gemini-delegator

Gemini Delegator Skill

Full documentation:

• ROUTING_SYSTEM.md

Real-World Performance (Tested 2026-01-18)

Metric	Value	Notes
Max files analyzed	978 files in single query	~280k LOC processed successfully
Latency (simple)	10-15 seconds	Single directory analysis
Latency (complex)	58-73 seconds	Full codebase analysis
Cache efficiency	94k tokens cached	Significant cost savings
Tool calls/session	Up to 20	File reads auto-executed
Model routing	flash-lite → pro	Two-model approach for optimization

When to Delegate to Gemini

Condition	Threshold/Criteria	Reason
Context size	> 100K tokens	Gemini: 1M context window
Large codebase analysis	Multiple files, > 50 files	Fast bulk processing
Image/screenshot analysis	Any visual content	Native multimodal support
Video/audio processing	Any A/V content	Native multimodal support
Speed-critical tasks	Latency sensitive	Flash models optimized
Cost-sensitive operations	Budget constraints	Generous free tier (1K/day)
Google Cloud integration	BigQuery, Cloud Functions	Native integration

When NOT to Delegate

• Complex reasoning requiring deep analysis
• Security-critical implementations
• Production code generation needing high quality
• Tasks requiring careful planning and architecture

Methods

Method 1: delegate_to_model MCP Tool

Use the nexus-agents MCP tool for intelligent routing:

# The tool analyzes task and recommends optimal model
nexus-agents delegate_to_model --task "Analyze this 500K token codebase"

Method 2: Direct Gemini CLI

# Basic delegation (human-readable output)
gemini -p "Analyze this codebase"

# JSON output for parsing (note: response may contain markdown fences)
gemini -p "Analyze this codebase" --output-format json

# With auto-approve for autonomous operation
gemini --yolo -p "Review all files in src/" --output-format json

# Specify model
gemini -m gemini-2.5-flash -p "Quick analysis task"
gemini -m gemini-3-pro-preview -p "Complex reasoning task"

JSON Output Parsing Note: The response field in JSON output may contain markdown code fences. Parse accordingly:

const result = JSON.parse(output);
const response = result.response.replace(/```\w*\n?/g, '').trim();

Multimodal Examples

Image Analysis

# Analyze screenshot
gemini -p "Describe this UI and identify usability issues" < screenshot.png

# Analyze architecture diagram
gemini -p "Extract components and relationships from this diagram" < diagram.png

# Code screenshot OCR
gemini -p "Extract the code from this screenshot" < code-image.png

Video Analysis

# Analyze video content (uses File API for > 1 minute)
gemini -p "Summarize the key points from this demo video" < demo.mp4

Batch Processing

# Process multiple images
for img in screenshots/*.png; do
  gemini -p "Analyze this UI screenshot" < "$img" --output-format json >> results.json
done

Context Advantage

Model	Context Window	Best For
Claude	~200K tokens	Quality reasoning
Gemini Pro	1M tokens	Large context, multimodal
Gemini Flash	200K-1M tokens	Speed, high volume

The 1M token context window enables:

• Entire codebase analysis in single context
• Full documentation sets without chunking
• Complete git history review
• Large data file processing

Output Formats

# Human readable (default)
gemini -p "task"

# JSON for parsing
gemini -p "task" --output-format json

# Streaming JSON for real-time
gemini -p "task" --output-format stream-json

Process

1. Evaluate task requirements:

   • Estimate context size
   • Check for multimodal content
   • Assess speed/cost sensitivity

2. Choose delegation method:

   • Use delegate_to_model for intelligent routing
   • Use direct Gemini CLI for explicit control

3. Configure execution:

   • Select appropriate model (Pro vs Flash)
   • Set output format for parsing
   • Use --yolo for autonomous tasks

4. Process results:

   • Parse JSON output
   • Integrate findings into workflow

Quick Reference

# Large context analysis
gemini -p "Analyze this entire codebase structure" --yolo

# Screenshot analysis
gemini -p "What does this UI show?" < screenshot.png

# Fast iteration
gemini -m gemini-2.5-flash -p "Quick review" --output-format json

# Cost-sensitive batch
for f in *.ts; do gemini -p "Review: $(cat $f)" >> reviews.txt; done

Anti-rationalization — Gemini delegation

Excuse	Counter
"Gemini for everything, it has a big context"	Big context isn't free — output quality varies by task. Use Gemini for >100k context, multimodal, or research-shaped tasks.
"Skip the multimodal check"	If the task includes images, Gemini is the canonical CLI. Other CLIs may text-summarize via OCR but lose information.
"I'll use Gemini even for short code-gen"	Codex is faster on code. Use Gemini when context size or multimodal forces the choice.

Red flags

• Gemini used for short single-file code-gen (Codex is faster)
• Multimodal task routed to a non-Gemini CLI without explicit reason
• Context exceeded 1M without verification of token budget

Verification checklist

• Task category matches Gemini's strengths (large context, research, multimodal)
• Token estimate confirmed within Gemini's context window before dispatch
• Outcome recorded for adaptive routing