Total 30,708 skills, AI & Machine Learning has 4959 skills
Showing 12 of 4959 skills
DeepSeek AI models for coding. Use for code AI.
LlamaIndex data framework for LLMs. Use for RAG applications.
Scikit-learn machine learning library. Use for classical ML.
Keras high-level neural network API. Use for deep learning.
AI generation provenance and audit trail tracking. Records decision factors, data lineage, reasoning chains, confidence scoring, and cost tracking for AI-generated content.
Brief description of what this skill does and when to use it. Be specific about capabilities and use cases to help agents decide when to load this skill.
This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.
Orchestrate multi-agent workflows from a Kiro spec using codex (code) + Gemini (UI), including dispatch/review/state sync via AGENT_STATE.json + PROJECT_PULSE.md; triggers on user says "Start orchestration from spec at <path>", "Run orchestration for <feature>", or mentions multi-agent execution.
Nano Banana Pro (nano-banana-pro) image generation skill. Use this skill when the user asks to "generate an image", "generate images", "create an image", "make an image", uses "nano banana", or requests multiple images like "generate 5 images". Generates images using Google's Gemini 2.5 Flash for any purpose - frontend designs, web projects, illustrations, graphics, hero images, icons, backgrounds, or standalone artwork. Invoke this skill for ANY image generation request.
Build conversational AI agents using Pydantic AI + OpenRouter. Use when creating type-safe Python agents with tool calling, validation, and streaming.
Analyzes and improves LLM prompts and agent instructions for token efficiency, determinism, and clarity. Use when (1) writing a new system prompt, skill, or CLAUDE.md file, (2) reviewing or improving an existing prompt for clarity and efficiency, (3) diagnosing why a prompt produces inconsistent or unexpected results, (4) converting natural language instructions into imperative LLM directives, or (5) evaluating prompt anti-patterns and suggesting fixes. Applies to all LLM platforms (Claude, GPT, Gemini, Llama).
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.