Claude Code Plugins

Community-maintained marketplace

Feedback

gemini-exploration-patterns

@melodic-software/claude-code-plugins
1
0

Strategic patterns for codebase exploration using Gemini's large context window. Covers token thresholds, model routing, and exploration strategies. Use when deciding between Claude and Gemini for exploration, analyzing large codebases, or choosing between Flash and Pro models for context size.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name gemini-exploration-patterns
description Strategic patterns for codebase exploration using Gemini's large context window. Covers token thresholds, model routing, and exploration strategies. Use when deciding between Claude and Gemini for exploration, analyzing large codebases, or choosing between Flash and Pro models for context size.
allowed-tools Read, Glob, Grep, Skill

Gemini Exploration Patterns

🚨 MANDATORY: Invoke gemini-cli-docs First

STOP - Before providing ANY response about Gemini exploration:

  1. INVOKE gemini-cli-docs skill
  2. QUERY for the specific exploration/context topic
  3. BASE all responses EXCLUSIVELY on official documentation loaded

Overview

This skill provides strategic guidance for leveraging Gemini CLI's large context window for codebase exploration. It covers when to delegate exploration to Gemini, which model to use, and how to structure outputs for Claude to consume.

When to Use This Skill

Keywords: explore codebase, analyze architecture, large context, token limit, gemini exploration, codebase analysis, when to use gemini, model selection

Use this skill when:

  • Deciding whether to explore with Claude or Gemini
  • Planning a large codebase analysis
  • Choosing between Flash and Pro models
  • Structuring exploration output for cross-CLI consumption
  • Optimizing exploration for cost vs quality

Token Threshold Decision Matrix

Codebase Size Tokens Recommended Agent Rationale
Small <50K Claude native Claude's tools are faster
Medium 50K-500K Gemini Flash Good balance of speed/cost
Large 500K-1M Gemini Flash + chunking Stay within Flash limits
Very Large 1M-2M Gemini Pro Need extended context
Massive >2M Gemini Pro + progressive Multi-pass exploration

Token Estimation

# Quick estimation: 1 token ~ 4 characters
chars=$(find . -name "*.ts" -o -name "*.py" | xargs wc -c | tail -1 | awk '{print $1}')
tokens=$((chars / 4))
echo "Estimated tokens: $tokens"

Decision Rule

IF estimated_tokens < 50,000:
    USE Claude's native Explore agent

ELIF estimated_tokens < 1,000,000:
    USE Gemini Flash via /gemini-explore

ELIF estimated_tokens < 2,000,000:
    USE Gemini Pro via /gemini-explore --pro

ELSE:
    USE Progressive exploration (chunk by module)

Model Selection Guide

Gemini Flash (gemini-2.5-flash)

Context: Large (exact limits set by Google, check current API docs) Cost: Lower Speed: Faster

Best for:

  • Bulk file analysis
  • Pattern detection across codebase
  • Dependency mapping
  • Initial exploration passes
  • Log file analysis
  • Documentation generation

Gemini Pro (gemini-2.5-pro)

Context: Very large (exact limits set by Google, check current API docs) Cost: Higher Speed: Slower

Best for:

  • Complex architectural reasoning
  • Security-critical analysis
  • Nuanced code quality assessment
  • Very large codebases (>1M tokens)
  • Tasks requiring deep understanding

Exploration Strategies

Strategy 1: Full Codebase Sweep

Best for: Understanding overall architecture

# Collect all source files
find . -type f \( -name "*.ts" -o -name "*.tsx" \) \
  -not -path "*/node_modules/*" \
  -not -path "*/.git/*" \
  | xargs cat | gemini "Analyze architecture" --output-format json

Strategy 2: Module-by-Module

Best for: Very large codebases (>2M tokens)

# Explore each top-level module separately
for dir in src/*/; do
  echo "=== Exploring $dir ==="
  find "$dir" -name "*.ts" | xargs cat | gemini "Analyze this module" --output-format json
done

Strategy 3: Entry-Point Focused

Best for: Understanding execution flow

# Focus on entry points and their dependencies
cat package.json src/index.ts src/main.ts | gemini "Analyze entry points and startup flow" --output-format json

Strategy 4: Dependency-First

Best for: Understanding relationships

# Package manifests + import statements
find . -name "package.json" -o -name "requirements.txt" -o -name "go.mod" | xargs cat
grep -r "^import\|^from" src/ | head -1000

