-key": str(self.api_key)}, timeout=30.0 ) if response.status_code != 200: return {"status": "error", "error": f"API error: {response.status_code}"} data = response.json() status = data.get("status", "unknown") if status == "completed": outputs = data.get("outputs", []) if outputs: text = outputs[-1].get("text", "") return {"status": "completed", "result": text, "raw": data} return {"status": "completed", "result": None, "raw": data} elif status == "failed": return {"status": "failed", "error": data.get("error", "Unknown error")} else: return {"status": status, "raw": data} except httpx.HTTPError as e: return {"status": "error", "error": str(e)} async def wait_for_completion( self, interaction_id: str, timeout: Optional[int] = None, poll_interval: Optional[int] = None, progress_callback: Optional[Callable[[int, float, str], None]] = None ) -> Dict[str, Any]: """ Wait for research to complete with polling. Args: interaction_id: The interaction ID to poll timeout: Maximum wait time in seconds poll_interval: Seconds between polls progress_callback: Optional callback for progress updates Returns: Dict with research result or error """ timeout = timeout or self.timeout poll_interval = poll_interval or self.poll_interval start_time = asyncio.get_event_loop().time() poll_count = 0 while True: elapsed = asyncio.get_event_loop().time() - start_time if elapsed > timeout: self.history.update_status(interaction_id, "timeout") return {"status": "timeout", "error": f"Research timed out after {timeout}s"} result = await self.get_status(interaction_id) poll_count += 1 if progress_callback: progress_callback(poll_count, elapsed, result.get("status", "unknown")) if result["status"] == "completed": self.history.update_status(interaction_id, "completed") return result elif result["status"] in ["failed", "error"]: self.history.update_status(interaction_id, "failed") return result await asyncio.sleep(poll_interval) async def stream_research( self, query: str, format_spec: Optional[str] = None ) -> AsyncIterator[Dict[str, Any]]: """ Stream research progress in real-time. Yields: Dict with event type and content """ prompt = self._build_prompt(query, format_spec) client = await self._get_client() try: async with client.stream( "POST", f"{self.BASE_URL}?alt=sse", headers={ "Content-Type": "application/json", "x-goog-api-key": str(self.api_key) }, json={ "input": prompt, "agent": self.AGENT, "background": True, "stream": True, "agent_config": { "type": "deep-research", "thinking_summaries": "auto" } }, timeout=None # No timeout for streaming ) as response: interaction_id = None buffer = "" async for chunk in response.aiter_text(): buffer += chunk # Parse SSE events while "\n\n" in buffer: event_str, buffer = buffer.split("\n\n", 1) if not event_str.strip(): continue # Parse SSE format data_line = None for line in event_str.split("\n"): if line.startswith("data: "): data_line = line[6:] break if not data_line: continue try: event = json.loads(data_line) except json.JSONDecodeError: continue event_type = event.get("event_type", "") if event_type == "interaction.start": interaction_id = event.get("interaction", {}).get("id") if interaction_id: self.history.add_interaction(interaction_id, query, "streaming") yield {"type": "start", "interaction_id": interaction_id} elif event_type == "content.delta": delta = event.get("delta", {}) delta_type = delta.get("type") if delta_type == "text": yield {"type": "text", "content": delta.get("text", "")} elif delta_type == "thought_summary": content = delta.get("content", {}) yield {"type": "thought", "content": content.get("text", "")} elif event_type == "interaction.complete": if interaction_id: self.history.update_status(interaction_id, "completed") yield {"type": "complete"} elif event_type == "error": if interaction_id: self.history.update_status(interaction_id, "failed") yield {"type": "error", "error": event.get("error", "Unknown error")} except httpx.HTTPError as e: yield {"type": "error", "error": str(e)} def parse_result(self, text: str) -> Optional[Dict]: """ Parse research result text into structured data if JSON. Args: text: Raw text output from Gemini Returns: Parsed JSON data or None if not JSON """ if not text: return None # Try direct JSON parsing try: return json.loads(text) except json.JSONDecodeError: pass # Try extracting JSON from markdown