gemini-research

📁 dnyoussef/context-cascade 📅 5 days ago
1
总安装量
1
周安装量
#48240
全站排名
安装命令
npx skills add https://github.com/dnyoussef/context-cascade --skill gemini-research

Agent 安装分布

replit 1
windsurf 1
openclaw 1
opencode 1
cursor 1
codex 1

Skill 文档

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:

  1. Results stored in Memory-MCP
  2. Claude agents read from memory key
  3. 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