system-prompts-and-models-of-ai-tools/scripts/METADATA_GENERATION.md

🤖 Metadata Generation Script

Purpose: Automatically generate or update metadata JSON files for AI coding tools.

🚀 Quick Start

# Generate metadata for a single tool
python scripts/generate-metadata.py --tool cursor

# Generate metadata for all tools
python scripts/generate-metadata.py --all

# Update existing metadata
python scripts/generate-metadata.py --update cursor

# Validate all metadata
python scripts/generate-metadata.py --validate

📋 Features

• Automatic Detection: Scans tool directories and extracts information
• Smart Analysis: Analyzes prompts for patterns and features
• Version Tracking: Detects multiple versions and tracks changes
• Validation: Ensures all required fields are present
• Bulk Operations: Process all tools at once

🔧 How It Works

1. Directory Scanning

Scans each tool directory for:

• System prompt files (.txt)
• Tool definition files (.json)
• README files
• Multiple versions

2. Content Analysis

Analyzes prompt content for:

• Conciseness patterns
• Security rules
• Tool capabilities
• Parallel execution support
• Memory systems
• TODO tracking

3. Feature Detection

Automatically detects:

• Code generation capabilities
• Agent mode support
• Multi-file editing
• Git integration
• Platform support

4. Metadata Generation

Creates JSON with:

• Basic information
• Version history
• Pricing (requires manual input)
• Feature flags
• Analysis and comparison

📖 Usage Guide

Generate Single Tool

python scripts/generate-metadata.py --tool "Cursor Prompts"

Output: metadata/cursor.json

Generate All Tools

python scripts/generate-metadata.py --all

Output: Creates/updates all metadata files

Update Existing

python scripts/generate-metadata.py --update cursor --field pricing

Validate

python scripts/generate-metadata.py --validate

Checks:

• Required fields present
• Valid JSON format
• Correct data types
• File references exist

🎯 Manual Fields

Some fields require manual input:

Required Manual:

• pricing.tiers - Pricing information
• links.website - Official website
• links.docs - Documentation URL
• marketPosition.userBase - Estimated users

Auto-Generated:

• features.* - Detected from prompts
• patterns.* - Analyzed from content
• metrics.* - Calculated from files
• documentation.* - Scanned from directory

📊 Output Format

Generated metadata follows the schema in metadata/README.md:

{
  "name": "Tool Name",
  "slug": "tool-name",
  "type": "IDE Plugin | CLI Tool | Web Platform | Autonomous Agent",
  "status": "active",
  "version": { ... },
  "pricing": { ... },
  "models": { ... },
  "features": { ... },
  "platforms": { ... },
  "documentation": { ... },
  "analysis": { ... }
}

🔍 Analysis Algorithms

Conciseness Detection

def detect_conciseness(prompt_text):
    indicators = [
        "be concise",
        "brief",
        "minimal",
        "short",
        "terse"
    ]
    score = sum(1 for i in indicators if i in prompt_text.lower())
    return "very-high" if score >= 3 else "high" if score >= 2 else "medium"

Feature Detection

def detect_features(prompt_text, tools_json):
    features = {
        "agentMode": "agent" in prompt_text.lower(),
        "parallelExecution": "parallel" in prompt_text.lower(),
        "todoTracking": "todo" in prompt_text.lower() or "task list" in prompt_text.lower(),
        "memorySystem": "memory" in prompt_text.lower() or "context" in prompt_text.lower()
    }
    return features

Tool Counting

def count_tools(tools_json):
    if not tools_json:
        return 0
    data = json.load(open(tools_json))
    return len(data.get("functions", []))

⚙️ Configuration

Config File: scripts/metadata-config.json

{
  "autoDetect": true,
  "analyzePrompts": true,
  "generateMetrics": true,
  "validateOutput": true,
  "overwriteExisting": false,
  "requiredFields": [
    "name",
    "slug",
    "type",
    "status",
    "description"
  ],
  "optionalFields": [
    "pricing",
    "links",
    "marketPosition"
  ]
}

🛠️ Advanced Options

Custom Template

python scripts/generate-metadata.py --tool cursor --template custom-template.json

Specific Fields Only

python scripts/generate-metadata.py --tool cursor --fields features,patterns,metrics

Dry Run

python scripts/generate-metadata.py --all --dry-run

Shows what would be generated without writing files.

Debug Mode

python scripts/generate-metadata.py --tool cursor --debug

Shows detailed analysis and detection steps.

📝 Examples

Example 1: New Tool

# Add new tool "NewIDE"
mkdir "NewIDE"
echo "System prompt..." > "NewIDE/prompt.txt"

# Generate metadata
python scripts/generate-metadata.py --tool "NewIDE"

# Review and edit
code metadata/newide.json

# Validate
python scripts/generate-metadata.py --validate metadata/newide.json

Example 2: Update Pricing

# Edit metadata file
code metadata/cursor.json

# Update pricing section manually

# Validate changes
python scripts/generate-metadata.py --validate metadata/cursor.json

Example 3: Bulk Update

# Update all metadata with latest patterns
python scripts/generate-metadata.py --all --refresh-patterns

# Regenerate metrics
python scripts/generate-metadata.py --all --refresh-metrics

✅ Validation Rules

Required Fields:

• ✅ name - Tool name
• ✅ slug - URL-friendly identifier
• ✅ type - Tool category
• ✅ status - Active/beta/deprecated
• ✅ description - Brief summary

Recommended Fields:

• ⚠️ pricing - Pricing information
• ⚠️ models - Supported AI models
• ⚠️ features - Feature flags
• ⚠️ links.website - Official website

Optional Fields:

• 💡 marketPosition - Market analysis
• 💡 analysis - Strengths/weaknesses
• 💡 tags - Search tags

🐛 Troubleshooting

Issue: "Tool directory not found"

Solution: Check directory name matches exactly

ls -la | grep -i "tool-name"

Issue: "Invalid JSON format"

Solution: Validate JSON syntax

python -m json.tool metadata/tool.json

Issue: "Missing required fields"

Solution: Check which fields are missing

python scripts/generate-metadata.py --validate --verbose

🤝 Contributing

When adding metadata:

1. Use the generation script
2. Manually fill pricing/links
3. Validate output
4. Test with API queries
5. Submit PR

📚 See Also

• Metadata Schema
• Contributing Guidelines
• Validation Script
• Analysis Script

---

Last Updated: 2025-01-02
Part of the AI Coding Tools repository