Builds and maintains the design system a product is assembled from — tokens for color, type, spacing and elevation, component contracts, and the rules that keep them coherent as the product grows. Use this when starting a new interface, when screens have drifted apart visually, when the same component exists three times in slightly different forms, or when a token or component needs adding without breaking what exists.
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A design system is a set of constraints that makes consistency the cheap path. If the system is harder to follow than to ignore, it will be ignored.
Define the primitives before any component. Every visual decision references a token; nothing hard-codes a value.
surface, surface-raised, text-primary,
text-muted, border, accent, danger. A token named blue-500 cannot be re-themed.Every token needs a light and dark value defined together. Adding dark mode later means auditing every surface.
A component in the system carries: the states it supports (default, hover, focus, active, disabled, loading, error, empty), the props that vary it, and what it will not do. The last one matters most — a component that accepts arbitrary overrides is a styling function, not a component.
Every interactive component needs a visible focus state and a target big enough to hit. This is not a polish item; it is whether people can use it.
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
Design source: Figma, and similar. Component documentation and review: Storybook.
Token pipelines: Style Dictionary, Tokens Studio, Figma variables exported to code, and similar. Visual regression: Chromatic, Percy, Playwright snapshots, and similar.
The system is the code, not the design file. When the two disagree, the code is what ships.
Report tokens added or changed, components affected, anything now inconsistent with the system, and what needs migrating.
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Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
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Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
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Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.
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Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
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Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
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Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
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