domain-name-brainstormer
为您的项目生成创意域名想法,并检查多个顶级域名(.com、.io、.dev、.ai 等)的可用性。节省数小时的头脑风暴和手动检查时间。
Найдите подходящую возможность для вашего агента.
为您的项目生成创意域名想法,并检查多个顶级域名(.com、.io、.dev、.ai 等)的可用性。节省数小时的头脑风暴和手动检查时间。
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Analyzes article structure, identifies positions requiring visual aids, generates illustrations with Type × Style two-dimension approach. Use when user asks to "illustrate article", "add images", "generate images for article", or "为文章配图".
Manage project-centric research workflows, claims, evidence, notes, and experiments. Use when the task should be persisted into ResearchClaw's structured research graph instead of remaining a transient chat reply.
Query sibling dot-ai projects to verify features are USABLE (not just defined). IMPORTANT: When calling this skill, explain HOW you plan to use the feature (e.g., 'I need to call X via REST API from the UI' or 'I need to import Y function'). This helps verify the full chain from definition to exposure.
Generate a feature request prompt for another dot-ai project. Use when you need a feature implemented in a sibling project (MCP server, controller, etc.) to unblock work in the current project.
Create a release tag based on accumulated changelog fragments. Run when ready to cut a release.
Write LaTeX ML/AI review articles for arXiv using the IEEEtran template and verified BibTeX citations.
Create changelog fragment for release notes. Invoke during /prd-done workflow during the first push to the PR.
Process a feature request or response from another dot-ai project. Reads from tmp directory, implements/integrates, and writes response if needed.
Use when user asks to explain, break down, or help understand technical concepts (AI, ML, or other technical topics). Makes complex ideas accessible through plain English and narrative structure. Use the provided scripts to transcribe videos
Create detailed implementation plans through an interactive process with research, code snippets, and structured deliverables. Use this skill when planning significant features, refactoring tasks, or complex implementations that require thorough analysis and structured documentation. The skill guides through context gathering, research, design decisions, and generates comprehensive plans with test strategies and success criteria.
Load comprehensive GitHub issue information including title, description, comments, labels, assignees, milestones, and related items (linked PRs and cross-references). This skill should be used when planning to fix an issue, when detailed issue context is needed for implementation work, or when a plan command needs to understand the full scope of an issue.
Use ManagedCode.Storage when a .NET application needs a provider-agnostic storage abstraction with explicit configuration, container selection, upload and download flows, and backend-specific integration kept behind one library contract.
Maintain or migrate EF6-based applications with realistic guidance on what to keep, what to modernize, and when EF Core is or is not the right next step. Use when working in an EF6 codebase or planning a data layer migration.
Use ManagedCode.Orleans.SignalR when a distributed .NET application needs Orleans-based coordination of SignalR real-time messaging, hub delivery, and grain-driven push flows.
Apply MCAF human-review-planning guidance for a large AI-generated code drop by reading the target area, tracing the natural user and system flows, identifying the riskiest boundaries, and prioritizing the files a human should inspect first. Use when the codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list.
Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations.