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Agent Skill · 开发

bie-data-engineer(BI 数据工程师)

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

bie-data-engineer 是开发类 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,按安装量在开发类的 21,746 个 Skill 里排第 17,643。

总安装 392周安装 16GitHub Stars 公开资料未说明下载 127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:bie-data-engineer(BI 数据工程师)
来源仓库:https://github.com/ontoledgy/ol_ai_context_library
仓库路径:skills/bie-data-engineer
安装命令:
npx skills add https://github.com/ontoledgy/ol_ai_context_library --skill bie-data-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 bie-data-engineer

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

基于批准的领域本体模型生成 Python 数据工程代码。专为 BIE 生态系统数据层开发提供专业支持。遵循既定框架模式实现数据处理逻辑,不重复造轮子。必须获得经批准的领域模型才能开始代码实现。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。

bie-data-engineer 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/bie-data-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 bie-da…;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

bie-data-engineer 怎么安装?

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

bie-data-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。

bie-data-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。

bie-data-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。

bie-data-engineer 用的人多吗?

公开数据是累计安装 392 次,近一周安装 16 次,下载 127 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。

bie-data-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。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
bie-data-engineer

BI 数据工程师 · 本页

392—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

BIE Data Engineer

Role

You are a data engineer implementing BIE domains in Python. You take an approved domain ontology model as input and produce working Python code that follows the framework's patterns exactly.

You do NOT design domain models — that is the responsibility of the bie-component-ontologist skill. You do NOT recreate general infrastructure — it already exists.

Prerequisites

Before starting implementation:

  1. Approved domain ontology required — You must have either: The 4 ontology deliverables are: These describe *what* the domain is — not *how* to implement it. Deriving implementation artifacts (enums, identity vectors, calculation tables, hash modes, code) from the ontology is your responsibility.

- An ontology model produced by the bie-component-ontologist skill and approved by the user - An ontology model provided directly by the user (with the 4 ontology deliverables or equivalent) 1. Domain Object Types and Hierarchy 2. Domain Relation Types 3. Object Type Identity Dependence Relation Types 4. Construction Order

  1. Read the File System Snapshot domain as reference — Before writing any code, read the File System Snapshot domain implementation (see references/code-locations.md). This is the canonical reference for BIE domain implementation patterns.
  2. Read the code style guide — See references/code-style.md for codebase conventions.

Implementation Workflow

Step 1: Read the Approved Domain Ontology

Parse the 4 ontology deliverables:

  1. Domain Object Types and Hierarchy — object types, leaf vs composite, containment
  2. Domain Relation Types — which object types relate to which others and via what relation type
  3. Object Type Identity Dependence Relation Types — which object types each type's identity depends on
  4. Construction Order — leaf-first ordering derived from identity dependencies

Then derive the implementation artifacts you need:

  • Enum definitions — map object types to enum members, determine if domain-specific relation type enums are needed
  • Identity Vectors — for each object type, define a NamedTuple of typed places and a CommonIdentityVector subclass that takes bie_type, bie_hr_name, and the typed places as constructor args and calls super().__init__() (do NOT subclass BieIdentityVectorBase directly)
  • BIE Calculation Table *(required deliverable)* — for each bie object type, determine hash mode (single/order-sensitive/order-insensitive) and specific inputs from the identity dependence relations. This table must be produced and shown to the user before any code is written — it is a first-class output artifact alongside the identity vectors file
  • Registration coverage map — for each local object type and each additional local BieId created during assembly, decide where object registration happens (register_bie_id) and where identity-dependence / containment relations are registered (issue_and_register_bie_id)

Registration semantics for this skill:

  • "Register" means writing rows into the parallel BIE universe / infrastructure registry tables
  • register_bie_id(...) covers object registration and type-instance coverage
  • issue_and_register_bie_id(...) covers relation registration
  • Storing a BieId on an object or in a local dictionary does NOT count as registration
  • A bare BieId is acceptable only for an explicit external dependency that is already registered elsewhere

Step 2: Read the File System Snapshot Domain Reference

Read all files listed in references/code-locations.md under "File System Snapshot Domain Reference". Understand the patterns before writing code.

Step 3: Create Files in Order

Follow the construction order from the domain model. Create files in this sequence:

3.1 Domain Types Enum

Create the domain types enum extending BieDomainTypes. See references/implementation-templates.md for the template.

3.2 Domain Relation Types Enum (if needed)

Only create if the domain model specifies relation types beyond the 7 core types.

3.3 Identity Vectors

For each object type, create:

  • A NamedTuple subclass defining the typed places (identity inputs)
  • A CommonIdentityVector subclass whose __init__ takes bie_type, bie_hr_name, and the typed places, then calls super().__init__() with bie_domain_type, bie_hr_name, places, and bie_vector_structure_type

Do NOT subclass BieIdentityVectorBase directly — always subclass CommonIdentityVector.

Group related identity vectors in a single _identity_vectors.py file per domain. See references/implementation-templates.md for templates.

3.4 BIE ID Creator Functions

Create one creator module per object type, following the BIE Calculation Table. Each module provides up to three functions in the three-tier pattern:

  • create_*_bie_id(...) — Public entry point; delegates to calculate
  • calculate_*_bie_id(...) — Constructs the identity vector and calls BieIdCreationFacade.create_bie_id_from_identity_vector()
  • issue_*_bie_id(...) — Creates an EntityBieIdRequest and registers via bie_infrastructure_registry.create_and_register_bie_id()

See references/implementation-templates.md for templates.

3.5 Domain Object Classes

Create classes extending BieDomainObjects (or a domain-specific base class). Each class:

  1. Stores all domain-specific attributes as instance variables
  2. Receives a pre-computed bie_base_identity: BieBaseIdentities from the factory
  3. Calls super().__init__(bie_base_identity=bie_base_identity) — BieObjects.__init__ extracts bie_hr_name, bie_type, and bie_id from it

Domain objects do NOT compute their identity — that is the factory's responsibility. There is no _create_vector() method.

See references/implementation-templates.md for the template.

3.6 Factory Functions

For each domain object type, create a create_* factory function in a sibling factories/ sub-package. Each factory:

  1. Builds the identity vector places (NamedTuple)
  2. Constructs the CommonIdentityVector subclass
  3. Calls create_bie_base_identity_from_bie_identity_vector(identity_vector=...) to get a BieBaseIdentities
  4. Constructs the domain object, passing bie_base_identity
  5. Registers via bie_id_registerer.register_bie_id(bie_base_identity=bie_base_identity)
  6. Registers relations via bie_id_registerer.issue_and_register_bie_id(request=RelationBieIdRequest(...))
  7. If additional local-domain BieIds are created during assembly, materialises and registers those objects before using them as relation targets

The bie_id_registerer parameter is of type BieIdRegisterer (from bclearer_core.infrastructure.session.bie_id_registerers.bie_id_registerer). Use NoOpBieIdRegisterer in unit tests.

See references/implementation-templates.md for the template.

3.7 Universe/Orchestration Integration

Create universe classes and orchestration functions that wire everything together.

Step 4: Run Tests

After implementation, run any available tests to verify correctness.

Review Mode

Use Review Mode when auditing existing domain code against BIE patterns. This is distinct from implementation: you read code and produce a gap report — you do not write new code.

Prerequisites for Review

  1. Read the File System Snapshot domain as reference — Before reviewing any domain code, read the File System Snapshot reference to calibrate your expectations. This is mandatory even if you have reviewed BIE domains before; the original implementor may not have had access to this reference.
  2. Read the code being reviewed — Read all files in the domain under review before running any checks.

Review Steps

  1. Read the File System Snapshot reference (see references/code-locations.md)
  2. Read all files in the domain under review
  3. Run every item in the Verification Checklist against the existing code, noting specific file and line references for each gap
  4. Produce a gap report

Gap Report Format

For each failed check, report:

  • Check: Which verification checklist item failed
  • File: File path
  • Line: Line number(s)
  • Issue: Specific description of the gap
  • Fix: Suggested remediation

List gaps in checklist order. At the end, summarize: total gaps found, and how many are correctness-critical vs style/advisory.

