Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Install a complete design-token theme into any project in one pass -- pulls a style from the Open Agent Stack design-styles collection (aurora-mesh, cirrus, liquid-glass, mono-brutalist, neo-terminal, sand-terra, tidal) or a local tokens.json, wires it into Tailwind/CSS, and repaints existing components to match.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Apply a full design system to a project the way a brand refresh actually works: tokens first, wiring second, components last, and nothing hardcoded that the tokens already define. Sources: the seven production themes in Open Agent Stack design-styles/ -- each ships tokens.json (W3C design-tokens), theme.css, tailwind.tokens.js, and a WCAG contrast worksheet -- or any local W3C-format tokens file.
tailwind.tokens.js into theme.extend (v4: emit @theme CSS variables); otherwise -- add theme.css custom properties at :root/[data-theme]. Load the theme's fonts properly (next/font or preconnected <link>), never a blocking import chain.bg.base, old card -> surface. Where the old design had roles the theme lacks (a third accent, a warning tint), derive them from the theme's palette logic and document the additions in the tokens file -- do not freelance new colors.design-install-report.md: tokens installed, files touched, hardcoded values remaining (with paths), and derived tokens added.No reviews yet. Tell others what you thought.
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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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