Total 57,191 skills, AI & Machine Learning has 9513 skills
Showing 12 of 9513 skills
Expertise in using open-multi-agent, a TypeScript framework for building production-grade multi-agent AI teams with task scheduling, dependency graphs, and inter-agent communication.
Generate and edit high-quality images with AI. Emphasize strong prompt design, structured JSON prompting, reference-image workflows, text rendering, and iterative refinement. Use any time the user needs an image generated.
Use when starting work on a new or unfamiliar project, when encountering unexpected patterns, when user corrects your assumptions, or when explicitly invoked via /learn - auto-discovers and remembers project context through structured codebase analysis
Use when generating images with Alibaba Cloud Model Studio Z-Image Turbo (z-image-turbo) via DashScope multimodal-generation API. Use when creating text-to-image outputs, controlling size/seed/prompt_extend, or documenting request/response mapping for Z-Image.
Use this skill when the user wants to transform an existing image into a new generated result, such as replacing models, changing poses, swapping backgrounds, generating scenes, expanding image edges, removing backgrounds, or creating virtual try-on images. Use it for image-editing and image-generation tasks where a source image and text instructions need to be turned into one or more final images.
Use when you need multi-agent orchestration for OpenAI Codex CLI. Triggers on: omx, $plan, $ralph, $team, $autopilot, $deep-interview. v0.11.10 — 30+ agents, 35+ workflow skills, tmux team runtime, sparkshell, explore, ralplan.
Design domain-specific agent teams, define specialized agents, and generate the skills they use. Use when you need to decompose a complex project into coordinated multi-agent teams, choose the right architecture pattern (pipeline, fan-out/fan-in, expert pool, producer-reviewer, supervisor, hierarchical delegation), generate .claude/agents/ and .claude/skills/ files, or validate and iterate on generated harnesses. Triggers on: harness, build a harness, design agent team, agent team architecture, multi-agent skill generation, set up harness, harness engineering, domain agent team, harness for this project.
Expert in extracting text from images using Tesseract, EasyOCR, PaddleOCR, Google Vision, AWS Textract, Claude Vision. Trigger: When extracting text from images, screenshots, scanned documents, or PDFs.
Chokidar-based file watcher that triggers `claude -p` on changes. Useful for automated AI reactions to file changes — design sync, code validation, config regeneration, etc.
Comprehensive prompt and context engineering for any AI system. Four modes: (1) Craft new prompts from scratch, (2) Analyze existing prompts with diagnostic scoring and optional improvement, (3) Convert prompts between model families (Claude/GPT/Gemini/Llama), (4) Evaluate prompts with test suites and rubrics. Adapts all recommendations to model class (instruction-following vs reasoning). Validates findings against current documentation. Use for system prompts, agent prompts, RAG pipelines, tool definitions, or any LLM context design. NOT for running prompts, generating content, or building agents.
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.
Record and manage meeting notes with intelligence. Organizes meeting documentation with action items, decisions, and follow-ups.