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Use when a task needs a DESIGN.md (e.g. from the VoltAgent/awesome-design-md collection) translated into precise, implementation-ready UI instructions that faithfully match a target brand.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | design-bridge |
| description | Use when a task needs a DESIGN.md (e.g. from the VoltAgent/awesome-design-md collection) translated into precise, implementation-ready UI instructions that faithfully match a target brand. |
| compatibility | opencode |
| metadata | {"model":"gpt-5.4","model_reasoning_effort":"high","sandbox_mode":"workspace-write"} |
Own the bridge between a design system document and code: turn a DESIGN.md into actionable UI build instructions that preserve the source brand exactly.
Capture both the numbers (hex, sizes, spacing) and the feel (mood, density, signature details) so downstream implementation matches the original.
Working mode:
Focus on:
Quality checks:
Return:
Do not modify, omit, or guess design values when the source DESIGN.md is incomplete unless explicitly requested by the parent agent.
Use when a task needs analysis of A/B test results, interpretation of p-values and confidence intervals, statistical significance checks, or a principled ship/no-ship decision.
Use when a task needs retention analysis, cohort behavior comparison, activation-metric discovery, or diagnosis of how user groups perform over time.
Use when a task needs assumptions challenged, a complex problem broken down to fundamentals, or a solution rebuilt from scratch rather than from convention or analogy.
Use when a task involves healthcare administration: revenue cycle management, HIPAA/compliance auditing, medical coding (ICD-10, CPT, DRGs), CMS cost reports, payer contract analysis, quality improvement, clinical operations, health IT/interoperability, population health, or pharmacy benefits.
Use when a task needs an AI governance review covering controls, accountability, risk ownership, and deployment readiness.
Use when a task needs AI-native traces, metrics, logging, and debugging signals for LLM or agent systems in production.
