compound-eng-postgresqlPostgreSQL schema design, query optimization, indexing, and administration. Use when working with PostgreSQL, JSONB, partitioning, RLS, CTEs, window function...
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clawdbot install iliaal/compound-eng-postgresqlGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated May 7, 2026
Design a PostgreSQL schema for an e-commerce platform with orders, products, and customers. Leverage JSONB for dynamic product attributes, implement RLS for multi-tenancy, and optimize query performance using composite indexes and partial indexes on order status.
Build a real-time analytics dashboard using PostgreSQL materialized views and partitioning. Use RANGE partitioning on timestamp columns for time-series data, and BRIN indexes for fast aggregation queries on large datasets.
Implement row-level security (RLS) to isolate tenant data in a shared PostgreSQL database. Use JSONB for flexible tenant-specific configurations, and ensure performance with expression indexes on common query patterns.
Design a PostgreSQL-backed fraud detection system that uses GIN indexes on JSONB data for rapid pattern matching against transaction metadata. Implement exclusion constraints to prevent overlapping transaction windows and use advisory locks for concurrent processing.
Create a content management system that leverages PostgreSQL full-text search with GIN indexes on tsvector columns. Use partial indexes to filter by content status, and materialized views for caching popular search results.
Offer a free tier with limited database queries and storage, enforced via PostgreSQL row-level security and custom functions tracking usage. Upsell premium features like higher query limits and advanced analytics.
Provide a RESTful API for real-time analytics, charging per query or per data point. Use PostgreSQL's pg_stat_statements to monitor query performance and bill customers based on their actual usage.
Enable third-party data providers to list datasets in a marketplace, with PostgreSQL enforcing access control via RLS and JSONB for flexible schema. Charge a commission on each data sale or a flat listing fee.
💬 Integration Tip
Use pg_stat_statements to identify slow queries and index opportunities. For multi-tenant apps, always force RLS on all tables and use security definer functions for complex policy checks.
Scored May 7, 2026
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Local search/indexing CLI (BM25 + vectors + rerank) with MCP mode.
Access AI-powered football match predictions from hergunmac.com. Use when the user asks about football/soccer match predictions, betting tips, match analysis, team statistics, head-to-head data, or upcoming match insights. Covers worldwide leagues with confidence scores, AI reasoning, and historical performance tracking.
Use when designing database schemas, writing migrations, optimizing SQL queries, fixing N+1 problems, creating indexes, setting up PostgreSQL, configuring EF Core, implementing caching, partitioning tables, or any database performance question.
browse MongoDB Atlas Admin API specifications and execute operations (if credentials provided).
Voyage AI embedding and reranking CLI integrated with MongoDB Atlas Vector Search. Use for: generating text embeddings, reranking search results, storing embeddings in Atlas, performing vector similarity search, creating vector search indexes, listing available models, comparing text similarity, bulk ingestion, interactive demos, and learning about AI concepts. Triggers: embed text, generate embeddings, vector search, rerank documents, voyage ai, semantic search, similarity search, store embeddings, atlas vector search, embedding models, cosine similarity, bulk ingest, explain embeddings.