Gemini Research

Gemini Research

Deep web research using Gemini CLI with Google Search grounding. Cost-effective alternative to expensive research APIs.

Overview

Leverage Gemini CLI's built-in Google Search grounding (google_web_search) to fetch real-time information with citations. Returns structured JSON for Claude to analyze and synthesize.

Key advantages:

• Free tier: 60 requests/min, 1000/day
• 1M-token context window (Gemini 2.5 Pro)
• Built-in Google Search grounding
• Structured JSON output with citations
• Real-time data (news, docs, tutorials)

Prerequisites

Gemini CLI installed:

npm install -g @google/gemini-cli

Authentication: First run requires Google login (any account works):

gemini -p "test" --output-format json

Follow browser auth flow. Free Code Assist license included.

Verify installation:

gemini --version

Usage

When user asks to:

• "Research X using Google Search"
• "Find real-world examples of X"
• "Get latest information about X"
• "Deep research on X with sources"

Run the skill wrapper with appropriate mode.

⚠️ CRITICAL WORKFLOW STEP

EVERY TIME this skill runs, Claude MUST complete these steps:

1. Wait for research to complete - Script outputs markdown file path
2. Read the generated markdown file - Use Read tool on the path shown
3. Analyze the research findings - Extract key learnings, patterns, recommendations
4. Append Claude's Analysis section - Use Edit tool to replace the placeholder
5. Present findings to user - Summarize the analysis in chat

This is NOT optional. The markdown file contains a placeholder section that MUST be populated. Skipping this step leaves incomplete documentation.

Example Analysis Format

Your analysis should include:

• Key Learnings: 3-5 main takeaways from the research
• Recommendations: Practical advice based on findings
• Next Steps: Suggested actions or areas for further investigation
• Relevance: How findings apply to the specific context

Concrete Example

After research completes and you read the markdown file at docs/research/google/20250111123456-topic.md:

Use Edit tool to replace placeholder:

old_string: ## Claude's Analysis

_This section will be populated by Claude after analyzing the research findings._


new_string: ## Claude's Analysis

### Key Learnings
- Main finding 1 with context and implications
- Main finding 2 showing clear patterns across sources
- Main finding 3 connecting to broader best practices

### Recommendations
- Actionable advice based on finding 1
- Practical suggestion derived from finding 2
- Trade-off to consider from contradictory sources

### Next Steps
- Specific area to investigate further if needed
- Potential application to current project/context

Then present summary to user in chat response.

Research Modes

1. Quick Research (default)

Fast overview with 5-8 sources.

bash plugins/knowledge-work/skills/gemini-research/scripts/research.sh "your query here"

Output:

• JSON: gemini-research-output.json (for backward compatibility)
• Markdown: docs/research/google/<timestamp>-<topic>.md (human-readable)

JSON structure:

• queries_used: Search queries executed
• sources: Array of {title, url}
• key_points: Main findings (5-8 bullets)
• quotes: Array of {text, source_url}
• summary: 3-5 sentence brief

Example:

bash plugins/knowledge-work/skills/gemini-research/scripts/research.sh "TypeScript error handling patterns 2025"

2. Deep Research

Comprehensive analysis with 10-15 sources, contradictions identified.

bash plugins/knowledge-work/skills/gemini-research/scripts/research.sh "your query" deep

Output: Extended JSON with:

• All quick mode fields
• contradictions: Conflicting info found
• consensus: Widely-agreed points
• gaps: Areas needing more data

Example:

bash plugins/knowledge-work/skills/gemini-research/scripts/research.sh "React Server Components best practices" deep

3. Code Examples

Focus on practical code snippets and real-world implementations.

bash plugins/knowledge-work/skills/gemini-research/scripts/research.sh "your query" code

Output: Code-focused JSON with:

• sources: Prioritize GitHub, Stack Overflow, official docs
• code_snippets: Array of {language, code, source_url, description}
• patterns: Common implementation patterns
• libraries: Recommended tools/packages
• gotchas: Known pitfalls with solutions

Example:

bash plugins/knowledge-work/skills/gemini-research/scripts/research.sh "Playwright headless browser automation" code

Workflow

Integration with Deep-Research Agent

The deep-research agent can orchestrate multiple research sources in parallel:

1. Local codebase (Explorer agent)
2. Web search (existing deep-research)
3. Gemini research (this skill) ← NEW
4. GitHub code search (future)

Combine all sources for comprehensive context.

