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design-synthesis设计综合

Agent Skill

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

总安装

196

周安装

8

GitHub Stars

2

下载量

63
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:design-synthesis(设计综合)
来源仓库:https://github.com/bsamiee/parametric_forge
仓库路径:skills/design-synthesis
安装命令:
npx skills add https://github.com/bsamiee/parametric_forge --skill design-synthesis
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/bsamiee/parametric_forge --skill design-synthesis

简介

用于将外部研究成果转化为可执行的设计决策,基于轻量级代码库扫描生成方案。

  • 适合有明确研究输入的设计深化阶段,协调多代理并行分析并输出权衡报告。
  • 使用时需提交研究工件,经探索后选择最优路径并生成结构化设计文档。
  • 依赖 parallel-dispatch 工具进行代码扫描,需确保环境已配置相关依赖。
  • design-synthesis 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

[H1][DESIGN-SYNTHESIS]

Dictum: *Design decisions require grounded context before implementation.*

Synthesize research findings into design decisions via light codebase investigation.

Workflow:

  1. §INGEST — Load research artifact, parse original request
  2. §SCAN — Light codebase investigation via parallel-dispatch (3-4 agents)
  3. §EXPLORE — Generate 2-3 approaches with trade-offs
  4. §SELECT — Commit to best approach with rationale
  5. §OUTPUT — Structured design document

Dependencies:

  • parallel-dispatch — Agent orchestration for codebase scan
  • Research artifact — External findings from deep-research

Input:

  • Research: Path to research artifact (research_{slug}.md)
  • Request: Original user request/intent

[1][INGEST]

Dictum: *Grounded context prevents speculative design.*

Load and parse inputs:

[INDEX]SourceExtract
[1]Research fileFindings, confidence levels, key sources
[2]RequestIntent, scope boundaries, success criteria

Parse research structure:

  • ## [1][FINDINGS] → Domain knowledge by category
  • ## [2][CONFIDENCE] → High/Medium/Low ratings
  • ## [3][SOURCES] → Attribution for decisions

[IMPORTANT]:

  • [ALWAYS] Extract high-confidence findings as primary input.
  • [ALWAYS] Note low-confidence areas as design risks.
  • [NEVER] Proceed without understanding request intent.

[2][SCAN]

Dictum: *Pattern awareness prevents reinvention.*

Dispatch 3-4 agents via parallel-dispatch for codebase context.

Agent Assignment:

[INDEX][AGENT][SCOPE][RETURNS]
[1]PatternsSimilar implementations in codebaseConventions, reusable patterns, prior art
[2]ConstraintsProject rules, architecture limitsHard boundaries, style requirements
[3]InterfacesEntry/exit points for feature areaTouch points, consumers, integration surface

Agent Prompt Template:

Scope: [Specific investigation area]
Objective: Surface [patterns|constraints|interfaces] relevant to: [request summary]
Output: Bullet list of findings with file paths
Context: Research indicates: [key findings summary]
Exclusions: Do NOT analyze implementation details or specific file contents

[CRITICAL]:

  • [ALWAYS] Dispatch ALL agents in ONE message block.
  • [ALWAYS] Scope to patterns/constraints/interfaces—NOT implementation.
  • [NEVER] Deep-dive into file contents—that's plan's job.

[3][EXPLORE]

Dictum: *Comparison reveals optimal trade-offs.*

Generate 2-3 distinct approaches from research + scan findings.

Per Approach:

[INDEX]AspectContent
[1]StrategyHigh-level implementation direction
[2]AlignmentHow it leverages research findings
[3]PatternsWhich codebase conventions it follows
[4]Trade-offsPros and cons

Approach Generation Criteria:

  • Approach A: Most aligned with existing patterns (conservative)
  • Approach B: Best leverage of research findings (optimal)
  • Approach C: Simplest implementation path (minimal) — optional

[IMPORTANT]:

  • [ALWAYS] Ground approaches in scan findings—no speculation.
  • [ALWAYS] Include trade-off analysis per approach.
  • [ALWAYS] Apply YAGNI—cut unnecessary scope from all approaches.
  • [NEVER] Generate approaches without codebase evidence.

