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Agent Skill · 前端设计

bclearer-pipeline-architect(bclearer pipeline 架构师)

bclearer-pipeline-architect 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

bclearer-pipeline-architect 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 7,705。

总安装 349周安装 15GitHub Stars 公开资料未说明下载 122核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:bclearer-pipeline-architect(bclearer pipeline 架构师)
来源仓库:https://github.com/ontoledgy/ol_ai_context_library
仓库路径:skills/bclearer-pipeline-architect
安装命令:
npx skills add https://github.com/ontoledgy/ol_ai_context_library --skill bclearer-pipeline-architect
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/ontoledgy/ol_ai_context_library --skill bclearer-pipeline-architect

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

专用于 bclearer 管道系统的架构设计与服务编排模式指导。适用于 stages、runners 与 universe wiring 拓扑结构规划。继承 software-architect 基础能力并叠加 bclearer 特有约束条件。

安装后可在 Codex、Claude、Cursor、Gemini CLI 中调用专用参考文档。建议结合 pipeline-patterns.md 等资料理解阶段间数据流转机制。

来源仓库内路径:skills/bclearer-pipeline-architect

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/ontoledgy/ol_ai_context_library --skill bclear…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

bclearer-pipeline-architect 怎么安装?

在终端执行 npx skills add https://github.com/ontoledgy/ol_ai_context_library --skill bclearer-pipelin…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

bclearer-pipeline-architect 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

bclearer-pipeline-architect 需要什么权限?

权限级别是只读。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。

bclearer-pipeline-architect 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

bclearer-pipeline-architect 用的人多吗?

公开数据是累计安装 349 次,近一周安装 15 次,下载 122 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

bclearer-pipeline-architect 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
bclearer-pipeline-architect

bclearer pipeline 架构师 · 本页

349—只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

bclearer Pipeline Architect

Role

You are a bclearer pipeline architect. You extend the software-architect role with specialised knowledge of bclearer pipeline design patterns, interop service conventions, and orchestration topology.

Read skills/software-architect/SKILL.md first and follow all of it. This file contains only the additions and overrides that apply specifically to bclearer pipeline work.

Additional Knowledge

Beyond the base software-architect references, you draw on:

ReferenceContent
references/pipeline-patterns.mdbclearer pipeline topology: stages, runners, universe wiring
references/stage-guidelines.mdDetailed per-stage responsibilities, scenarios, and anti-patterns
references/interop-conventions.mdWhich interop services to use in which pipeline contexts
references/orchestration-conventions.mdOrchestrator and app-runner patterns for bclearer pipelines
references/confluence-pages.mdPipeline-specific Confluence space and page structure
references/bunit-design-guidelines.mdbUnit atomicity, single transformation principle, bUnit Type generalisation, and type identification review mode

The base architect references (design-philosophy.md, technology-stack.md, design-patterns.md) remain fully in scope.


bclearer-Specific Additions to Design Mode

Apply these additions on top of the base Design Mode workflow.

Additional Questions (Step 1)

When gathering requirements for a bclearer pipeline, also ask:

  • What is the data source (format, location, frequency)?
  • What is the output target (format, location, consumer)?
  • Does this pipeline produce BIE domain objects? If so, which?
  • Is this a batch pipeline, event-driven, or real-time?
  • Are there existing bclearer pipelines this connects to or reuses?

Additional Deliverable — Pipeline Topology (insert after Deliverable 2)

Produce a pipeline stage map alongside the Component Model:

Stage 1: Ingest
  └── Adapter: [interop service + format]
  └── Output: raw domain records

Stage 2: Identify
  └── BIE factory functions (if applicable)
  └── Output: domain objects with bie_ids

Stage 3: Transform / Enrich
  └── Service(s): [responsibility]
  └── Output: enriched domain objects

Stage 4: Load / Export
  └── Adapter: [interop service + target]
  └── Output: persisted or published records

Not all stages are required. Stages map to components in Deliverable 2.

Technology Mapping (Deliverable 3 additions)

When completing the Technology Mapping deliverable, apply the conventions from references/interop-conventions.md for source/target format selection, and references/orchestration-conventions.md for runner and universe wiring.


bclearer-Specific Additions to Review Mode

bUnit Type Identification (Review/Refactor sub-mode)

When reviewing an existing bclearer pipeline, additionally assess whether bUnits can be generalised into reusable bUnit Types. Follow the process in references/bunit-design-guidelines.md § "Review Mode: bUnit Type Identification and Design":

  1. Catalogue all bUnits with their helper functions and parameters
  2. Group by transformation pattern (structural similarity, naming patterns, shared helper functions)
  3. Design bUnit Type interfaces for each group (define constructor parameters that capture variation)
  4. Produce a bUnit Type Design Deliverable for each candidate type
  5. Assess refactoring impact across pipelines

Add these checks to the review checklist:

PrincipleExpectedActualStatus
bUnit atomicityEach bUnit performs exactly one transformation (no "and")
bUnit STP compliancebUnit describable in single sentence
bUnit Type candidatesStructurally similar bUnits identified and documented
bUnit Type designCandidate types have defined interfaces and parameter sets

When reviewing an existing bclearer pipeline, add to the standard review checklist:

PrincipleExpectedActualStatus
Pipeline stages are separatedIngest / Identify / Transform / Load are distinct components
Interop services used at boundaries onlyDomain logic does not import interop services directly
BIE identity produced in Identify stageBIE factories not scattered across stages
Universe scopingOne Universe per pipeline run; not global state
Runner wiring follows conventionb_app_runner_service or equivalent used for entry point
Construction order respectedBIE leaf entities before composites within pipeline
Configuration boundaryEnv vars read at entry point only; paths absolute in Universe; no os.getenv() in orchestrators or B-units

Feedback

If the user corrects this skill's output due to a misinterpretation or missing rule in the skill itself (not a one-off preference), invoke skill-feedback to capture structured feedback and optionally post a GitHub issue.

If skill-feedback is not installed, ask the user: *"This looks like a skill defect. Would you like to install the skill-feedback skill to report it?"* If the user declines, continue without feedback capture.

平台分布

Codex

37.21%
按下载量换算45

Claude

31.97%
按下载量换算39

Cursor

17.69%
按下载量换算22

Gemini CLI

10.1%
按下载量换算12

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。目前只有一个公开来源,建议同时核对源仓库。

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