ggg

name	ggg
description	Smart context filter - reduce tokens 50-90% before sending to Claude. Use when reading large files (>1000 lines), analyzing complex codebases, or when user types 'ggg'. Saves ~69% cost per task.
model	haiku

GGG - Gatekeeper (Smart Context Filter)

Purpose

Reduce token count by 50-90% before sending context to Claude using Gemini Flash or Local LLM. This dramatically reduces costs while maintaining quality.

When to Use

• User explicitly types ggg
• Before reading large files (>1000 lines)
• When analyzing complex codebases
• Fixing bugs in specific areas
• Adding features to existing code
• Any task where you need focused context

Cost Comparison

Before Gatekeeper:
  - Read 20k tokens: $0.06 (Claude input)
  - Total: $0.075/task

After Gatekeeper:
  - Filter 20k→2k: $0.0021 (Gemini Flash)
  - Read 2k tokens: $0.006 (Claude input)
  - Total: $0.0231/task
  - SAVINGS: 69% 🎉

Steps

1. Check Prerequisites

Verify Python script exists:

test -f gatekeeper.py && echo "✅ Found" || echo "❌ Missing: gatekeeper.py"

2. Get Task Details

Ask user (if not already provided):

• What file to analyze?
• What task to perform? (e.g., "Fix login bug", "Add rate limiting")

3. Run Gatekeeper

Mode Selection:

• auto (default): Try Gemini Flash → fallback to Local LLM
• flash: Force Gemini Flash (best quality)
• local: Force Local LLM (offline/privacy)

Execute:

python gatekeeper.py [file-path] "[task description]" --mode auto

Examples:

# Auto mode (recommended)
python gatekeeper.py src/auth.ts "Fix login bug" --mode auto

# Force Gemini Flash
python gatekeeper.py api/routes.ts "Add rate limiting" --mode flash

# Force Local LLM
python gatekeeper.py utils.js "Refactor error handling" --mode local

# Extract specific keywords
python gatekeeper.py large_file.py "token,auth,jwt" --extract

4. Review Output

Gatekeeper will output:

{
  "relevant_code": "[filtered code]",
  "summary": "[brief summary]",
  "tokens_original": 20000,
  "tokens_filtered": 2000,
  "reduction_percent": 90
}

5. Use Filtered Content

Present to user:

✅ Gatekeeper Filter Complete!

**Original Size**: 20,000 tokens
**Filtered Size**: 2,000 tokens
**Reduction**: 90%
**Cost Savings**: $0.054 (69%)

**Filtered Content:**
[Show filtered code]

**Summary:**
[Show summary]

Ready to proceed with: [task description]

Then proceed with the task using the filtered content instead of the full file.

Important Notes

Quality

• Always review filtered content before using
• If filter seems to miss important context, use --mode flash for better quality
• If filter fails entirely, fall back to reading full file

Modes

• Auto Mode: Recommended for most cases (tries flash, falls back to local)
• Flash Mode: Best quality, requires GEMINI_API_KEY env var
• Local Mode: Works offline, quality varies

Environment Variables

# Gemini Flash API (recommended)
export GEMINI_API_KEY="your-key-here"

# Local LLM URL (optional, fallback)
export LOCAL_LLM_URL="http://192.168.1.202:8088/v1/chat/completions"

When NOT to Use

• Files < 500 lines (overhead not worth it)
• When you need complete file context
• When keywords aren't clear
• When filter consistently fails for a file type

Fallback Strategy

If gatekeeper fails:

1. Try different mode (flash → local or vice versa)
2. Try more specific task description
3. Try --extract with explicit keywords
4. Fall back to reading full file

Error Handling

"gatekeeper.py not found":

# Check if script exists
ls -la gatekeeper.py

# If missing, inform user:
"❌ Gatekeeper script not found. Using full file instead."

"Gemini API key not set":

# Fall back to local mode
python gatekeeper.py [file] "[task]" --mode local

# Or inform user:
"⚠️ GEMINI_API_KEY not set. Using local LLM (quality may vary)."

"Local LLM not responding":

# Inform user and fall back
"❌ Local LLM not available. Using full file instead."

"Filter quality poor":

# Try flash mode if was using local
python gatekeeper.py [file] "[task]" --mode flash

# Or fall back to full file
"⚠️ Filter quality insufficient. Using full file for accuracy."

Success Criteria

• ✅ Gatekeeper script executed successfully
• ✅ Token reduction 50-90%
• ✅ Filtered content reviewed and approved
• ✅ Cost savings calculated and shown to user
• ✅ Task proceeds with filtered content
• ✅ User informed of savings

Advanced Usage

Batch Processing

# Filter multiple files
for file in src/*.ts; do
  python gatekeeper.py "$file" "security audit" --mode flash
done

Integration with Other Skills

# Use with nnn (planning)
ggg [file] "[task]" --mode flash
# Then create plan with filtered context

# Use with gogogo (execution)
ggg [file] "[task]" --mode auto
# Then implement using focused context

Performance Tips

• Use flash mode for production tasks (consistent quality)
• Use local mode for experimentation (faster, offline)
• Use auto mode when unsure (best of both worlds)
• Provide specific task descriptions for better filtering
• Review filtered content before relying on it