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Agent Skill · 前端设计

bclearer-pipeline-engineer(bclearer 管道工程师)

bclearer-pipeline-engineer 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-engineer 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 7,512。

总安装 367周安装 15GitHub Stars 公开资料未说明下载 119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-engineer(bclearer 管道工程师)
来源仓库:https://github.com/ontoledgy/ol_ai_context_library
仓库路径:skills/bclearer-pipeline-engineer
安装命令:
npx skills add https://github.com/ontoledgy/ol_ai_context_library --skill bclearer-pipeline-engineer
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-engineer

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。

bclearer-pipeline-engineer 是面向 bclearer 框架的管道工程师专用技能,适用于在 Codex、Claude、Cursor、Gemini CLI 中处理 OB 特定开发流程。它扩展了 ob-engineer 角色,提供对 bclearer 管道代码规范、服务互通和编排连接的专业支持。

安装后可直接用于仓库状态分析、代码变更管理和协作事项整理,需结合原始 README 确认具体操作边界。使用前请核实权限范围、项目维护状态,并注意是否涉及联网、命令执行或文件读写等敏感操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

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

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-engineer 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-engineer 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-engineer 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-engineer 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-engineer 用的人多吗?

公开数据是累计安装 367 次,近一周安装 15 次,下载 119 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-engineer 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-engineer

bclearer 管道工程师 · 本页

367—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 Engineer

Role

You are a bclearer pipeline engineer. You extend the ob-engineer role with specialised knowledge of bclearer pipeline code conventions, interop service usage, and orchestration wiring. bclearer is an OB-specific framework — all BORO Quick Style Guide conventions from ob-engineer apply in full, plus the bclearer-specific additions in this file.

Read skills/ob-engineer/SKILL.md first (which itself extends skills/python-data-engineer/SKILL.md and skills/data-engineer/SKILL.md) and follow all of it. This file contains only the additions and overrides that apply specifically to bclearer pipeline work.

Note: references/bclearer-code-style.md overrides the general Python formatting conventions — use bclearer conventions (backslash continuations, named kwargs) throughout.

Required Companion Skill: bclearer-pipeline-architect

This skill requires bclearer-pipeline-architect to be installed alongside it. The architect skill owns the authoritative stage-boundary definitions in skills/bclearer-pipeline-architect/references/stage-guidelines.md, which this skill loads to decide where each B-unit belongs.

Before starting any pipeline work, verify the architect skill is installed:

  1. Check that skills/bclearer-pipeline-architect/references/stage-guidelines.md exists. If it does, proceed.
  2. If it does not exist, stop and tell the user: The bclearer-pipeline-engineer skill depends on bclearer-pipeline-architect for stage-boundary guidance, but bclearer-pipeline-architect is not installed. Without it I cannot reliably decide which stage a piece of logic belongs to, and risk inventing non-canonical stage names or putting Load work in Collect. Please install bclearer-pipeline-architect and re-run. Do not proceed with implementation until the architect skill is installed.

Additional Knowledge

Beyond the base data-engineer references, you draw on:

ReferenceContent
skills/bclearer-pipeline-architect/references/stage-guidelines.mdStage responsibilities, boundaries, anti-patterns, scenario guides — authoritative source for what work belongs in which stage
references/pipeline-implementation.mdStage structure, file layout, class and function conventions
references/bclearer-code-style.mdbclearer-specific formatting and naming (overrides general clean coding style)
references/bie-integration.mdWhen and how to delegate to bie-data-engineer for domain work
references/configuration-management.mdConfiguration flow, env var rules, path resolution, application-pipeline boundary
references/bunit-implementation-guidelines.mdbUnit implementation patterns, domain configuration, bUnit Type generalisation, and type extraction refactoring

The base data-engineer references (clean-coding-index.md, testing-index.md) remain fully in scope, but references/bclearer-code-style.md takes precedence for formatting and naming where it specifies a stricter rule.

Blocking requirement: Load stage-guidelines.md before placing any B-unit in a stage folder or deciding which stage a piece of logic belongs to. The canonical stage folder names are 1c_collect, 2l_load, 3e_evolve, 4a_assimilate, 5r_reuse — no other stage names are valid.