code blocks json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL) if json_match: try: return json.loads(json_match.group(1)) except json.JSONDecodeError: pass # Try finding JSON object pattern json_match = re.search(r'\{[^{}]*"[^"]+"\s*:[^{}]*\}', text, re.DOTALL) if json_match: try: return json.loads(json_match.group(0)) except json.JSONDecodeError: pass return None def print_progress(poll_count: int, elapsed: float, status: str): """Print progress update during polling.""" mins = int(elapsed // 60) secs = int(elapsed % 60) print(f"\r[{mins:02d}:{secs:02d}] Poll #{poll_count} - Status: {status}", end="", flush=True) async def cmd_research(args): """Execute a research query.""" client = DeepResearchClient() try: if args.stream: # Streaming mode print(f"Starting streaming research...\n") full_text = "" async for event in client.stream_research(args.query, args.format): if event["type"] == "start": print(f"Interaction ID: {event['interaction_id']}\n") elif event["type"] == "thought": print(f"\n[Thinking] {event['content']}\n", file=sys.stderr) elif event["type"] == "text": print(event["content"], end="", flush=True) full_text += event["content"] elif event["type"] == "complete": print("\n\n[Research Complete]") elif event["type"] == "error": print(f"\n[Error] {event['error']}", file=sys.stderr) return 1 # Output JSON if requested if args.json and full_text: parsed = client.parse_result(full_text) if parsed: print("\n\n--- Parsed JSON ---") print(json.dumps(parsed, indent=2)) else: # Polling mode previous_id = args.continue_from if hasattr(args, 'continue_from') else None print(f"Starting research task...") interaction_id = await client.start_research( args.query, args.format, previous_id ) print(f"Interaction ID: {interaction_id}") print(f"Estimated time: 2-10 minutes\n") if args.no_wait: print(f"Research started. Check status with: --status {interaction_id}") return 0 print("Waiting for completion (Ctrl+C to cancel)...") result = await client.wait_for_completion( interaction_id, progress_callback=print_progress ) print() # New line after progress if result["status"] == "completed": text = result.get("result", "") if args.json: parsed = client.parse_result(text) if parsed: print(json.dumps(parsed, indent=2)) else: print(json.dumps({"text": text}, indent=2)) elif args.raw: print(json.dumps(result.get("raw", {}), indent=2)) else: print("\n--- Research Result ---\n") print(text) return 0 else: print(f"\nResearch failed: {result.get('error', 'Unknown error')}") return 1 except DeepResearchError as e: print(f"Error: {e}", file=sys.stderr) return 1 except KeyboardInterrupt: print("\n\nCancelled by user.") return 130 finally: await client.close() async def cmd_status(args): """Check status of a research task.""" client = DeepResearchClient() try: result = await client.get_status(args.interaction_id) if args.json: print(json.dumps(result, indent=2)) else: print(f"Status: {result['status']}") if result["status"] == "completed": text = result.get("result", "") if text: print(f"\n--- Result Preview ---\n{text[:500]}...") elif result["status"] in ["failed", "error"]: print(f"Error: {result.get('error', 'Unknown')}") return 0 if result["status"] != "error" else 1 except DeepResearchError as e: print(f"Error: {e}", file=sys.stderr) return 1 finally: await client.close() async def cmd_wait(args): """Wait for a research task to complete.""" client = DeepResearchClient() try: print(f"Waiting for {args.interaction_id}...") result = await client.wait_for_completion( args.interaction_id, progress_callback=print_progress ) print() if result["status"] == "completed": text = result.get("result", "") if args.json: parsed = client.parse_result(text) if parsed: print(json.dumps(parsed, indent=2)) else: print(json.dumps({"text": text}, indent=2)) else: print("\n--- Research Result ---\n") print(text) return 0 else: print(f"Research failed: {result.get('error', 'Unknown error')}") return 1 except DeepResearchError as e: print(f"Error: {e}", file=sys.stderr) return 1 except KeyboardInterrupt: print("\n\nCancelled by user.") return 130 finally: await client.close() async def cmd_list(args): """List recent research tasks.""" history = HistoryManager() interactions = history.get_recent(args.limit) if not interactions: print("No research history found.") return 0 if args.json: