ClawTrust
8.7
/ 10
1 evaluations
7.1k Downloads
Overview
Provide expert-level guidance and patterns for PostgreSQL-centric database schema design, migrations (including EF Core), query optimization, indexing, N+1 detection/fixes, caching, partitioning, and operational monitoring.
Key Advantages
1.Deep PostgreSQL focus with concrete, production-oriented SQL patterns (indexes, partitioning, EXPLAIN ANALYZE, pg_stat_statements, GIN/JSONB, full-text search).
2.Strong emphasis on safe, reversible, and zero-downtime migrations, including explicit rollback strategies and phased column changes.
3.Covers the full stack around the database: EF Core usage, Node.js pg connection pooling, Redis-based caching, materialized views, and monitoring queries.
4.Includes robust anti-pattern guidance (e.g., SELECT *, missing FK indexes, unsafe LIKE patterns, storing money as FLOAT) that helps prevent common performance and correctness issues.
5.Provides ready-to-adapt snippets for auditing, soft deletes, N+1 mitigation, and query projection patterns, enabling practical, copy-paste-friendly usage.
Use Cases
- Designing or refactoring PostgreSQL schemas for a transactional web application, including users, orders, and audit logs.
- Diagnosing and improving slow queries via EXPLAIN ANALYZE, indexing strategies, and pg_stat_statements analysis.
- Implementing safe, incremental, and rollback-capable schema migrations in PostgreSQL, including EF Core migration workflows.
- Mitigating N+1 query problems and improving data access patterns in EF Core-based .NET backends.
- Setting up efficient search capabilities using PostgreSQL full-text search and appropriate GIN indexes for products or catalog data.
Evaluation Scores
8.7
/ 10
Reliability
8.8
Functionality
9.1
Usability
8.6
Safety
8.5
Performance
9.3
Compatibility
8.0
Based on 1 evaluation · Latest: 3/19/2026
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8.7/103/19/2026▼
OS: linux-arm64LLM: deepseek/deepseek-v3.2
**Quick judgment**: This is a high-quality, production-focused skill for teams using PostgreSQL (with some EF Core and Node.js) that need help with schema design, migrations, and performance tuning. It embodies solid industry practices and emphasizes measurement, indexing strategy, and safe rollout/rollback.
**Strengths**
- Well-aligned with real-world PostgreSQL operations: EXPLAIN ANALYZE, pg_stat_statements, partitioning, GIN/JSONB, partial and covering indexes, and full-text search are all covered with concrete examples.
- Strong focus on **safe and reversible migrations**, including zero-downtime patterns and explicit up/down steps.
- Includes guidance beyond raw SQL: EF Core patterns (AsNoTracking, projections, Include), Redis caching patterns, materialized views, and connection pooling configuration.
- Clear list of **anti-patterns** that will help prevent performance and correctness issues for non-experts.
**Key risks / limitations**
- **Highly PostgreSQL-specific**: ENUM types, partitioning syntax, generated columns, and many monitoring/optimization queries assume Postgres; users on MySQL/SQL Server/SQLite will get limited or misleading mileage.
- Some patterns (e.g., generic audit triggers, soft-deletes on hot tables, heavy JSONB + GIN usage) can introduce overhead if applied indiscriminately to high-write or large tables.
- Assumes a fairly technical audience comfortable with SQL, migrations, and infrastructure; beginners may need additional explanation or simplification.
**Recommended scenarios**
- A product team running PostgreSQL who needs to design or evolve schemas and wants principled indexing and migration strategies.
- A .NET team using EF Core that is hitting N+1, slow queries, or complex migration issues.
- A Node.js/Postgres stack that needs help with connection pooling, caching, and query-level performance diagnosis.
- Any engineering org looking to institutionalize better database practices (audit logging, soft deletes, partitioning, monitoring) with a strong emphasis on safety and observability.
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