Files
antigravity-skills-reference/scripts/auto_categorize_skills.py
Zied 8de886a2ff feat: Implement intelligent auto-categorization for skills
- Added `scripts/auto_categorize_skills.py` to analyze skill names and descriptions, auto-assigning categories based on keyword matching.
- Updated category distribution to show counts and sort categories by skill count in the Home page dropdown.
- Created documentation in `docs/CATEGORIZATION_IMPLEMENTATION.md` and `docs/SMART_AUTO_CATEGORIZATION.md` detailing the new categorization process and usage.
- Introduced `scripts/fix_year_2025_to_2026.py` to update all skill dates from 2025 to 2026.
- Enhanced user experience by moving "uncategorized" to the bottom of the category list and displaying skill counts in the dropdown.
2026-02-26 12:52:03 +01:00

276 lines
11 KiB
Python

#!/usr/bin/env python3
"""
Auto-categorize skills based on their names and descriptions.
Removes "uncategorized" by intelligently assigning categories.
Usage:
python auto_categorize_skills.py
python auto_categorize_skills.py --dry-run (shows what would change)
"""
import os
import re
import json
import sys
import argparse
# Ensure UTF-8 output for Windows compatibility
if sys.platform == 'win32':
import io
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
# Category keywords mapping
CATEGORY_KEYWORDS = {
'web-development': [
'react', 'vue', 'angular', 'svelte', 'nextjs', 'gatsby', 'remix',
'html', 'css', 'javascript', 'typescript', 'frontend', 'web', 'tailwind',
'bootstrap', 'sass', 'less', 'webpack', 'vite', 'rollup', 'parcel',
'rest api', 'graphql', 'http', 'fetch', 'axios', 'cors',
'responsive', 'seo', 'accessibility', 'a11y', 'pwa', 'progressive',
'dom', 'jsx', 'tsx', 'component', 'router', 'routing'
],
'backend': [
'nodejs', 'node.js', 'express', 'fastapi', 'django', 'flask',
'spring', 'java', 'python', 'golang', 'rust', 'c#', 'csharp',
'dotnet', '.net', 'laravel', 'php', 'ruby', 'rails',
'server', 'backend', 'api', 'rest', 'graphql', 'database',
'sql', 'mongodb', 'postgres', 'mysql', 'redis', 'cache',
'authentication', 'auth', 'jwt', 'oauth', 'session',
'middleware', 'routing', 'controller', 'model'
],
'database': [
'database', 'sql', 'postgres', 'postgresql', 'mysql', 'mariadb',
'mongodb', 'nosql', 'firestore', 'dynamodb', 'cassandra',
'elasticsearch', 'redis', 'memcached', 'graphql', 'prisma',
'orm', 'query', 'migration', 'schema', 'index'
],
'ai-ml': [
'ai', 'artificial intelligence', 'machine learning', 'ml',
'deep learning', 'neural', 'tensorflow', 'pytorch', 'scikit',
'nlp', 'computer vision', 'cv', 'llm', 'gpt', 'bert',
'classification', 'regression', 'clustering', 'transformer',
'embedding', 'vector', 'embedding', 'training', 'model'
],
'devops': [
'devops', 'docker', 'kubernetes', 'k8s', 'ci/cd', 'git',
'github', 'gitlab', 'jenkins', 'gitlab-ci', 'github actions',
'aws', 'azure', 'gcp', 'terraform', 'ansible', 'vagrant',
'deploy', 'deployment', 'container', 'orchestration',
'monitoring', 'logging', 'prometheus', 'grafana'
],
'cloud': [
'aws', 'amazon', 'azure', 'gcp', 'google cloud', 'cloud',
'ec2', 's3', 'lambda', 'cloudformation', 'terraform',
'serverless', 'functions', 'storage', 'cdn', 'distributed'
],
'security': [
'security', 'encryption', 'cryptography', 'ssl', 'tls',
'hashing', 'bcrypt', 'jwt', 'oauth', 'authentication',
'authorization', 'firewall', 'penetration', 'audit',
'vulnerability', 'privacy', 'gdpr', 'compliance'
],
'testing': [
'test', 'testing', 'jest', 'mocha', 'jasmine', 'pytest',
'unittest', 'cypress', 'selenium', 'puppeteer', 'e2e',
'unit test', 'integration', 'coverage', 'ci/cd'
],
'mobile': [
'mobile', 'android', 'ios', 'react native', 'flutter',
'swift', 'kotlin', 'objective-c', 'app', 'native',
'cross-platform', 'expo', 'cordova', 'xamarin'
],
'game-development': [
'game', 'unity', 'unreal', 'godot', 'canvas', 'webgl',
'threejs', 'babylon', 'phaser', 'sprite', 'physics',
'collision', '2d', '3d', 'shader', 'rendering'
],
'data-science': [
'data', 'analytics', 'science', 'pandas', 'numpy', 'scipy',
'jupyter', 'notebook', 'visualization', 'matplotlib', 'plotly',
'statistics', 'correlation', 'regression', 'clustering'
],
'automation': [
'automation', 'scripting', 'selenium', 'puppeteer', 'robot',
'workflow', 'automation', 'scheduled', 'trigger', 'integration'
],
'content': [
'markdown', 'documentation', 'content', 'blog', 'writing',
'seo', 'meta', 'schema', 'og', 'twitter', 'description'
]
}
def categorize_skill(skill_name, description):
"""
Intelligently categorize a skill based on name and description.
Returns the best matching category or None if no match.
"""
combined_text = f"{skill_name} {description}".lower()
# Score each category based on keyword matches
scores = {}
for category, keywords in CATEGORY_KEYWORDS.items():
score = 0
for keyword in keywords:
# Prefer exact phrase matches with word boundaries
if re.search(r'\b' + re.escape(keyword) + r'\b', combined_text):
score += 2
elif keyword in combined_text:
score += 1
if score > 0:
scores[category] = score
# Return the category with highest score
if scores:
best_category = max(scores, key=scores.get)
return best_category
return None
def auto_categorize(skills_dir, dry_run=False):
"""Auto-categorize skills and update generate_index.py"""
skills = []
categorized_count = 0
already_categorized = 0
failed_count = 0
for root, dirs, files in os.walk(skills_dir):
dirs[:] = [d for d in dirs if not d.startswith('.')]
if "SKILL.md" in files:
skill_path = os.path.join(root, "SKILL.md")
skill_id = os.path.basename(root)
try:
with open(skill_path, 'r', encoding='utf-8') as f:
content = f.read()
# Extract name and description from frontmatter
fm_match = re.search(r'^---\s*\n(.*?)\n---', content, re.DOTALL)
if not fm_match:
continue
fm_text = fm_match.group(1)
metadata = {}
for line in fm_text.split('\n'):
if ':' in line and not line.strip().startswith('#'):
key, val = line.split(':', 1)
metadata[key.strip()] = val.strip().strip('"').strip("'")
skill_name = metadata.get('name', skill_id)
description = metadata.get('description', '')
current_category = metadata.get('category', 'uncategorized')
# Skip if already has a meaningful category
if current_category and current_category != 'uncategorized':
already_categorized += 1
skills.append({
'id': skill_id,
'name': skill_name,
'current': current_category,
'action': 'SKIP'
})
continue
# Try to auto-categorize
new_category = categorize_skill(skill_name, description)
if new_category:
skills.append({
'id': skill_id,
'name': skill_name,
'current': current_category,
'new': new_category,
'action': 'UPDATE'
})
if not dry_run:
# Update the SKILL.md file - add or replace category
fm_start = content.find('---')
fm_end = content.find('---', fm_start + 3)
if fm_start >= 0 and fm_end > fm_start:
frontmatter = content[fm_start:fm_end+3]
body = content[fm_end+3:]
# Check if category exists in frontmatter
if 'category:' in frontmatter:
# Replace existing category
new_frontmatter = re.sub(
r'category:\s*\w+',
f'category: {new_category}',
frontmatter
)
else:
# Add category before the closing ---
new_frontmatter = frontmatter.replace(
'\n---',
f'\ncategory: {new_category}\n---'
)
new_content = new_frontmatter + body
with open(skill_path, 'w', encoding='utf-8') as f:
f.write(new_content)
categorized_count += 1
else:
skills.append({
'id': skill_id,
'name': skill_name,
'current': current_category,
'action': 'FAILED'
})
failed_count += 1
except Exception as e:
print(f"❌ Error processing {skill_id}: {str(e)}")
# Print report
print("\n" + "="*70)
print("AUTO-CATEGORIZATION REPORT")
print("="*70)
print(f"\n📊 Summary:")
print(f" ✅ Categorized: {categorized_count}")
print(f" ⏭️ Already categorized: {already_categorized}")
print(f" ❌ Failed to categorize: {failed_count}")
print(f" 📈 Total processed: {len(skills)}")
if categorized_count > 0:
print(f"\n📋 Sample changes:")
for skill in skills[:10]:
if skill['action'] == 'UPDATE':
print(f"{skill['id']}")
print(f" {skill['current']}{skill['new']}")
if dry_run:
print(f"\n🔍 DRY RUN MODE - No changes made")
else:
print(f"\n💾 Changes saved to SKILL.md files")
return categorized_count
def main():
parser = argparse.ArgumentParser(
description="Auto-categorize skills based on content",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python auto_categorize_skills.py --dry-run
python auto_categorize_skills.py
"""
)
parser.add_argument('--dry-run', action='store_true',
help='Show what would be changed without making changes')
args = parser.parse_args()
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
skills_path = os.path.join(base_dir, "skills")
auto_categorize(skills_path, dry_run=args.dry_run)
if __name__ == "__main__":
main()