Sub-skill Delegation

In addition to the clean coding sub-skills inherited from data-engineer, you delegate:

TaskDelegate To
BIE domain object implementation (enums, creators, objects, factories)bie-data-engineer
BIE component ontology design (if no model yet exists)bie-component-ontologist → bie-data-engineer

bclearer-Specific Additions to Implement Mode

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

Construction Order for Pipelines (Step 3 addition)

Follow the approved pipeline topology from the architecture design. Within each stage:

1. Common knowledge (enums, types, constants)
2. Domain objects / BIE components (delegate to bie-data-engineer)
3. Stage adapters (ingest and load/export)
4. Stage processors/services (transform/enrich)
5. Stage orchestrators (wires adapters + services)
6. Pipeline runner / entry point (wires all stages)

Code Layout Convention

Each pipeline lives under a dedicated package. Recommended structure:

[pipeline_name]/
├── common_knowledge/       # pipeline-level enums, types, constants
├── bie/                    # BIE domain objects (if applicable; bie-data-engineer's output)
├── adapters/
│   ├── ingest/             # Stage 1 adapters
│   └── export/             # Stage 4 adapters
├── services/               # Stage 3 processing/transform logic
├── orchestrators/          # Stage orchestrators
└── runners/                # Entry point(s)

See references/pipeline-implementation.md for file-level conventions within each folder.

Additional Verification (Step 5 addition)

Beyond pytest/mypy/ruff, verify:

  • All stage folders use only the canonical names: 1c_collect, 2l_load, 3e_evolve, 4a_assimilate, 5r_reuse (no invented stage names)
  • No pd.read_*(), json.load(), open() or equivalent parsing calls in 1c_collect B-units (that is Load's job)
  • No business logic, classifications, or derivations in 2l_load B-units (that is Evolve's job)
  • No bclearer_interop_services imports in 3e_evolve or 4a_assimilate B-units
  • Each stage is independently testable (no direct cross-stage imports)
  • Each stage has independent unit tests
  • One e2e test per top-level pipeline runner (tests/e2e/test_<pipeline>_b_application_runner.py)
  • One e2e test per thin-slice runner (sub-pipeline runnable on its own — tests/e2e/<thin_slice>/test_<thin_slice>_b_application_runner.py)
  • E2E tests invoke runners via run_b_application(app_startup_method=<runner>); per-slice conftest.py wires BConfigurations, output paths, and external service config
  • Interop services appear only in adapters/ — not in services/ or orchestrators/
  • BIE factories only in bie/ — not in services/ or adapters/
  • Universe is created in the runner, not in a stage
  • No module-level or global mutable state
  • Configuration follows references/configuration-management.md — no os.getenv() in B-units or orchestrators, all paths absolute in the Universe

See skills/clean-code-tests/SKILL.md § "E2E Tests — Pipeline Runner + Thin-Slice Convention" for the full e2e folder layout, conftest.py conventions, and review checklist. The bclearer-specific addition is the runner invocation idiom (run_b_application(app_startup_method=...)).


bclearer-Specific Additions to Review Mode

bUnit Type Extraction (Review/Refactor sub-mode)

When reviewing existing bclearer pipeline code, additionally assess whether concrete bUnits should be refactored into reusable bUnit Types. Follow the process in references/bunit-implementation-guidelines.md § "Review/Refactor Mode: bUnit Type Extraction":

  1. Catalogue all bUnits with their helper functions and varying parameters
  2. Group bUnits that call the same helper function with different parameters
  3. Extract the varying parameters as bUnit Type constructor arguments
  4. Implement the bUnit Type in libraries/core/bclearer_core/objects/b_units/types/
  5. Refactor concrete bUnits to inherit from the type, passing parameters via constructor
  6. Verify all gate contracts, data lineage traces, and tests remain unchanged

Add these checks to the review checklist:

PrincipleExpectedActualStatus
bUnit atomicityEach b_unit_process_function() does one thing
Helper function fan-inShared helpers identified as type candidates
bUnit Type extractionDuplicate patterns refactored into types
Gate contract preservationInput/output registers unchanged after refactor

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

PrincipleExpectedActualStatus
Canonical stage namesOnly 1c_collect, 2l_load, 3e_evolve, 4a_assimilate, 5r_reuse
Collect boundaryNo file parsing (pd.read_*, json.load, open().read()) in 1c_collect
Load boundaryNo business logic / classifications / derivations in 2l_load
Evolve / Assimilate boundaryNo bclearer_interop_services imports in 3e_evolve or 4a_assimilate
Stage separationEach stage in its own module/class
Adapter boundaryInterop services only in adapters
BIE boundaryBIE factories only in bie/
Universe scopingUniverse created at runner level
bclearer code styleBackslash continuations, named kwargs, verbose naming
Construction orderPipeline code follows leaf-before-whole
Configuration managementEnv vars at entry point only; absolute paths in Universe; B-units read-only
Unit test coverageEach stage has independent unit tests
E2E test coverageOne e2e test per top-level runner; one per thin-slice runner; per-slice conftest.py wires BConfigurations + paths

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

33.29%
按下载量换算40

Claude

29.62%
按下载量换算35

Cursor

19.78%
按下载量换算24

Gemini CLI

8.35%
按下载量换算10

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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