sqli-sql-injection
SQL injection playbook. Use when input reaches SQL queries, authentication logic, sorting, filtering, reporting, or DB-specific blind and out-of-band execution paths.
SQL injection playbook. Use when input reaches SQL queries, authentication logic, sorting, filtering, reporting, or DB-specific blind and out-of-band execution paths.
Design optimized database schemas for SQL and NoSQL databases including tables, relationships, indexes, and constraints. Creates ERD diagrams, migration scripts, and data modeling best practices. Use when users need database design, schema optimization, or data architecture planning.
Migrates databases between providers (Postgres, MySQL, Supabase, PlanetScale, MongoDB). Reads source schema, generates migration scripts, handles data type mapping, foreign keys, indexes, triggers, stored procedures. Validates migration with row counts and checksums. Generates migration-plan.md with step-by-step execution guide, rollback procedures, estimated downtime.
Creates and manages ephemeral Neon databases for testing, CI/CD pipelines, and isolated development environments. Use when building temporary databases for automated tests or rapid prototyping.
Creates a fully functional Drizzle ORM setup with a provisioned Neon database. Installs dependencies, provisions database credentials, configures connections, generates schemas, and runs migrations. Results in working code that can immediately connect to and query the database. Use when creating new projects with Drizzle, adding ORM to existing applications, or modifying database schemas.
Redis caching and queue patterns. Use when implementing caching, rate limiting, session storage, pub/sub, or background job queues with Redis.
PostgreSQL schema design, indexing, and query patterns. Use when designing database schemas, writing migrations, optimizing queries, or working with any PostgreSQL database.
SQLAlchemy 2.0 async ORM patterns. Use when defining models, relationships, queries, or migrations with SQLAlchemy in Python.
Use this skill whenever question must be answered based on database information. Triggers include queries about simple database questions, specific metrics, trends, comparisons, or insights from data, asking for insights, pattern, trends, outliers, optimizing sql queries, or requesting specific information. Also trigger when the user says things like "how many", "analyze this data", "query the database", or "find trends in..."
Caching patterns for web applications, APIs, and data layers. Use this skill when implementing memoization, HTTP cache headers, Redis caching, CDN configuration, or in-memory caches. Trigger whenever code deals with Cache-Control headers, ETags, functools.lru_cache, React useMemo, TanStack Query cache, or any caching strategy. Also applies to cache invalidation, TTL policies, and cache-aside patterns.
Use this skill whenever working with PostgreSQL databases, writing SQL queries, designing schemas, or optimizing database performance. Trigger on keywords like PostgreSQL, Postgres, SQL query, schema design, indexing, migrations, EXPLAIN ANALYZE, connection pooling, or any relational database operation. Also applies when debugging slow queries, setting up database tables, or working with ORMs that target PostgreSQL.
Optimizes Snowflake SQL query performance from provided query text. Use when optimizing Snowflake SQL for: (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions "slow query", "make faster", "improve performance", "optimize SQL", or "query tuning" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up
Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.
Finds and ranks expensive Snowflake queries by cost, time, or data scanned. Use when: (1) User asks to find slow, expensive, or problematic queries (2) Task mentions "query history", "top queries", "most expensive", or "slowest queries" (3) Analyzing warehouse costs or identifying optimization candidates (4) Finding queries that scan the most data or have the most spillage Returns ranked list of queries with metrics and optimization recommendations.
Develops and troubleshoots dbt incremental models. Use when working with incremental materialization for: (1) Creating new incremental models (choosing strategy, unique_key, partition) (2) Task mentions "incremental", "append", "merge", "upsert", or "late arriving data" (3) Troubleshooting incremental failures (merge errors, partition pruning, schema drift) (4) Optimizing incremental performance or deciding table vs incremental Guides through strategy selection, handles common incremental gotchas.
Manage PostgreSQL databases: run queries, create tables, manage users, backup and restore databases, execute migration scripts. Not for: MongoDB, Redis, or other NoSQL databases.
Translate natural language requests into NixOS configuration changes. Show diffs before applying. Use nixos-rebuild for atomic deploys. Every change is rollbackable and logged.
Formats SQL queries for better readability and consistency. Supports PostgreSQL, MySQL, and SQLite syntax. Not for: database schema design, query optimization, or data migration.
Migrate a Twirl SPARQL template (.scala.txt) to a type-safe rdf4j query builder with test-first workflow. Use this skill whenever the user wants to migrate a SPARQL query, convert a Twirl template, or mentions migrating to rdf4j. Triggers on: migrate query, migrate twirl, convert sparql, migrate rdf4j, migrate to rdf4j.
Guide for Convex performance optimization including denormalization, index design, avoiding N+1 queries, OCC (Optimistic Concurrency Control), and handling hot spots. Use when optimizing query performance, designing data models, handling high-contention writes, or troubleshooting OCC errors. Activates for performance issues, index optimization, denormalization patterns, or concurrency control tasks.
Schema migration agent for upgrading JSON schema versions with data validation. Use when user says "migrate schema", "upgrade schema version", "schema migration", "bump schema", "update schema", "version upgrade", "data migration", "schema version bump", "migrate data format", "schema evolution".