print(json.dumps(interactions, indent=2)) else: print(f"Recent research tasks (last {len(interactions)}):\n") for item in interactions: status_icon = { "completed": "[ok]", "failed": "[!!]", "started": "[..]", "streaming": "[>>]", "timeout": "[to]" }.get(item.get("status", ""), "[??]") print(f"{status_icon} {item['id'][:12]}...") print(f" Query: {item['query'][:60]}{'...' if len(item['query']) > 60 else ''}") print(f" Started: {item.get('started_at', 'N/A')}") if item.get("completed_at"): print(f" Completed: {item['completed_at']}") print() return 0 def main(): parser = argparse.ArgumentParser( description="Gemini Deep Research - Autonomous multi-step research agent", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: # Start a research task and wait for results %(prog)s --query "Research the history of Kubernetes" # With structured output format %(prog)s --query "Compare Python web frameworks" \\ --format "1. Executive Summary\\n2. Comparison Table\\n3. Recommendations" # Stream progress in real-time %(prog)s --query "Analyze EV battery market" --stream # Start without waiting %(prog)s --query "Research topic" --no-wait # Check status of running research %(prog)s --status abc123 # Continue from previous research %(prog)s --query "Elaborate on point 2" --continue abc123 # List recent research %(prog)s --list Note: Research tasks typically take 2-10 minutes and cost $2-5 per task. """ ) # Main commands (mutually exclusive) cmd_group = parser.add_mutually_exclusive_group(required=True) cmd_group.add_argument("--query", "-q", help="Research query to execute") cmd_group.add_argument("--status", "-s", dest="interaction_id", metavar="ID", help="Check status of a research task") cmd_group.add_argument("--wait", "-w", dest="wait_id", metavar="ID", help="Wait for a research task to complete") cmd_group.add_argument("--list", "-l", action="store_true", help="List recent research tasks") # Query options parser.add_argument("--format", "-f", metavar="SPEC", help="Output format specification") parser.add_argument("--continue", dest="continue_from", metavar="ID", help="Continue from previous research interaction") parser.add_argument("--stream", action="store_true", help="Stream progress in real-time") parser.add_argument("--no-wait", action="store_true", help="Start research without waiting for completion") # Output options parser.add_argument("--json", "-j", action="store_true", help="Output results as JSON") parser.add_argument("--raw", "-r", action="store_true", help="Output raw API response") # List options parser.add_argument("--limit", type=int, default=10, help="Number of recent tasks to show (default: 10)") args = parser.parse_args() # Route to appropriate command if args.query: exit_code = asyncio.run(cmd_research(args)) elif args.interaction_id: exit_code = asyncio.run(cmd_status(args)) elif args.wait_id: args.interaction_id = args.wait_id exit_code = asyncio.run(cmd_wait(args)) elif args.list: exit_code = asyncio.run(cmd_list(args)) else: parser.print_help() exit_code = 1 sys.exit(exit_code) if __name__ == "__main__": main()
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README.md 7.5 KB
# Gemini Deep Research Skill Execute autonomous multi-step research tasks using Google's Gemini Deep Research Agent. Unlike standard LLM queries that respond in seconds, Deep Research is an "analyst-in-a-box" that plans, searches, reads, and synthesizes information into comprehensive, cited reports. ## Overview The Deep Research Agent (`deep-research-pro-preview-12-2025`) powered by Gemini 3 Pro: - **Plans** research strategy based on your query - **Searches** the web and analyzes sources - **Reads** and extracts relevant information - **Iterates** through multiple search/read cycles - **Outputs** detailed, cited reports This process takes 2-10 minutes but produces thorough analysis that would take a human researcher hours. ## Installation ```bash # Navigate to skill directory cd skills/deep-research # Install dependencies pip install -r requirements.txt # Set up API key cp .env.example .env # Edit .env and add your GEMINI_API_KEY ``` ### Getting a Gemini API Key 1. Go to [Google AI Studio](https://aistudio.google.com/) 2. Click "Get API key" 3. Create a new key or use an existing one 4. Copy the key to your `.env` file ## Quick Start ```bash # Basic research query python3 scripts/research.py --query "Research the competitive landscape of cloud providers in 2024" # Stream progress in real-time python3 scripts/research.py --query "Compare React, Vue, and Angular frameworks" --stream # Get structured JSON output python3 scripts/research.py --query "Analyze the EV market" --json ``` ## Commands ### `--query` / `-q` Start a new research task. ```bash # Basic query python3 scripts/research.py -q "Research the history of containerization" # With output format specification python3 scripts/research.py -q "Compare database solutions" \ --format "1. Executive Summary\n2. Comparison Table\n3. Pros/Cons\n4. Recommendations" # Start without waiting for results python3 scripts/research.py -q "Research topic" --no-wait ``` ### `--stream` Stream research progress in real-time. Shows thinking steps and builds the report as it's generated. ```bash python3 scripts/research.py -q "Analyze market trends" --stream ``` ### `--status` / `-s` Check the status of a running research task. ```bash python3 scripts/research.py --status abc123xyz ``` ### `--wait` / `-w` Wait for a specific research task to complete. ```bash python3 scripts/research.py --wait abc123xyz ``` ### `--continue` Continue a conversation from previous research. Useful for follow-up questions. ```bash # First, run initial research python3 scripts/research.py -q "Research Kubernetes architecture" # Output: Interaction ID: abc123xyz # Then ask follow-up python3 scripts/research.py -q "Elaborate on the networking section" --continue abc123xyz ``` ### `--list` / `-l` List recent research tasks from local history. ```bash python3 scripts/research.py --list python3 scripts/research.py --list --limit 20 ``` ## Output Options | Flag | Description | |------|-------------| | (default) | Human-readable markdown report | | `--json` / `-j` | Structured JSON output | | `--raw` / `-r` | Raw API response | ## Configuration ### Environment Variables | Variable | Default | Description | |----------|---------|-------------| | `GEMINI_API_KEY` | (required) | Your Google Gemini API key | | `DEEP_RESEARCH_TIMEOUT` | `600` | Max wait time in seconds | | `DEEP_RESEARCH_POLL_INTERVAL` | `10` | Seconds between status polls | | `DEEP_RESEARCH_CACHE_DIR` | `~/.cache/deep-research` | Local history cache directory | ### .env File ```bash GEMINI_API_KEY=your-api-key-here DEEP_RESEARCH_TIMEOUT=600 DEEP_RESEARCH_POLL_INTERVAL=10 ``` ## Cost & Performance ### Estimated Costs Deep Research uses a pay-as-you-go model based on token usage: | Task Type | Search Queries | Input Tokens | Output Tokens | Estimated Cost | |-----------|---------------|--------------|---------------|----------------| | Standard | ~80 | ~250k (50-70% cached) | ~60k | $2-3 | | Complex | ~160 | ~900k (50-70% cached) | ~80k | $3-5 | ### Time Expectations - **Simple queries**: 2-5 minutes - **Complex analysis**: 5-10 minutes - **Maximum**: 60 minutes (API limit) ## Use Cases ### Market Analysis ```bash python3 scripts/research.py -q "Analyze the competitive landscape of \ EV battery manufacturers, including market share, technology, and supply chain" ``` ### Technical Research ```bash python3 scripts/research.py -q "Compare Rust vs Go for building \ high-performance backend services" \ --format "1. Performance Benchmarks\n2. Memory Safety\n3. Ecosystem\n4. Learning Curve" ``` ### Due Diligence ```bash python3 scripts/research.py -q "Research Company XYZ: recent news, \ financial performance, leadership changes, and market position" ``` ### Literature Review ```bash python3 scripts/research.py -q "Review recent developments in \ large language model efficiency and optimization techniques" ``` ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | `GEMINI_API_KEY not set` | Missing API key | Set in `.env` or environment | | `API error 429` | Rate limited | Wait and retry | | `Research timed out` | Task took too long | Simplify query or increase timeout | | `Failed to parse result` | Unexpected response | Use `--raw` to see actual output | ## Exit Codes | Code | Meaning | |------|---------| | 0 | Success | | 1 | Error (API, config, timeout) | | 130 | Cancelled by user (Ctrl+C) | ## Architecture ``` ┌─────────────────┐ ┌──────────────────────┐ │ CLI Script │──────│ DeepResearchClient │ │ (research.py) │ │ │ └─────────────────┘ └──────────┬───────────┘ │ ▼ ┌──────────────────────┐ │ Gemini