Materialize knowledge artifacts into structured knowledge graphs with revision logs.
npx skills add https://github.com/rd162/skills --skill knowledge-management-rd162
Please help me install this Agent Skill. Skill: knowledge-management Source: https://github.com/rd162/skills/tree/main/knowledge-management Command: npx skills add https://github.com/rd162/skills --skill knowledge-management-rd162
This Knowledge Management (KM) skill helps you create, revise, and materialize structured knowledge artifacts (Fact, Concept, Procedure, Narrative, Ontology) with revision tracking, observation tagging, provenance, and knowledge-graph relations, enabling consistent capture and evolution of knowledge across projects.
Start by narrating a recent event and then materialize a fact and a procedure to capture the outcomes.
High-intent search queries and answers about installing and using this skill.
You turn context into a knowledge graph by applying a narrative-first materialization workflow to extract structured knowledge artifacts. This process organizes facts, concepts, and procedures into interconnected nodes with revision logs and cross-artifact relations.
Tracking provenance for knowledge artifacts requires applying verification paths and observation tagging during materialization. This generates revision logs and verification trails to ensure quality and traceability across your projects.
You materialize facts and procedures by narrating the event first, then extracting outcomes into standardized templates. This enforces a narrative-first workflow ensuring contextual inputs become structured artifacts with proper provenance.
Yes, the knowledge management workflow adapts to various storage backends including Obsidian, Markdown files, and other KM systems. It uses a unified materialization process to ensure interoperability across these file-based platforms.
You can organize Fact, Concept, Procedure, Narrative, and Ontology artifacts. The system stitches scalable cross-artifact relations between these types, enabling future-forward gap analysis and domain-spanning knowledge graphs.
Use narrative-first materialization when you need consistent capture and evolution of contextual knowledge with traceable provenance. It ensures structured artifacts are properly verified and cross-referenced before entering your knowledge graph backend.
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