Total 57,111 skills, AI & Machine Learning has 9498 skills
Showing 12 of 9498 skills
Use when "writing prompts", "prompt optimization", "few-shot learning", "chain of thought", or asking about "RAG systems", "agent workflows", "LLM integration", "prompt templates"
DigitalOcean Gradient AI agentic cloud and AI platform for building, training, and deploying AI agents on GPU infrastructure with foundation models, knowledge bases, and agent routes. Use when planning or operating AI agents on DigitalOcean.
Create slash commands for Claude Code with $ARGUMENTS handling, agent invocation patterns, and template best practices. Reference for building user-triggered workflow shortcuts.
AI-powered web search, research, and reasoning via Perplexity
Only to be triggered by explicit super-swarm commands.
Generate voice messages using local Qwen3-TTS (offline, Apple Silicon). Convert text to speech with customizable voices, emotions, and speed. Use when user asks for voice reply, audio, or TTS.
Convert text to natural speech using Sarvam AI's Bulbul v3 model. Handles audio generation, voiceovers, and voice interfaces for 11 Indian languages with 30+ voices. Supports REST, HTTP streaming, WebSocket, and pronunciation dictionaries. Use when generating spoken audio from text.
Use this skill to interact with Moorcheh, the Universal Memory Layer for Agentic AI. Provides semantic search with ITS (Information-Theoretic Scoring), namespace management, text and vector data operations, and AI-powered answer generation (RAG). Use when building applications that need semantic search, knowledge bases, document Q&A, AI memory systems, or retrieval-augmented generation.
Use when users want to maintain persistent memory across sessions, track user preferences, store important decisions, manage tasks and reminders, or provide personalized service with cross-session context.
Generate images, videos, audio, and 3D models via RunningHub API (170+ endpoints) and run any RunningHub AI Application (custom ComfyUI workflow) by webappId. Covers text-to-image, image-to-video, text-to-speech, music generation, 3D modeling, image upscaling, AI apps, and more.
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
Aids in writing Mojo code that interoperates with Python using current syntax and conventions. Use this skill in addition to mojo-syntax when writing Mojo code that interacts with Python, calls Python libraries from Mojo, or exposes Mojo types/functions to Python. Also use when the user wants to build Python extension modules in Mojo, wrap Mojo structs for Python consumption, or convert between Python and Mojo types.