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Found 798 Skills
Deploy and orchestrate 38 MCP servers for offensive security tools (Nmap, Nuclei, Ghidra, SQLMap, etc.) via Docker
Query MaxCompute (ODPS) Information Schema metadata views. Tenant-level (SYSTEM_CATALOG.INFORMATION_SCHEMA.*, recommended) or project-level (Information_Schema.*, deprecated). NL→SQL for IS views: tables, columns, partitions, tasks_history, tunnels_history, table_privileges, users, user_roles, quota_usage, etc. NOT for: DDL/DML, listing tables via MCP, running ad-hoc SQL, general MaxCompute questions.
Complete guide to implementing the Syncfusion QueryBuilder component in ASP.NET Core applications. Use this when working with visual query/filter builders, rule-based filtering UI, SQL/JSON/MongoDB query generation, drag-and-drop rule reordering, or import/export of filter conditions using Syncfusion EJ2 TagHelpers.
Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.
Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.
Build type-safe D1 databases with Drizzle ORM. Includes schema definition, migrations with Drizzle Kit, relations, and D1 batch API patterns. Prevents 18 errors including SQL BEGIN failures, cascade data loss, 100-parameter limits, and foreign key issues. Use when: defining D1 schemas, managing migrations, bulk inserts, or troubleshooting D1_ERROR, BEGIN TRANSACTION, foreign keys, "too many SQL variables".
Patterns for SQLite databases in Python projects - state management, caching, and async operations. Triggers on: sqlite, sqlite3, aiosqlite, local database, database schema, migration, wal mode.
Google Cloud Platform CLI (gcloud, gcloud storage, bq). Use when: managing GCP resources, deploying to Cloud Run/Cloud Functions/GKE/App Engine, working with Cloud Storage, BigQuery, IAM, Compute Engine, Cloud SQL, Pub/Sub, Secret Manager, Artifact Registry, Cloud Build, Cloud Scheduler, Cloud Tasks, Vertex AI, VPC/networking, DNS, logging/monitoring, or any GCP service. Also covers: authentication, project/config management, CI/CD integration, serverless deployments, container registry, docker push to GCP, managing secrets, Workload Identity Federation, and infrastructure automation.
Conduct rigorous, adversarial code reviews with zero tolerance for mediocrity. Use when users ask to "critically review" my code or a PR, "critique my code", "find issues in my code", or "what's wrong with this code". Identifies security holes, lazy patterns, edge case failures, and bad practices across Python, R, JavaScript/TypeScript, SQL, and front-end code. Scrutinizes error handling, type safety, performance, accessibility, and code quality. Provides structured feedback with severity tiers (Blocking, Required, Suggestions) and specific, actionable recommendations.
Ultimate 25+ years expert-level backend skill covering FastAPI, Express, Node.js, Next.js with TypeScript. Includes ALL databases (PostgreSQL, MongoDB, Redis, Elasticsearch), ALL features (REST, GraphQL, WebSockets, gRPC, Message Queues), comprehensive security hardening (XSS, CSRF, SQL injection, authentication, authorization, rate limiting), complete performance optimization (caching, database tuning, load balancing), ALL deployment strategies (Docker, Kubernetes, CI/CD), advanced patterns (microservices, event-driven, saga, CQRS), ALL use cases (e-commerce, SaaS, real-time, high-traffic), complete testing (unit, integration, E2E, load, security). Route protection, middleware, authentication implementation in PERFECTION. Use for ANY backend system requiring enterprise-grade security, performance, scalability, and architectural excellence.
Develop and deploy Lakeflow Jobs on Databricks. Use when creating data engineering jobs with notebooks, Python wheels, or SQL tasks. Invoke BEFORE starting implementation.
Lovrabet development workflow CLI — Manage datasets, SQL queries, BFF scripts and code generation via the rabetbase command. Trigger words: dataset, data table, custom SQL, sql.execute, bff.execute, get_dataset_detail, validate_sql_content, save_or_update_custom_sql, @lovrabet/sdk, lovrabet development, rabetbase, filter, codegen.