asyncpg-detection
This skill should be used when the user asks to "detect asyncpg usage", "find asyncpg patterns", "scan for asyncpg imports", or "identify asyncpg database code in FastAPI projects". It automatically scans Python files to identify asyncpg imports, connection patterns, and query execution methods that need conversion to SQLAlchemy.
When & Why to Use This Skill
This Claude skill automates the detection and analysis of asyncpg usage within FastAPI projects, streamlining the transition to SQLAlchemy. It identifies specific code patterns, including imports, connection pools, and query execution methods, to provide a structured roadmap for database layer modernization and refactoring.
Use Cases
- Database Migration: Identifying all raw asyncpg queries and connection patterns in a FastAPI project to facilitate a move to SQLAlchemy ORM.
- Technical Debt Audit: Scanning legacy Python codebases to locate direct database driver dependencies that need to be abstracted.
- Refactoring Estimation: Generating detailed reports with line numbers and complexity assessments to help developers estimate the effort required for database-related code updates.
- Code Standardization: Ensuring consistency across microservices by flagging non-standard asyncpg connection methods or manual transaction handling.
| name | asyncpg-detection |
|---|---|
| description | This skill should be used when the user asks to "detect asyncpg usage", "find asyncpg patterns", "scan for asyncpg imports", or "identify asyncpg database code in FastAPI projects". It automatically scans Python files to identify asyncpg imports, connection patterns, and query execution methods that need conversion to SQLAlchemy. |
| version | 1.0.0 |
AsyncPG Detection for FastAPI Projects
This skill provides comprehensive detection of asyncpg usage patterns in FastAPI applications, identifying all code that needs to be converted to SQLAlchemy with asyncpg engine support.
Detection Overview
Scan FastAPI projects for asyncpg patterns including imports, connection management, queries, transactions, and error handling. Generate detailed reports with line numbers and conversion recommendations.
Core Detection Patterns
Import Detection
Look for these import statements:
import asyncpgfrom asyncpg importimport asyncpg as pgfrom asyncpg import Connection, Pool
Connection Patterns
Identify these asyncpg connection approaches:
asyncpg.connect()callsasyncpg.create_pool()usage- Manual connection string parsing
- Environment-based connection configuration
Query Patterns
Detect these asyncpg execution methods:
connection.fetch()for SELECT queriesconnection.execute()for INSERT/UPDATE/DELETEconnection.fetchval()for single valuesconnection.fetchrow()for single rowsconnection.iter()for result iteration
Usage Instructions
To detect asyncpg usage in your FastAPI project:
- Run comprehensive scan: Use the
/convert-asyncpg-to-sqlalchemycommand to scan all Python files in the project - Analyze detection results: Review the generated report for files containing asyncpg code
- Prioritize conversion: Focus on files with the most asyncpg usage first
- Check for complex patterns: Look for nested connections, transactions, and error handling that may require special attention
Reporting Format
The detection generates reports with:
- File list: All files containing asyncpg imports
- Pattern analysis: Specific asyncpg methods found
- Complexity assessment: Files requiring manual intervention
- Conversion recommendations: Suggested SQLAlchemy equivalents
Additional Resources
Reference Files
references/patterns-mapping.md- Complete asyncpg to SQLAlchemy pattern mappingreferences/complex-cases.md- Handling of complex asyncpg scenariosreferences/supabase-specific.md- Supabase-specific asyncpg patterns
Examples
examples/detection-report.md- Sample detection outputexamples/fastapi-project-structure.md- Example FastAPI project with asyncpg usage