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wzp2026

职业技能个性学习路径生成

by [email protected] · GitHub ↗ · v2.4.1 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install learning-path-builder
Description
根据输入的职业技能标准文档,输出基于知识图谱的三层学习路径规划。适用于职业技能学习、职业资格认证学习。本技能以中国国内职业技能为目标,参考资源为国内官方资源。详细说明见 references/README.md
README (SKILL.md)

学习路径构建器 v2.4.0

核心功能

输入职业技能标准文档(如PDF)→ 输出三层知识图谱(JSON/CSV/JSON-LD格式)

核心设计:L1大领域 → L2模块 → L3叶节点,每节点0.5学时,来源标注(权威/参考/推断)

工作流程

第零步:检测多方向标准(如有人工智能5个方向则推荐默认方向)
第一步:提取PDF文本 → 写入 /tmp/kg_pdf_text_{job_id}.txt
第二步:动态提取L1职业功能领域(从原文结构,非固定4个框)
第三步:逐域构建L2/L3节点 → 增量写入 /tmp/kg_progress_{job_id}.json
第四步:生成 JSON + CSV(nodes/edges) + JSON-LD
第五步:生成来源说明文档 + 质量检查
每步完成后通过当前会话发送进度通知

核心原则

  1. 权威资源优先:osta.org.cn、openstd.samr.gov.cn、教育部/工信部/中国大学MOOC
  2. 搜索优先:先搜索权威来源,找不到再用参考来源,最后才推论
  3. 来源标注:每个节点标注 ✅权威 / ⚠️参考 / 🤖推断
  4. 动态L1:L1领域从PDF标准原文的职业功能结构动态提取,非固定框架

多方向标准处理

检测到多方向标准时(如人工智能工程技术人员5个方向):

  1. 分析各方向的学习热度、材料丰富度、应用前景
  2. 生成推荐报告,提供默认推荐
  3. 学习者未选择时使用默认方向

详细示例见 references/README.md

输出格式

每次构建同时生成4个文件:

  • {name}_kg.json — 主JSON
  • {name}_kg_nodes.csv — 节点表(Excel可打开)
  • {name}_kg_edges.csv — 关系表
  • {name}_kg.jsonld — JSON-LD开放格式

