This skill guides building high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Use when creating MCP servers, designing tool interfaces, or implementing protocol handlers.
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.
Fetches up-to-date documentation for technologies used in the Personal AI Employee project via Context7 MCP server. Use when you need current API documentation, library references, code examples, or implementation guidance for any technology in the stack.
Guidelines for using Zen MCP tools effectively in this repo. Use for complex multi-model tasks, architectural decisions, or when cross-model validation adds value.
Executes database migrations using the postgres MCP server. Use when applying schema changes, running migrations, or updating database structure.
Executes database migrations using the postgres MCP server. Use when applying schema changes, running migrations, or updating databese structure.
Execute SQL queries, migrations, and RLS policies on Supabase database using MCP tools
Create and manage Supabase database migrations using MCP tools. Use when creating tables, modifying schemas, adding RLS policies, or working with database structure.
Workflow chuẩn khi làm việc với Supabase (DDL/migrations, query/debug, RLS policies) cho repo này.
Designs Redis caching strategies for Braiins API data, optimizing for data freshness vs. API rate limits and response latency.
Redis caching strategies for MCP servers - cache invalidation, TTL management, pub/sub patterns, and performance optimization
MongoDB database development - queries, aggregations, schema analysis using MCP tools
PostgreSQL MCPでスキーマ確認とread-only分析を行う。集計・意思決定資料が必要な時に使う。
Neo4j driver best practices for Python. Use when working with Neo4j connections, Cypher queries, transactions, or GraphRAG implementations.
Expert in Microsoft SQL Server development and administration. Use when writing T-SQL queries, optimizing database performance, designing schemas, configuring SQL Server, or integrating SQL Server with Node.js using mssql package.
SQLite problem-solving patterns for embedded/edge deployments. Use when: connection setup, "database locked" errors, ALTER TABLE workarounds, concurrency problems, FTS setup, SQLite-specific gotchas. Do not use for: basic DDL/DML syntax (use context7 MCP), schema design decisions (use data-modeling skill first). Workflow: data-modeling skill (design) → this skill (implement). Decision: If asking "should I use PostgreSQL instead?" — see comparison section at bottom.
Master third-party API integration in ANY language with best practices and patterns
Systematic debugging approach for ANY codebase, ANY language, ANY bug type. Use when facing unexpected behavior, crashes, performance issues, or intermittent problems.
Master third-party API integration in ANY language with best practices and patterns
Generates hierarchical knowledge graphs via Recursive Pareto Principle for optimised schema construction. Produces four-level structures (L0 meta-graph through L3 detail-graph) where each level contains 80% fewer nodes while grounding 80% of its derivative, achieving 51% coverage from 0.8% of nodes via Pareto³ compression. Use when creating domain ontologies or knowledge architectures requiring: (1) Atomic first principles with emergent composites, (2) Pareto-optimised information density, (3) Small-world topology with validated node ratios (L1:L2 2-3:1), or (4) Bidirectional construction. Integrates with graph (η≥4 validation), abduct (refactoring), mega (SuperHyperGraphs), infranodus (gap detection). Triggers: 'schema generation', 'ontology creation', 'Pareto hierarchy', 'recursive graph', 'first principles decomposition'.
