Agent skill
gemini-research
Use Gemini CLI for research with Google Search grounding and 1M token context
Install this agent skill to your Project
npx add-skill https://github.com/DNYoussef/context-cascade/tree/main/skills/platforms/gemini-research
SKILL.md
Gemini Research Skill
LIBRARY-FIRST PROTOCOL (MANDATORY)
Before writing ANY code, you MUST check:
Step 1: Library Catalog
- Location:
.claude/library/catalog.json - If match >70%: REUSE or ADAPT
Step 2: Patterns Guide
- Location:
.claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md - If pattern exists: FOLLOW documented approach
Step 3: Existing Projects
- Location:
D:\Projects\* - If found: EXTRACT and adapt
Decision Matrix
| Match | Action |
|---|---|
| Library >90% | REUSE directly |
| Library 70-90% | ADAPT minimally |
| Pattern exists | FOLLOW pattern |
| In project | EXTRACT |
| No match | BUILD (add to library after) |
Purpose
Route research tasks to Gemini CLI when:
- Real-time information is needed (Google Search grounding)
- Context exceeds Claude's 200k limit (Gemini has 1M)
- Need web-grounded factual answers
Unique Capability
What Gemini Does Better:
- Google Search grounding for current information
- 1M token context for massive document analysis
- 70+ extensions (Figma, Stripe, Shopify, etc.)
- Web content analysis with source attribution
When to Use
Perfect For:
- Current events, recent documentation
- Large codebase analysis (>150k tokens)
- Literature reviews with many papers
- Real-time API documentation lookup
- Market research, competitor analysis
Don't Use When:
- Offline/airgapped environments
- Complex multi-step reasoning (use Claude)
- Code generation requiring iteration (use Codex)
Usage
Basic Research
/gemini-research "What are the latest React 19 best practices?"
With Context Files
/gemini-research "Analyze architecture" --context @src/
Large Document Analysis
/gemini-research "Summarize all papers" --context papers/*.pdf
Command Pattern
bash scripts/multi-model/gemini-research.sh "<query>" "<task_id>" "json"
Memory Integration
Results stored to Memory-MCP:
- Key:
multi-model/gemini/research/{task_id} - Tags: WHO=gemini-cli, WHY=research
Output Format
{
"content": "Research findings...",
"sources": ["url1", "url2"],
"model": "gemini-2.5-pro",
"timestamp": "2025-12-28T..."
}
Handoff to Claude
After Gemini research completes:
- Results stored in Memory-MCP
- Claude agents read from memory key
- Use research to inform implementation
// Claude agent reads Gemini research
const research = memory_retrieve("multi-model/gemini/research/{task_id}");
Task("Coder", `Implement using: ${research.content}`, "coder");
Configuration
- Retries: 3 attempts on failure
- Timeout: 60 seconds per query
- Fallback: Claude researcher agent if Gemini unavailable
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