Strategy 5: Progressive Depth

Best for: Iterative understanding

  1. Pass 1: File tree + READMEs only
  2. Pass 2: Package manifests + configs
  3. Pass 3: Entry points + main modules
  4. Pass 4: Deep dive on specific areas

Output Format Standards

All Gemini exploration outputs should follow this format for Claude consumption:

YAML Frontmatter (Required)

---
generated-by: gemini-cli
model: gemini-2.5-flash
timestamp: 2025-11-30T12:00:00Z
tokens: 150000
scope: architecture|dependencies|patterns|all
---

Machine-Readable Summary (Required)

{
  "type": "exploration",
  "scope": "architecture",
  "tokens_used": 150000,
  "model": "gemini-2.5-flash",
  "key_findings": [
    "Uses Clean Architecture pattern",
    "React frontend with Express backend",
    "PostgreSQL database with Prisma ORM"
  ],
  "files_analyzed": 245,
  "entry_points": ["src/index.ts", "src/server.ts"]
}

Human-Readable Content (Required)

Structured markdown with clear sections:

  • Overview: 2-3 sentence summary
  • Architecture: Directory structure, patterns
  • Key Components: Core modules and responsibilities
  • Dependencies: External and internal
  • Patterns: Conventions and style
  • Recommendations: What to read first, areas of concern

Recommendations for Claude (Required)

Specific, actionable guidance:

## Recommendations for Claude

### Files to Read First
1. `src/index.ts` - Main entry point
2. `src/config/index.ts` - Configuration patterns
3. `CLAUDE.md` - Project conventions

### Patterns to Follow
- Use dependency injection for services
- Follow the existing error handling pattern in `src/errors/`

### Areas of Concern
- Complex state management in `src/store/` - read carefully
- Database migrations in `prisma/migrations/` - check before schema changes

File Filtering Patterns

Include Patterns

# Source code
-name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.jsx"
-name "*.py" -o -name "*.go" -o -name "*.rs" -o -name "*.java"

# Configuration
-name "*.json" -o -name "*.yaml" -o -name "*.yml" -o -name "*.toml"

# Documentation
-name "*.md" -o -name "README*"

Exclude Patterns

-not -path "*/node_modules/*"
-not -path "*/.git/*"
-not -path "*/dist/*"
-not -path "*/build/*"
-not -path "*/__pycache__/*"
-not -path "*/.next/*"
-not -path "*/coverage/*"
-not -path "*/.cache/*"

Cost Optimization

Reduce Token Usage

  1. Filter aggressively: Only include relevant file types
  2. Limit file count: Use head -500 for file lists
  3. Truncate large files: Cap individual files at reasonable sizes
  4. Exclude generated code: dist/, build/, vendor/

Batch Efficiently

# Bad: Many small calls
for file in *.ts; do gemini "analyze $file"; done

# Good: One large call
cat *.ts | gemini "analyze all files"

Related Skills

  • gemini-delegation-patterns - When to delegate any task to Gemini
  • gemini-token-optimization - Cost optimization strategies
  • gemini-cli-execution - CLI invocation patterns
  • gemini-workspace-bridge - Artifact storage and exchange

Related Commands

  • /gemini-explore - Execute exploration with standard output
  • /gemini-plan - Generate implementation plans

Keyword Registry

Topic Keywords
Token limits context window, token limit, large context
Model selection flash vs pro, which model, model routing
Exploration explore codebase, analyze architecture, understand code
Cost reduce tokens, optimize cost, batch calls
Output exploration format, cross-cli artifact, claude readable

Test Scenarios

Scenario 1: Token Threshold Decision

Query: "Should I use Claude or Gemini to explore this codebase?" Expected Behavior:

  • Skill activates on "explore codebase" or "large context"
  • Provides token threshold decision matrix Success Criteria: User receives clear guidance based on codebase size

Scenario 2: Model Selection

Query: "Should I use Flash or Pro for codebase analysis?" Expected Behavior:

  • Skill activates on "flash vs pro" or "which model"
  • Provides model comparison and use cases Success Criteria: User receives model recommendation with rationale

Scenario 3: Exploration Strategy

Query: "How do I analyze a very large codebase with Gemini?" Expected Behavior:

  • Skill activates on "very large" or "analyze architecture"
  • Provides progressive exploration strategy Success Criteria: User receives module-by-module or chunking approach

Version History

  • v1.1.0 (2025-12-01): Added MANDATORY section, Test Scenarios, Version History
  • v1.0.0 (2025-11-25): Initial release