Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Build a complete typographic system -- modular or fluid type scale, line-height and tracking per size, weight roles, and vertical rhythm -- exported as tokens, Tailwind config, and CSS. Pairs with font-pairing-suggester (which picks the fonts; this builds the system they live in).
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Typography is the design system's skeleton: most products that look "off" have inconsistent type long before they have bad colors. Build the scale as a system with roles and math, then make the codebase actually use it. Input: the chosen fonts (or run font-pairing-suggester first), the product type (marketing site, dense app UI, long-form editorial), and the target range of devices.
clamp() per step so headings scale between viewport bounds without breakpoint jumps; fixed steps are fine for app UI.display, h1-h4, body-lg, body, body-sm, caption, overline, code. Each role = size step + line-height + tracking + weight + family, as one token. Components consume roles; nobody composes raw steps.text-wrap: balance on headings, and measure limits (65-75ch for body prose) so line length stays readable at every viewport.typography-tokens.json, Tailwind fontSize config (size + line-height + tracking tuples per role; v4 @theme), typography.css with role classes, and TYPOGRAPHY.md. Then map the step-1 inventory onto roles and replace -- with a per-case flag on anything that does not cleanly map (it is either a missing role or drift to delete).text-h2, never text-[28px] -- and no role means the component asks the system for one, not the developer for a number.clamp() minimum must stay readable on a 320px phone and the max must not blow the layout on ultrawide.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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