rdf-ttl-pipeline — json to ttl conversion rdf-ttl-pipeline, LOGI-MASTER-DASH, community, json to ttl conversion, ide skills, ontology consistency checking, maintaining data integrity with rdf-ttl-pipeline, installing rdf-ttl-pipeline, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Semantic Web Agents needing to maintain consistency between relational data and ontologies through JSON to TTL conversion and validation. rdf-ttl-pipeline is a skill that converts JSON data to TTL format and checks ontology consistency, ensuring data integrity and accuracy.

Features

Converts JSON data to TTL format for ontology consistency
Validates column usage against a single source of truth (SSOT)
Generates TTL files and logs used columns for auditing
Performs ontology consistency checks to ensure data integrity
Loads column specifications from JSON files for configuration
Creates validation reports for data quality assurance

# Core Topics

json to ttl conversion ontology consistency checking maintaining data integrity with rdf-ttl-pipeline installing rdf-ttl-pipeline
macho715 macho715
[0]
[0]
Updated: 3/8/2026

Agent Capability Analysis

The rdf-ttl-pipeline skill by macho715 is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for json to ttl conversion, ontology consistency checking, maintaining data integrity with rdf-ttl-pipeline.

Ideal Agent Persona

Perfect for Semantic Web Agents needing to maintain consistency between relational data and ontologies through JSON to TTL conversion and validation.

Core Value

Empowers agents to convert JSON data to TTL format, validate column usage, and ensure ontology consistency, leveraging the power of RDF and TTL protocols, with features like column specification loading and validation reporting.

↓ Capabilities Granted for rdf-ttl-pipeline

Converting JSON data to TTL for ontology integration
Validating column usage in relational data
Ensuring consistency between relational data and ontologies

! Prerequisites & Limits

  • Requires HVDC JSON data and column specifications
  • Limited to TTL file output and validation reporting
Labs Demo

Browser Sandbox Environment

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

rdf-ttl-pipeline

Install rdf-ttl-pipeline, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

목적

관계형 데이터와 온톨로지 간 일관성 유지.

사용 시점

  • JSON → TTL 변환
  • 컬럼 사용 검증
  • 온톨로지 정합성 확인

입력

  • HVDC JSON 데이터
  • 컬럼 스펙 (JSON)
  • assets/columns.hvdc_status.example.json

출력

  • TTL 파일
  • 사용 컬럼 로그
  • 검증 리포트

절차

  1. 컬럼 스펙(SSOT) 로드
  2. JSON → TTL 변환 실행
  3. 사용 컬럼 감사 로그 생성
  4. 정합성 검증

필수 참조

  • AGENTS.md - 프로젝트 규칙 (최우선)
  • SSOT.md - 단일 진실원 (hvdc-logistics-ssot 스킬 참조)

참조

  • assets/columns.hvdc_status.example.json
  • scripts/validate_used_cols.py
  • references/RDF_MAPPING_GUIDE.md

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is rdf-ttl-pipeline?

Perfect for Semantic Web Agents needing to maintain consistency between relational data and ontologies through JSON to TTL conversion and validation. rdf-ttl-pipeline is a skill that converts JSON data to TTL format and checks ontology consistency, ensuring data integrity and accuracy.

How do I install rdf-ttl-pipeline?

Run the command: npx killer-skills add macho715/LOGI-MASTER-DASH/rdf-ttl-pipeline. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for rdf-ttl-pipeline?

Key use cases include: Converting JSON data to TTL for ontology integration, Validating column usage in relational data, Ensuring consistency between relational data and ontologies.

Which IDEs are compatible with rdf-ttl-pipeline?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for rdf-ttl-pipeline?

Requires HVDC JSON data and column specifications. Limited to TTL file output and validation reporting.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add macho715/LOGI-MASTER-DASH/rdf-ttl-pipeline. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use rdf-ttl-pipeline immediately in the current project.

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