Output Format

Quick Mode

{
  "queries_used": ["typescript error handling 2025", "typescript custom errors"],
  "sources": [
    {"title": "TypeScript Handbook: Error Handling", "url": "https://..."},
    {"title": "Modern Error Handling in TS", "url": "https://..."}
  ],
  "key_points": [
    "Use typed errors extending Error class",
    "Avoid throwing strings or plain objects",
    "Leverage exhaustive type checking"
  ],
  "quotes": [
    {
      "text": "TypeScript 5.0 introduced better error inference",
      "source_url": "https://..."
    }
  ],
  "summary": "Modern TypeScript error handling favors typed custom errors over generic Error. Key patterns include discriminated unions for error types and exhaustive checking for safety."
}

Deep Mode

Adds:

{
  "contradictions": ["Some sources recommend Error subclassing, others favor plain objects with type guards"],
  "consensus": ["All sources agree: avoid throwing strings"],
  "gaps": ["Limited guidance on async error handling in React Server Components"]
}

Code Mode

Adds:

{
  "code_snippets": [
    {
      "language": "typescript",
      "code": "class ValidationError extends Error { constructor(public field: string) { super(`Invalid: ${field}`); } }",
      "source_url": "https://...",
      "description": "Custom error with field context"
    }
  ],
  "patterns": ["Custom error classes", "Error discriminated unions"],
  "libraries": ["zod (validation)", "neverthrow (Result types)"],
  "gotchas": [
    {
      "issue": "Error.prototype breaks after compilation",
      "solution": "Set prototype manually: Object.setPrototypeOf(this, ValidationError.prototype)"
    }
  ]
}

Error Handling

Missing API auth:

Error: Not authenticated. Run: gemini -p "test"

Solution: Run gemini once manually to auth.

Rate limits (60/min or 1000/day):

Error: Rate limit exceeded

Solution: Wait 1 minute or try next day. Consider Perplexity/Tavily MCP for higher limits.

No results:

{"error": "No results found", "suggestion": "Rephrase query or try different keywords"}

Gemini CLI not installed:

Error: gemini command not found

Solution: npm install -g @google/gemini-cli

Configuration

Customize prompts: Edit templates in scripts/research.sh:

• QUICK_TEMPLATE
• DEEP_TEMPLATE
• CODE_TEMPLATE

Change output location:

• JSON (temp): gemini-research-output.json (current directory)
• Markdown (permanent): docs/research/google/<timestamp>-<topic>.md

Customize via OUTPUT_FILE and RESEARCH_DIR variables in script.

Performance

• Quick mode: ~5-10 seconds (5-8 sources)
• Deep mode: ~10-20 seconds (10-15 sources)
• Code mode: ~8-15 seconds (code-focused)

Speed depends on:

• Query complexity
• Number of sources
• Network latency
• Gemini API load

Comparison with Alternatives

Tips

Force Google Search grounding: Prompts explicitly say "Use google_web_search" to ensure tool usage.

Demand citations: All prompts require URLs + quotes for verifiability.

JSON for easy parsing: --output-format json enables programmatic analysis by Claude.

Chain research: Run multiple queries, Claude synthesizes results.

Example chaining:

# 1. Get overview
bash scripts/research.sh "React Server Components" quick

# 2. Get code examples
bash scripts/research.sh "RSC data fetching patterns" code

# 3. Claude combines both JSONs for implementation

Advanced: Integration Points

With Deep-Research Agent

plugins/development-lifecycle/agents/deep-context-gatherer.md can orchestrate:

1. Run local codebase search (Grep, Glob)
2. Run Gemini research (this skill)
3. Run GitHub code search (future)
4. Synthesize all sources
5. Return unified context

With Feature Development

/commands/develop-feature.md can include research phase:

Phase 1: Research
- Ask user for topic
- Run gemini-research skill
- Extract patterns from JSON
- Proceed to planning

Troubleshooting

Tool call flakiness: Gemini CLI sometimes ignores tool hints. Solution: Make prompts explicit ("Use google_web_search to...").

Missing URLs: If JSON lacks source URLs, check prompt template includes "with citations and URLs".

Empty results: Try broader query or different keywords. Gemini may filter very niche topics.

JSON parsing errors: Gemini occasionally returns markdown-wrapped JSON. Script uses jq to extract.

Notes

• Free tier may train on data (check Google's terms)
• Personal Google account sufficient (no paid plan needed)
• For production/high-volume: consider Perplexity/Tavily MCP
• Outputs:
  • JSON: gemini-research-output.json (temp, for backward compatibility)
  • Markdown: docs/research/google/<timestamp>-<topic>.md (permanent, human-readable)
• Script uses --output-format json for structured data
• Markdown includes placeholder for Claude's analysis section
• Claude must read markdown and append synthesis after research completes