2.9K skills · sorted by relevance
Audit game code for save tampering, cheating, network weaknesses, input validation gaps, exposed data, and dependency risks. Produce a report with severity ratings, repair priorities, release recommendations, and explicit notes about areas that could not be assessed.
Delegate checks for AGENTS.md rule violations to a dedicated agent. It scans the codebase, fixes high-impact issues in a separate working copy, runs full validation, and creates a pull request, tracking progress across runs through memory.
Validate the staging NemoClaw Brev Launchable by checking its web deployment flow, environment access, deployed image, and preinstalled user journey. Report inference results and missing dependencies separately as advisory evidence outside automated end-to-end testing.
Implement or modify GitButler’s but commands using its established structure. It covers typed arguments, input validation, execution, and human-readable, shell, or JSON output, while keeping operations usable from the terminal interface.
Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.
Design hybrid search on AlloyDB by combining meaning-based search, keywords, and SQL filters. Turn requirements into architecture diagrams, database setup scripts, deployment guidance, and validation plans for search applications on Google Cloud.
Design AI agent analytics workflows across distributed tables and documents. Combine Knowledge Catalog for metadata, meaning information about data and its usage rules, with managed Spark processing to produce architecture diagrams, deployment guidance, and validation plans.
Design a Google Cloud system that combines multiple services, starting with a discussion of business and technical requirements. The skill produces service recommendations, architecture diagrams, deployment guidance, and validation plans, with user approval at each stage.
Helps create and extend Apache Airflow DAGs, workflows that define task order and dependencies, for Managed Service for Apache Airflow. Covers environment checks, version compatibility, coding practices, and local or deployed validation.
Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.
Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.
Guide a Trae agent through recurring failures with diagnosis, alternative approaches, and relevant checks. The skill defines when to intervene, requires evidence for completion, and separates the agent’s self-review from external validation.
Fill and submit web forms with dropdowns, checkboxes, radio buttons, file uploads, and directly editable fields. Check unclear fields before acting, inspect visual states when needed, and verify the result after submission.
Build Vue 3 interfaces from JSON specifications, including AI-generated layouts. The skill covers component registration, named content areas, synchronized form values, conditional visibility, validation, actions, and connections to external state management.
Build Svelte interfaces for json-render using 36 ready-made components. This skill guides component selection, connects component definitions to implementations, and explains form validation settings for apps using Svelte 5 and Tailwind CSS.
Build json-render interfaces with 36 ready-made UI components, including forms, tables, and navigation. This skill guides component selection, connects definitions to React implementations, and explains custom components and input validation.
Use past local Codex sessions to propose updates to a selected skill and project memory file. Recurring tasks are replayed, changes are checked on separate validation tasks, and proposals are staged for review before adoption.
Turn an explicit request for a coding plan into an ordered checklist. Review project documentation and relevant files, define scope, and outline implementation, validation, and risks without changing any files.
Build MCP servers, which give AI agents tools for external services, using Python or TypeScript. The skill guides tool design, input validation, error handling, code review, and evaluations based on realistic tasks.
Create, migrate, refine, or debug CARLA vehicle assets with Blender and Unreal. Use real vehicle references to guide geometry and materials, configure wheels, lights, and physics, and deliver editable sources with evidence from runtime validation.
Add safety checks to LLM applications with NeMo Guardrails. This skill guides input and output validation, detection of attempts to bypass safety rules, personal information masking, and checks for unsupported or inaccurate answers.
Organize PyTorch training code with LightningModule and Trainer. This skill guides training, validation, logging, model checkpoint saving, early stopping when progress stalls, and configuration for running the same model across multiple GPUs.
Build NestJS APIs with TypeScript code for modules, request handlers, services, authentication, input validation, and database access. The skill also prepares Swagger API documentation and unit and end-to-end tests for the implemented features.
Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.