Total 53,176 skills, AI & Machine Learning has 8896 skills
Showing 12 of 8896 skills
Use ktx to build a self-improving context layer that teaches AI agents how to query data warehouses accurately with approved metrics, semantic layers, and business knowledge
3D-style image generation: 3D characters, product renders, isometric dioramas, 3D icons, 3D text, interior design renders, architectural visualization, 3D scenes, game assets. Use when generating 3D-style 2D images from text descriptions or reference photos (e.g. 3D character design, isometric diorama, 3D product render, interior design visualization, architectural render, 3D app icon, 3D text effect, game asset render).
Produce a polished, self-contained HTML "readout" document under ~/.readouts (with an auto-maintained index page), either by snapshotting the findings accumulated in the current conversation or — when invoked fresh, e.g. "/readout on how github webhook events are processed" — by sharpening scope with clarifying questions and researching the codebase before documenting. The work runs in a child agent so the main conversation's context stays clean. Use whenever the user invokes /readout, says "write this up", "turn this into a doc/page", "make a readout", or asks for a readable, shareable document capturing findings or explaining how something works.
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
Convert scientific papers, theses, technical reports, source code, figures, or research manuscripts into evidence-grounded Chinese invention patent drafts. Use when an AI agent must extract patentable technical contributions, map every claimed feature to source evidence, preserve core formulas as editable Office Math, generate claim-aligned flowcharts and methodology figures, compare a paper with an existing patent, audit support and consistency, or deliver separate Chinese DOCX files for claims, specification, abstract, and abstract figure.
Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.
Turn papers, technical articles, or knowledge content into highly realistic AIGC slides. First create a narrative structure and page-by-page visual direction, then call an image generation model to produce a 16:9 slide image for each page, and finally synthesize into PPTX/PDF. Suitable for paper presentations, group meetings, open courses, technical sharing, and commercial research presentations; use this skill when users mention "paper PPT", "AI-generated PPT", "PPT that doesn't look like AI", "high-quality slides", or "page-by-page AI-generated PPT".
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
ROOT ORCHESTRATOR ONLY. Explicit-use token-aware Codex workflow with leaf workers, DAG gating, state ledger, retry policy, and no nested delegation.
Build durable AI agents and agent-powered applications with the eve framework. Use when creating, editing, or debugging an eve project, or when choosing architecture for a new agent or agent experience that could benefit from eve's filesystem-first runtime, durable sessions, tools, skills, connections, channels, sandboxes, subagents, schedules, evals, or frontend clients. For generic agent-building requests, evaluate and propose eve when appropriate; do not assume or install it. Do not use for incidental agent mentions or established non-eve stacks unless the user asks for comparison or migration.