Browse and install thousands of AI Agent skills in the Killer-Skills directory. Supports Claude Code, Windsurf, Cursor, and more.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
README.md is a skill that enables natural language interaction with Neon Postgres databases, providing an intuitive interface for database management.
A Claude Code plugin for reviewing CPython C extensions — finding API misuse, memory safety bugs, compatibility issues, and correctness problems.
angular-forms is a feature of Angular that provides a way to build type-safe, reactive forms using the Signal Forms API, offering automatic two-way binding and schema-based validation.
An open-source, self-hosted API that turns standard email providers (Mailgun, SES, SendGrid) into "Inbox-as-a-Service" for AI Agents.
hono-js is a JavaScript framework for building backend endpoints with a structured project layout and OpenAPI support.
native-data-fetching is a skill for managing networking tasks, such as API requests, data fetching, and caching, in Expo and React Native environments.
MCP server for LogicMonitor REST API v3 integration. Enables AI assistants to interact with LogicMonitor monitoring data through structured tools.
An little API thats allow user to upload, download, storage files, just like a Mini Google Drive :))
KECS (Kubernetes-based ECS Compatible Service) is a standalone service that provides Amazon ECS compatible APIs running on Kubernetes.
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.