Databases

GitHub PR workflow automation including fetching unresolved comments, resolving review threads, and parallel comment resolution. Use this skill when working with PR reviews, addressing reviewer feedback, or automating PR comment workflows.

Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.

Write focused tests for core user flows and critical paths with clear test names, behavior-focused assertions, mocked external dependencies, and fast execution, deferring edge case testing until explicitly required. Use this skill when creating or modifying test files, writing unit tests, integration tests, or test cases for any feature. Apply when writing test files (test/, __tests__/, spec/, .test.js, .spec.ts, test_*.py), implementing tests for core user workflows, testing critical business logic, mocking external dependencies (databases, APIs, file systems), writing descriptive test names, creating fast-running unit tests, or adding tests at logical completion points of feature development. Use for any task involving test creation, test coverage, test strategy, or test-driven development.

Expert guidance for testing and validating GitHub Actions workflows before deployment - catches cache errors, path issues, monorepo dependencies, and service container problems that local testing misses

Implement comprehensive validation with server-side enforcement, client-side UX feedback, early failure, specific error messages, allowlists over blocklists, type checking, input sanitization, and consistent validation across all entry points. Use this skill when implementing validation logic in forms, API endpoints, data models, user inputs, or any data processing. Apply when validating form inputs, API request parameters, database model fields, implementing client-side validation for user experience, enforcing server-side validation for security, sanitizing user input to prevent injection attacks, checking data types and formats, validating business rules, or providing field-specific error messages. Use for any task involving input validation, data integrity checks, security validation, or user input processing.

Write secure, optimized database queries using parameterized queries, eager loading to prevent N+1 problems, and strategic indexing for performance. Use this skill when writing SQL queries, ORM queries, database interactions, or optimizing data fetching logic. Apply when working with query files, repository patterns, data access layers, SQL statements, ORM methods (ActiveRecord, Sequelize, Prisma queries), JOIN operations, WHERE clauses, preventing SQL injection, implementing eager loading or includes, adding query timeouts, wrapping operations in transactions, or caching expensive queries. Use for any task involving database reads, writes, complex queries, query optimization, or data fetching performance.

Design and implement RESTful API endpoints following REST principles with proper HTTP methods, status codes, and resource-based URLs. Use this skill when creating or modifying API endpoints, route handlers, controllers, or API configuration files. Apply when working on REST API design, endpoint implementations, API versioning, request/response handling, HTTP method routing (GET, POST, PUT, PATCH, DELETE), query parameter filtering, API rate limiting, or any file that defines API routes such as routes.py, api.js, controllers/, endpoints/, or API documentation files.

Expert assistant for using litdb - a literature and document database for scientific research

Create and manage database migrations with reversible changes, proper naming conventions, and zero-downtime deployment strategies. Use this skill when creating database migration files, modifying schema, adding or removing tables/columns, managing indexes, or handling data migrations. Apply when working with migration files (e.g., db/migrate/, migrations/, alembic/, sequelize migrations), schema changes, database versioning, rollback implementations, or when you need to ensure backwards compatibility during deployments. Use for any task involving database structure changes, index creation, constraint modifications, or data transformation scripts.

Comprehensive guide to PostgreSQL migrations - common errors, generated columns, full-text search, indexes, idempotent migrations, and best practices for database schema changes

Generate safe, read-only PostgreSQL queries from natural language. Use when users need to query blog_small, ecommerce_medium, or saas_crm_large databases. Supports query generation, execution, and result analysis with confidence scoring.

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases. Teaches thinking, not fixed SQL.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.