超时策略

  • 不设硬性超时,任务完整执行
  • 每步结果写文件,失败可从该步骤续接
  • references/ 目录包含详细说明文档,按需加载

详细说明:references/README.md 作者:Wang Zhipeng | 更新:2026-04-21 14:13

Usage Guidance
Things to consider before installing and running: - Missing runtime/dependency declarations: SKILL.md and scripts expect tools/libraries that are not declared in metadata. Ensure your environment provides: a suitable Python 3 runtime, PyYAML (yaml), and the pdftotext binary (poppler-utils) if you plan to process PDFs. Without these the skill may fail or behave unexpectedly. - Network access and scraping: the code actively fetches content from public Chinese sites (baike.baidu.com, osta.org.cn, openstd.samr.gov.cn, icourse163.org, etc.). Confirm you allow outbound HTTP(S) from the agent environment and consider rate limits / terms of service for those sites. - Long-running, no hard timeout: the SKILL.md states “no hard timeout” and incremental file writes and progress notifications; run it in a controlled/sandboxed environment to avoid runaway jobs or unexpected resource use. - File writes: the skill writes temporary files to /tmp and produces outputs in an output_dir; review these outputs and ensure sensitive documents are not unintentionally uploaded. - Legality/ToS: automated scraping of sites (e.g., Baidu Baike) may conflict with site terms of service—review before wide use. - Audit the scripts: although I saw no hidden external command-and-control endpoints, you should review scripts (especially truncated/omitted files) for any hardcoded or obfuscated endpoints before granting broader permissions. - If you need stricter safety: run the skill in an isolated container/VM with limited network access, install only required dependencies, and review logs/output files. If you want to prevent autonomous runs, keep model invocation controlled (disable autonomous invocation in your agent policy).
Capability Analysis
Type: OpenClaw Skill Name: learning-path-builder Version: 2.4.1 The 'learning-path-builder' skill bundle is a legitimate tool designed to generate structured learning paths from professional standard documents (PDFs). The bundle contains Python scripts (e.g., scripts/baidu_baike_extractor.py, scripts/rag_integration.py) that extract and validate information from authoritative sources such as Baidu Baike, Wikipedia, and Arxiv. The instructions in SKILL.md and README.md clearly define a multi-step workflow involving text extraction, knowledge graph construction, and multi-format output (JSON, CSV, JSON-LD). There is no evidence of malicious intent, data exfiltration, persistence mechanisms, or harmful prompt injections; all network and file operations are aligned with the stated purpose of building educational content.
Capability Assessment
Purpose & Capability
名字与描述(基于职业标准构建知识图谱与学习路径)与包含的脚本和工作流一致:脚本用于提取PDF文本、构建KG、从国内权威/参考站点验证内容(如百度百科、国家站点、慕课平台)。没有请求与目的不符的凭证或外部服务。
Instruction Scope
SKILL.md 明确描述了运行流程(提取PDF→解析章节→增量写入/tmp→多格式导出→来源验证并发送会话进度)。这与代码一致。但 SKILL.md 明确要求使用 `pdftotext` 提取全文,而 registry metadata 列出的“必需二进制/环境变量”为空——这是一个不一致(运行时可能失败)。此外,工作流程没有硬性超时且会进行网络检索/抓取(百度百科、政府/慕课站点),会产生外部网络流量并可能耗时较长;SKILL.md 要求通过“当前会话发送进度通知”,意味着运行时会反复输出中间结果。
Install Mechanism
包是 instruction-only(没有安装规范),但包含多个 Python 脚本(scripts/)。没有声明需要的运行时(如 Python 版本、PyYAML 等第三方库)或系统工具(pdftotext)。脚本大部分使用标准库 urllib/json/re/re,但部分脚本导入了 yaml(需要 PyYAML)。没有从不可信 URL 下载依赖或执行远程二进制,这降低了直接供应链风险,但缺乏明确依赖声明会导致运行失败或意外行为。
Credentials
技能未要求任何环境变量、凭证或配置路径,这与其以公开权威资源为主的设计一致。脚本会进行公开网站抓取(baike、gov、mooc),但不要求敏感凭据或访问其它技能的配置。
Persistence & Privilege
flags 中没有 always:true,也未声明要修改其他技能或系统范围配置。脚本写入 /tmp 和输出目录文件,但这属于本技能自身的正常产物,未表现出对系统持久化或跨技能权限的请求。
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install learning-path-builder
  3. After installation, invoke the skill by name or use /learning-path-builder
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v2.4.1
- 进度提醒使用当前会话,替换了原来的飞书消息 - 其他没有变化
v2.4.0
learning-path-builder v2.4.0 introduces a robust learning path construction system for Chinese vocational skills, featuring dynamic knowledge graph generation and multi-format export. - Supports input of vocational skill standard documents (e.g., PDF) and outputs a three-tier knowledge graph (L1: domain → L2: module → L3: leaf nodes) with authority-source annotation. - Automatically detects and recommends among multiple skill directions (e.g., in multi-track professions). - Exports results in JSON, CSV (nodes/edges), and JSON-LD formats, with clear source annotations (authoritative, reference, inferred). - Implements incremental file-based progress tracking and failure recovery, plus Feishu progress notifications. - Prioritizes authoritative Chinese resources and provides detailed documentation in references/README.md.
Metadata
Slug learning-path-builder
Version 2.4.1
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 2
Frequently Asked Questions

What is 职业技能个性学习路径生成?

根据输入的职业技能标准文档,输出基于知识图谱的三层学习路径规划。适用于职业技能学习、职业资格认证学习。本技能以中国国内职业技能为目标,参考资源为国内官方资源。详细说明见 references/README.md. It is an AI Agent Skill for Claude Code / OpenClaw, with 122 downloads so far.

How do I install 职业技能个性学习路径生成?

Run "/install learning-path-builder" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is 职业技能个性学习路径生成 free?

Yes, 职业技能个性学习路径生成 is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does 职业技能个性学习路径生成 support?

职业技能个性学习路径生成 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created 职业技能个性学习路径生成?

It is built and maintained by [email protected] (@wzp2026); the current version is v2.4.1.

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