Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Define a product's motion design language -- duration and easing scales, choreography rules, and signature moves -- and export it as tokens plus ready-to-use Framer Motion variants and CSS. The difference between animations and a motion system: everything moves like it belongs to the same product.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Design motion the way type and color get designed: as a system with a scale, roles, and rules -- not per-component improvisation. Output is a motion spec plus drop-in implementation (Framer Motion variants, CSS custom properties and keyframes) so the system is enforceable, not aspirational.
instant ~100ms for state feedback, fast ~180-220ms for micro-interactions, base ~300ms for reveals, slow ~500ms+ for scene changes) and an easing vocabulary (standard, decelerate for entrances, accelerate for exits, plus at most ONE signature spring/overshoot -- the personality lives here). Calibrate values to the brand read from step 1, not generic defaults.prefers-reduced-motion behavior: fade or nothing).motion-tokens.json (durations, easings, distances), motion.css (custom properties + keyframes + reduced-motion guards), motion.ts (Framer Motion variants for each signature move, referencing the tokens), and MOTION.md -- the spec a new developer reads before animating anything.还没有评价。欢迎分享你的使用感受。
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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