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database-optimization

SQL query optimization and database performance specialist. Use when optimizing slow queries, fixing N+1 problems, designing indexes, implementing caching, or improving database performance. Works with PostgreSQL, MySQL, and other databases.

aiskillstore/marketplace

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含まれるファイル（3）

SKILL.md3.1 KB
references/query-patterns.md3.3 KB
skill-report.json22.3 KB

SKILL.md（原文）

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Database Optimization

This skill optimizes database performance including query optimization, indexing strategies, N+1 problem resolution, and caching implementation.

When to Use This Skill

When optimizing slow database queries
When fixing N+1 query problems
When designing indexes
When implementing caching strategies
When optimizing database migrations
When improving database performance

What This Skill Does

Query Optimization: Analyzes and optimizes SQL queries
Index Design: Creates appropriate indexes
N+1 Resolution: Fixes N+1 query problems
Caching: Implements caching layers (Redis, Memcached)
Migration Optimization: Optimizes database migrations
Performance Monitoring: Sets up query performance monitoring

How to Use

Optimize Queries

Optimize this slow database query

Fix the N+1 query problem in this code

Specific Analysis

Analyze query performance and suggest indexes

Optimization Areas

Query Optimization

Techniques:

Use EXPLAIN ANALYZE
Optimize JOINs
Reduce data scanned
Use appropriate indexes
Avoid SELECT *

Index Design

Strategies:

Index frequently queried columns
Composite indexes for multi-column queries
Avoid over-indexing
Monitor index usage
Remove unused indexes

N+1 Problem

Pattern:

# Bad: N+1 queries
users = User.all()
for user in users:
    posts = Post.where(user_id=user.id)  # N queries

# Good: Single query with JOIN
users = User.all().includes(:posts)  # 1 query

Examples

Example 1: Query Optimization

Input: Optimize slow user query

Output:

## Database Optimization: User Query

### Current Query
```sql
SELECT * FROM users
WHERE email = 'user@example.com';
-- Execution time: 450ms

Analysis

Full table scan (no index on email)
Scanning 1M+ rows

Optimization

-- Add index
CREATE INDEX idx_users_email ON users(email);

-- Optimized query
SELECT id, email, name FROM users
WHERE email = 'user@example.com';
-- Execution time: 2ms

Impact

Query time: 450ms → 2ms (99.5% improvement)
Index size: ~50MB


## Best Practices

### Database Optimization

1. **Measure First**: Use EXPLAIN ANALYZE
2. **Index Strategically**: Not every column needs an index
3. **Monitor**: Track slow query logs
4. **Cache**: Cache expensive queries
5. **Denormalize**: When justified by read patterns

## Reference Files

- **`references/query_patterns.md`** - Common query optimization patterns, anti-patterns, and caching strategies

## Related Use Cases

- Query optimization
- Index design
- N+1 problem resolution
- Caching implementation
- Database performance improvement

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概要と使いどころ

acquisition-policy-workflow

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Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.

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