Total 56,789 skills, AI & Machine Learning has 9442 skills
Showing 12 of 9442 skills
guide and workflow for creator-validation.
Framework for automated search over task-specific model harnesses — the code around a fixed base model that decides what to store, retrieve, and show while the model works.
Use when creating specialized subagents for Claude Code plugins or the Task tool - covers description writing for auto-delegation, tool selection, prompt structure, and testing agents
Smart Money Signal Copy Trade v1.0 — onchainos Agentic Wallet + Cost-Aware TP + Dev/Bundler Safety + Session Risk Control. Triggers when the user mentions smart money strategy, signal strategy, copy trading, whale tracking, KOL copy trading, on-chain signal trading, co-riding addresses, take profit / stop loss, risk preference, or wants to automatically buy/sell based on smart money signals. Runtime file: bot.py (includes Web Dashboard http://localhost:3248) Config file: config.py (hot-reload)
Creates and orchestrates multi-agent pipelines on the iii engine. Use when building AI agent collaboration, agent orchestration, research/review/synthesis chains, or any system where specialized agents hand off work through queues and shared state.
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
Turns a free-form project description into PROJECT_MANIFEST.md and SOFTWARE_FACTORY_MANIFEST.md for a 6-agent software factory pipeline. Agent-agnostic: works in Claude Code, Codex CLI, Gemini CLI.
Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.
Workflow for learning CuTe Python DSL by reading, importing, profiling, and extracting reusable patterns from CUTLASS Blackwell example kernels. Use when: (1) studying CUTLASS CuTe DSL reference implementations, (2) importing CUTLASS examples into the project runtime infrastructure, (3) building CuTe DSL knowledge base entries from profiling experiments, (4) understanding CuTe DSL API patterns, TMA pipelining, warpgroup scheduling, or persistent kernel structure.
Compress LLM responses to pure signal — Rocky's early notation style. Drop articles, filler, hedging. Best for pipelines and coding.
Generate images from text prompts and edit existing images using OpenRouter's image generation models. Use when the user asks to create, generate, or make an image, picture, or illustration from a description, or wants to edit, modify, transform, or alter an existing image with a text prompt.
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.