[4][SELECT]

Dictum: *Committed direction enables focused planning.*

Select best approach via weighted criteria:

[INDEX]CriterionWeightEvaluation
[1]Pattern alignmentHighMatches existing codebase conventions
[2]Research supportHighBacked by high-confidence findings
[3]SimplicityMediumMinimal moving parts
[4]Risk profileMediumLow-confidence areas minimized

Selection Output:

  • Selected approach name
  • Primary rationale (1-2 sentences)
  • Key trade-off accepted

[CRITICAL]:

  • [ALWAYS] Commit to ONE approach—no hedging.
  • [ALWAYS] Document trade-off accepted.
  • [NEVER] Defer selection to downstream phases.

[5][OUTPUT]

Dictum: *Downstream consumers require predictable structure.*

Produce brainstorm.md with structure:

# [H1][DESIGN]: [Title]
>**Dictum:** *[Build target—refined from request]*

<br>

**Research Summary:** [Key findings relevant to design]

---
## [1][APPROACHES]

### [1.1][APPROACH_A]: [Name]

| [INDEX] | [ASPECT]  | [DETAIL]                        |
| :-----: | --------- | ------------------------------- |
|   [1]   | Strategy  | [High-level direction]          |
|   [2]   | Alignment | [Research findings leveraged]   |
|   [3]   | Patterns  | [Codebase conventions followed] |
|   [4]   | Pros      | [Benefits]                      |
|   [5]   | Cons      | [Drawbacks]                     |

---
### [1.2][APPROACH_B]: [Name]

| [INDEX] | [ASPECT]  | [DETAIL]                        |
| :-----: | --------- | ------------------------------- |
|   [1]   | Strategy  | [High-level direction]          |
|   [2]   | Alignment | [Research findings leveraged]   |
|   [3]   | Patterns  | [Codebase conventions followed] |
|   [4]   | Pros      | [Benefits]                      |
|   [5]   | Cons      | [Drawbacks]                     |

---
## [2][SELECTED_APPROACH]

| [INDEX] | [KEY]              | [VALUE]                |
| :-----: | ------------------ | ---------------------- |
|   [1]   | Choice             | [Approach name]        |
|   [2]   | Rationale          | [Why this approach]    |
|   [3]   | Trade-off Accepted | [What we're giving up] |

---
## [3][DESIGN_CONSTRAINTS]

| [INDEX] | [CONSTRAINT]    | [SOURCE]        |
| :-----: | --------------- | --------------- |
|   [1]   | [Hard boundary] | [Codebase scan] |
|   [2]   | ...             | ...             |

---
## [4][KEY_DECISIONS]

| [INDEX] | [DECISION]      | [CHOICE]          | [RATIONALE] |
| :-----: | --------------- | ----------------- | ----------- |
|   [1]   | [Design choice] | [Selected option] | [Why]       |
|   [2]   | [Design choice] | [Selected option] | [Why]       |

[CRITICAL]:

  • [ALWAYS] Include all sections—downstream depends on structure.
  • [ALWAYS] Table format for approaches and decisions.
  • [NEVER] Prose paragraphs—tables and lists only.

[6][VALIDATION]

Dictum: *Incomplete synthesis cascades errors downstream.*

[VERIFY]:

  • Ingest: Research parsed, request intent extracted
  • Scan: 3-4 agents dispatched in ONE message
  • Explore: 2-3 approaches with trade-offs generated
  • Select: ONE approach committed with rationale
  • Output: All sections present, table format used
  • YAGNI: Unnecessary scope cut from all approaches

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.69%
按下载量换算23

Claude

28.43%
按下载量换算18

Cursor

17.52%
按下载量换算11

Gemini CLI

9.22%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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