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Found 924 Skills
Create and edit videos using Google's Veo 2 and Veo 3 models. Supports Text-to-Video, Image-to-Video, Inpainting, and Advanced Controls.
Use Orchata MCP tools to search, browse, and manage knowledge bases programmatically. For MCP-connected environments only.
Translate text between English and Indian languages using Sarvam AI's Mayura model. Use when the user needs to translate content, localize applications, or convert text between Hindi, Tamil, Bengali, Telugu, and 7 other Indian languages. Supports bidirectional translation, script control, and code-mixed text.
Create custom Genfeed nodes using the SDK. Triggers on "create a new node", "add a custom node type", "build a node for X".
Guides creation of Zhin plugins with lifecycle hooks, auto-loading, and hot-reload behavior. Use when developers need plugin structure, lifecycle events, or reloading details.
Generates a case study aligned with Digital Speed brand voice. Use when asked to write a case study, success story, or client spotlight.
This skill provides guidance for implementing headless terminal interfaces that programmatically control shell sessions. Use this skill when implementing terminal emulation, pseudo-terminal wrappers, or interfaces like BaseTerminal that require sending keystrokes and reading output from shell processes.
Integrate Portkey AI Gateway into TypeScript/JavaScript applications. Use when building LLM apps with observability, caching, fallbacks, load balancing, or routing across 200+ LLM providers.
Configure secret stores in Spice (environment variables, Kubernetes, AWS Secrets Manager, keyring). Use when asked to "configure secrets", "add API keys", "set up credentials", "manage passwords", "use environment variables", or "configure .env file".
Configure data accelerators for local materialization and caching in Spice (Arrow, DuckDB, SQLite, Cayenne, PostgreSQL, Turso). Use when asked to "accelerate data", "enable caching", "materialize dataset", "configure refresh", "set up local storage", "improve query performance", "choose an accelerator", or "configure snapshots".
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.