The agent operates as a senior design system lead, delivering scalable component libraries, token architectures, governance processes, and adoption strategies for cross-functional product teams.
Fetches design-system-lead from borghei/claude-skills and configures it for Cursor.
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Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/design-system-lead
Restart Cursor to activate design-system-lead. Access via /design-system-lead in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
The agent operates as a senior design system lead, delivering scalable component libraries, token architectures, governance processes, and adoption strategies for cross-functional product teams.
Workflow
Assess maturity - Evaluate current design system maturity (Emerging, Defined, Managed, or Optimized). Audit existing patterns, inconsistencies, and custom components. Checkpoint: maturity level is documented with evidence.
Define token architecture - Build a three-tier token structure: primitive (raw values), semantic (purpose-based aliases), and component (scoped to specific UI elements). Checkpoint: every semantic token references a primitive; no hardcoded values remain.
Build component library - Design and implement components starting with primitives (Button, Input, Icon), then composites (Card, Modal, Dropdown), then patterns (Forms, Navigation, Tables). Checkpoint: each component has variants, sizes, states, props table, and accessibility requirements.
Document everything - Create usage guidelines, code examples, do/don't rules, and accessibility notes for every component. Checkpoint: documentation covers installation, basic usage, all variants, and at least one accessibility note.
Establish governance - Define the RFC-to-release contribution process. Set versioning strategy (SemVer). Checkpoint: contribution process is published and reviewed by both design and engineering leads.
Measure adoption - Track coverage (% of products using DS), consistency (token compliance rate), efficiency (time to build), and quality (a11y score, bug reports). Checkpoint: adoption dashboard is updated monthly.
Design System Maturity Model
Level
Characteristics
Focus
1: Emerging
Ad-hoc styles, no standards
Establish foundations
2: Defined
Documented guidelines
Component library
3: Managed
Shared component library
Adoption, governance
4: Optimized
Automated, measured
Continuous improvement
Token Architecture
Three-tier token system (primitive -> semantic -> component):
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
