# How to Use the Brief Inference Section to Guide AI Design Decisions > Guide AI design decisions by using the Brief Inference section to analyze design briefs and generate a concise read. Prevent default styles and hallucinations in your AI. - Repository: [Leon Lin/taste-skill](https://github.com/Leonxlnx/taste-skill) - Tags: how-to-guide - Published: 2026-06-05 --- **The Brief Inference section forces AI agents to explicitly analyze a design brief and emit a one-sentence "design read" that anchors all downstream UI generation decisions, preventing default-fallback styles and hallucinations.** The Leonxlnx/taste-skill repository introduces a structured approach to AI-driven frontend design through its v2-experimental framework. By utilizing the **Brief Inference** section marked as **§0** in [`skills/taste-skill/SKILL.md`](https://github.com/Leonxlnx/taste-skill/blob/main/skills/taste-skill/SKILL.md), developers ensure that AI agents read the room before writing any code. This step extracts critical signals from design briefs to guide component selection, motion intensity, and visual hierarchy according to the specific constraints provided. ## Understanding the §0 Brief Inference Step The **Brief Inference** section serves as the mandatory first step in the `taste-skill` pipeline. Before generating any HTML, CSS, or component code, the agent must process the brief and publish a concise one-sentence **design hypothesis** known as the *design read*. According to the source code definition in [`CHANGELOG.md`](https://github.com/Leonxlnx/taste-skill/blob/main/CHANGELOG.md) at line 26: *"§0 Brief Inference – before any code, the agent reads the room (page kind, vibe words, references, audience, constraints) and declares a one-line design read. Anti-default discipline."* This explicit requirement prevents the model from skipping the contextual analysis phase, which dramatically reduces hallucinations and eliminates default-fallback styles that ignore the project's specific aesthetic requirements. ## How Brief Inference Analyzes Design Briefs When processing the brief, the agent systematically extracts four categories of signals: ### Identifying Page Types The agent first categorizes the interface architecture—whether the deliverable is a **landing page**, **product detail page**, **blog post**, **dashboard**, or another format. This classification determines the base component hierarchy and layout patterns available for selection. ### Capturing Vibe Keywords Specific tonal descriptors such as *sleek*, *playful*, *premium*, *minimal*, or *data-driven* are extracted to shape the visual language. These **vibe keywords** directly influence which design system map the agent selects and how it configures the motion intensity dial. ### Pulling Reference Assets The agent collects URLs to **example sites**, **style-guides**, or **brand kits** provided in the brief. These references serve as concrete visual anchors that constrain the aesthetic direction beyond abstract descriptions. ### Noting Audience and Constraints Critical limitations including **target audience** demographics, **brand-color** requirements (e.g., primary colours like `#1A73E8`), **accessibility** standards (e.g., WCAG AA contrast), and **motion intensity** caps (e.g., ≤ 3) are recorded to enforce hard boundaries during generation. ## Practical Implementation Examples ### Minimal Prompt Approach The simplest way to trigger Brief Inference is passing a structured text block directly in the prompt: ```text ## Brief Inference Design brief: a fintech landing page that feels trustworthy and futuristic. Vibe: sleek, data-driven, high-contrast. References: https://example.com/fintech-hero, https://dribbble.com/shots/1234567. Audience: startup founders, investors. Constraints: primary brand colour #1A73E8, WCAG AA contrast, motion intensity ≤ 3. ``` The agent responds with a constrained **design read** such as: `"Sleek fintech landing focused on high-contrast trust, using primary blue #1A73E8 and modest motion."` This single line then drives every subsequent design decision. ### Full Skill Template Integration For production workflows using the `taste-skill` SKILL.md template, embed the brief using the YAML structure: ```yaml name: design-taste-frontend description: | # §0 Brief Inference {{BRIEF}} # §1 Design System Map … ``` Replace `{{BRIEF}}` with the content from the minimal prompt example. When parsed, the agent stores the one-line read and subsequent sections—such as **§1 Design System Map** and **Dark-Mode Protocol**—automatically reference this stored value to select appropriate palettes and motion settings. ### Command Line Interface Usage For CLI-driven workflows, inject the brief using the `npx skills` command: ```bash npx skills add https://github.com/Leonxlnx/taste-skill \ --skill "design-taste-frontend" \ --prompt "$(cat <