360 Secure SkillHub
360 安全技能中心

semantic-pkm-creator

核心功能是从批量线性文本中提取场景、概念、实体,分两轮构建结构化个人知识库:第一轮生成去重的骨架文件(含ID、定义、来源),待用户确认后,第二轮完善内容,构建以概念为核心的知识关联体系。典型场景为整理读书笔记、技术教程等文本类知识,实现知识复用。输入为批量线性文本,输出为三个结构化YAML文件及进度文档,所有内容基于源文本,严格遵循知识层级与关联规则。

agenticweb4
agenticweb4
热度 21.08
最近更新 2026-09-15 10:09:07
分类:知识依赖:内部调用大模型 / 读写文件应用场景:数据处理 / 笔记整理 / 概念解释

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add semantic-pkm-creator
Metadata
name
semantic-pkm-creator
description
Extracts callable scenes, concepts, and entities from source text for personal knowledge management in two rounds: a deduplicated skeleton for human confirmation, then IPO, decomposition, assembly, and relations with concepts as the hub.
metadata
{"language":"en","translation_of":"semantic-pkm-creator-cn","openclaw":{"homepage":"https://github.com/ontology-of-everything/concept-skills/tree/main/skills/en/semantic-pkm-creator"}}

Scene–Concept–Entity Extraction

Input is a batch of linear source texts. Output sce-scenes.yaml, sce-concepts.yaml, and sce-entities.yaml in the user-selected directory, with progress in _sce-round1-progress.md. The value is reuse: one scene can directly invoke a concept established elsewhere.

Principles

  1. Source first — define and IPO come from the source and stay within 50 words. Leave unsupported content empty rather than inventing it.
  2. Concepts are the hub — Establish concepts first, attach entities to concepts, and let scenes invoke only established concepts and entities.
  3. Prefer omission — A candidate without its own IPO or decomposition is not a concept. Merge abstractions into their parent and demote fragments to steps.
  4. Bound references — Keep two to six relations per concept and only the strongest relation for a knowledge-element pair.
  5. Stop for confirmation between rounds. Never start round two before approval.

Round One: Skeleton

Process five to ten texts per batch and update _sce-round1-progress.md.

  1. Read the complete batch and record progress.
  2. Extract candidates with only id + define + sources: scenes answer “how do I…”, concepts can express an IPO, and entities answer “did I use X?”
  3. Deduplicate globally: merge at 80% or greater semantic overlap, normalize synonymous IDs, and return overly fine steps to their parent. Record every X → Y merge.
  4. Write the three YAML files and mark remaining fields TODO.
  5. Stop and present deduplication and concept coverage for confirmation.

Round Two: Content

Use sources to return to the original text. Sequence words indicate steps; requirement and input words indicate IPO inputs.

  1. Fill from the hub outward: concepts first with complete IPO or decomposition, entities in parallel, then scenes with trigger, goal, and three to six progressive assembly stages.
  2. Read references/relations.md; add entity hierarchy and concept–entity invocation relations.
  3. Run completeness checks and write updated_at.

Completion requires a skeleton-only round one with a merge log; every concept has IPO or decomposition; every entity passes “I used X” and has hierarchy depth at most three; every scene invokes established concepts/entities in three to six stages; all references resolve; every definition has a source and stays within 50 words; there are no isolated concepts; and every concept has two to six relations.

Layer Meaning Question Valid form
Concept Class: executable action Which method should I use? IPO preserves independent logic; decomposition records stages, sub-concepts, and why each is needed
Entity Instance: identifiable person, tool, product, or named framework What did I use? “I used X,” where X is not an action
Scene Assembly: problem plus orchestration How do I…? Trigger, goal, three to six stages, invocations, and constraints

During round two, read references/relations.md for the direction and selection order of the eight relation types.