Extract color palettes from images, websites, or designs. Identifies dominant colors, generates complementary schemes, and exports in multiple formats (HEX, RGB, HSL, Tailwind, CSS variables). Use when users need color schemes from images, brand colors, or design system palettes.
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Extract and generate accessible color palettes from images, websites, and designs.
references/output-template.md — full report structure for presenting a palettereferences/export-formats.md — CSS, Tailwind, SCSS, JSON, iOS, Android snippetsreferences/color-theory.md — harmony schemes, accessibility, extraction best practicesIdentify the source: image file (PNG, JPG, SVG), website URL, screenshot, design mockup, or an existing color code to build from.
Extract colors.
Build the primary palette (5-10 colors): most dominant color, 2-3 supporting colors, 1-2 accents, a background, and a text color. Generate an extended palette with tints, shades, tones, and 50-950 numeric scales. See references/color-theory.md.
Run color harmony analysis to produce complementary, analogous, triadic, split-complementary, tetradic, and monochromatic schemes. See references/color-theory.md.
Check accessibility: compute WCAG 2.1 contrast ratios for each text/background pairing and test against protanopia, deuteranopia, and tritanopia. Recommend accessible alternatives where a pairing fails. See references/color-theory.md.
Format the report following references/output-template.md, and export to the requested formats using references/export-formats.md.
Deliver palettes that have clear dominant colors, sufficient variations, semantic naming, harmony schemes, contrast ratios that pass accessibility checks, usage guidelines, and exports in multiple formats. Produce professional, accessible palettes ready for immediate use in design systems.
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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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