Deep │ │ Research API │ │ │ │ POST /interactions │ │ GET /interactions │ └──────────────────────┘ │ ▼ ┌──────────────────────┐ │ HistoryManager │ │ (~/.cache/deep- │ │ research/) │ └──────────────────────┘ ``` ## Safety & Privacy - **Read-only**: This skill only reads/researches; no file modifications - **No secrets in queries**: Avoid including sensitive data in research queries - **Source verification**: Always verify citations in the output - **Cost awareness**: Each task costs $2-5; be mindful of usage ## Limitations - **No custom tools**: Cannot use MCP or function calling - **No structured output enforcement**: JSON formatting relies on prompt engineering - **Web-only research**: Cannot access private/authenticated sources - **60-minute max**: Very complex tasks may time out ## References - [Gemini Deep Research Documentation](https://ai.google.dev/gemini-api/docs/deep-research) - [Google AI Studio](https://aistudio.google.com/) - [Gemini API Pricing](https://ai.google.dev/gemini-api/docs/pricing) -
requirements.txt 35 B
httpx>=0.25.0 python-dotenv>=1.0.0 -
SKILL.md 4.1 KB
--- name: gemini-deep-research description: "Run autonomous multi-step research with Google's Gemini Deep Research Agent: kick off a query, poll progress, and collect a cited report for market analysis or literature reviews." category: research risk: critical source: https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research source_repo: sanjay3290/ai-skills source_type: community date_added: "2026-07-09" author: sanjay3290 tags: [research, gemini, google, reports] tools: [claude, cursor, gemini] license: "Apache-2.0" license_source: "https://github.com/sanjay3290/ai-skills/blob/main/LICENSE" --- # Gemini Deep Research Skill ## When to Use - Use when a question needs autonomous multi-step research with cited sources (market analysis, literature reviews, competitive scans) - Use when you want to start a Gemini Deep Research run, poll its progress, and collect the final report - Use when a quick web search is not enough and a structured, source-grounded report is required Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports. ## Requirements - Python 3.8+ - httpx: `pip install -r requirements.txt` - GEMINI_API_KEY environment variable ## Setup 1. Get a Gemini API key from [Google AI Studio](https://aistudio.google.com/) 2. Set the environment variable: ```bash export GEMINI_API_KEY=your-api-key-here ``` Or create a `.env` file in the skill directory. ## Safety Gate Before starting a research job, show the user the exact query, the fact that it will be sent to Google's Gemini service, the expected cost range, and the output destination. Start a job only after explicit approval. Do not include private workspace material, credentials, personal data, or confidential customer information in a query. ## Usage ### Start a research task ```bash python3 scripts/research.py --query "Research the history of Kubernetes" ``` ### With structured output format ```bash python3 scripts/research.py --query "Compare Python web frameworks" \ --format "1. Executive Summary\n2. Comparison Table\n3. Recommendations" ``` ### Stream progress in real-time ```bash python3 scripts/research.py --query "Analyze EV battery market" --stream ``` ### Start without waiting ```bash python3 scripts/research.py --query "Research topic" --no-wait ``` ### Check status of running research ```bash python3 scripts/research.py --status <interaction_id> ``` ### Wait for completion ```bash python3 scripts/research.py --wait <interaction_id> ``` ### Continue from previous research ```bash python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id> ``` ### List recent research ```bash 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 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 - Each research job is a paid, third-party API request; costs and availability can change, and the listed estimate is not a spending authorization. - Reports may contain incomplete, stale, or incorrect citations. Verify consequential claims against primary sources. - This skill cannot guarantee that a prompt is safe to disclose; redact proprietary or personal material before requesting user approval. - An API key must remain local and must never be committed, printed, or sent in a query.
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