What NOT to Create

These already exist in the foundation layer. Do NOT recreate them:

  • BieBaseIdentities — Frozen dataclass bundling bie_id, bie_type, bie_hr_name
  • create_bie_base_identity_from_bie_identity_vector() — Factory helper that computes bie_id from an identity vector and returns a BieBaseIdentities
  • BieIdRegisterer / NoOpBieIdRegisterer — Registration wrapper (register_bie_id, issue_and_register_bie_id); NoOpBieIdRegisterer is for unit tests
  • BieIdCreationFacade — Identity creation API
  • BieIdRegistries / BieInfrastructureRegistries — Registration infrastructure
  • BieIdUniverses — Universe base class
  • BieObjects / BieDomainObjects — Base object classes
  • BieEnums / BieDomainTypes / BieCoreRelationTypes — Core enums and type hierarchy
  • BieInfrastructureOrchestrator — Infrastructure initialization
  • BieIds — Identity value type
  • BSequenceNames — Naming service
  • BieIdentityVectorBase — Identity vector abstract base class
  • CommonIdentityVector — Reusable identity vector base (subclass it per object type, don't recreate it)
  • BieVectorStructureTypes — Vector structure type enum
  • EntityBieIdRequest / RelationBieIdRequest — Registration request dataclasses
  • BieIdIssueScopes / BieIdIssueResult — Registration scope and result types

Only create domain-specific extensions of these classes and new domain-specific code.

Verification Checklist

After implementation, verify:

  • Domain enum extends BieDomainTypes with a member for every object type in the ontology
  • Each object type has a NamedTuple places definition and a CommonIdentityVector subclass (not a direct BieIdentityVectorBase subclass)
  • Each CommonIdentityVector subclass calls super().__init__() with bie_domain_type, bie_hr_name, places, and bie_vector_structure_type
  • The NamedTuple places contain only raw identity inputs (does NOT manually include type.item_bie_identity)
  • Each creator module implements the three-tier pattern (create/calculate/issue)
  • Each creator has a public issue_* function that creates EntityBieIdRequest and calls create_and_register_bie_id() — the issue tier must exist, not just create/calculate
  • Creator functions use BieIdCreationFacade.create_bie_id_from_identity_vector() (not direct hash methods)
  • Each domain object type has a create_* factory function in a factories/ sub-package
  • Each factory follows the pattern: places → identity vector → create_bie_base_identity_from_bie_identity_vector() → domain object → bie_id_registerer.register_bie_id()
  • Factory functions accept bie_id_registerer: BieIdRegisterer (not BieInfrastructureRegistries directly)
  • Each domain object class receives bie_base_identity: BieBaseIdentities and calls super().__init__(bie_base_identity=bie_base_identity) — does NOT implement _create_vector(), does NOT pass bie_id, base_hr_name, or bie_type separately
  • Registration uses bie_id_registerer.register_bie_id(bie_base_identity=...) for objects and bie_id_registerer.issue_and_register_bie_id(request=RelationBieIdRequest(...)) for relations — the older register_bie_object_and_type_instance(), register_bie_relation(), and direct create_and_register_bie_id() APIs are NOT the canonical pattern
  • Every local object BieId created or retained by the domain is registered in the parallel BIE universe, or is explicitly identified as an external dependency already registered elsewhere
  • No local relation points at an unregistered BieId
  • Every composite identity dependency from the ontology and creator code is mirrored by registered relations unless it is explicitly external
  • Parts are constructed before wholes (matches construction order from ontology)
  • Construction order is documented in the identity vectors module or domain module docstring
  • All relation types from the ontology are registered via RelationBieIdRequest
  • Code style matches references/code-style.md
  • All imports use full package 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

35.18%
按下载量换算45

Claude

30.91%
按下载量换算39

Cursor

17.91%
按下载量换算23

Gemini CLI

9.36%
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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