Audit and sync design tokens between the source of truth (tokens.json, Figma variables export) and the code that should consume them (Tailwind config, CSS custom properties, theme files). Finds drift -- hardcoded values, orphan tokens, out-of-date copies -- and fixes it in the right direction.
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Keep one source of truth actually true. Every design system decays the same way: a hex gets hardcoded in a hurry, a token gets renamed in Figma but not in Tailwind, a second half-copy of the palette appears in a one-off CSS file. This skill finds the drift, decides direction with the user, and reconciles.
tokens.json/*.tokens.json (W3C format), Figma variables export, tailwind.config.* theme, CSS custom properties, TS theme objects, SCSS variable files. If more than one claims to be canonical, ask which wins before touching anything -- direction of sync is a human decision.token-drift-report.md in four buckets:
STALE COPIES -- downstream definitions that no longer match canonical (with both values)HARDCODED -- raw values in components where a matching token exists (exact match, then near-match within perceptual distance for colors)ORPHANS -- tokens defined but consumed nowhereUNTOKENIZED -- raw values appearing 3+ times with no token; these are tokens waiting to be namednpm run tokens:check) that re-runs the drift detection and fails CI on new stale copies or hardcoded near-matches, so the system stays synced after you leave.#0B1121 vs #0b1220 may be drift or may be a decision.color.blue.500 -> color.accent -> button.bg, fix references at the right layer instead of flattening everything to primitives.theme.css is built from tokens.json, fix the source and rebuild; never hand-edit output.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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