Converts tabular CSV data into structured graph-ready nodes, edges, and triples for Neo4j, RDF stores, and knowledge graphs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install csv-graph-loader-generator
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install csv-graph-loader-generator using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is CSV Graph Loader Generator?
The CSV Graph Loader Generator is a specialized skill designed to automate the transformation of flat, tabular CSV datasets into rich, interconnected graph database structures. By analyzing schema rows, entity identifiers, and column associations, it bridges the gap between traditional structural data and modern knowledge graph construction.
This utility generates production-ready ingestion assets, including Neo4j Cypher loading scripts, RDF Turtle triples, and standard property graph JSON models. It reduces manual mapping overhead by automatically detecting data types, inferring relationships, and enforcing ID-based deduplication strategy matrices, making it a vital asset in the Openclaw Skills ecosystem for data engineers and AI workflows.
CSV Graph Loader Generator Use Cases
- Preparing raw relational CSV data for ingestion into Neo4j graph databases.
- Generating semantic web-compliant RDF triple mappings and Turtle files.
- Constructing unified automated knowledge graphs from distinct tabular data files.
- Developing reproducible ETL graph data pipeline configurations.
- Prototyping property graph structures directly from unstructured enterprise reports.
How CSV Graph Loader Generator Works
- Parse CSV: The utility ingests the source file, checking for structural integrity, headers, and delimiter patterns.
- Analyze Schema: It dynamically runs type inference over data columns to determine whether they represent strings, integers, floats, booleans, dates, or references.
- Detect Entities: It matches column patterns to identify potential core identity nodes based on key signatures like ID or name.
- Infer Relationships: The engine maps structural column intersections to logical graph edges and directional associations.
- Create Schema & Mappings: It defines specific node/relationship wrappers and establishes strict property assignment rules.
- Validate & Generate Loaders: The validation layers check integrity before generating full, executable Neo4j Cypher strings, RDF triples, or property JSON objects.
CSV Graph Loader Generator Setup
Install Openclaw Skills and add the CSV Graph Loader Generator component via your package or skill manager interface. Ensure python runtime environments are equipped with key parsing and graph libraries.
# Install the necessary library dependencies
pip install pandas polars rdflib networkx pydantic
# Register or invoke the skill within your workspace environment
openclaw skill install csv_graph_loader_generator
CSV Graph Loader Generator Data Schema & Taxonomy
The skill accepts custom rules and yields structured output configurations matching downstream targets. Data schemas are partitioned cleanly into node definitions, edge definitions, and graph constraints.
| Object Component | Data Fields / Properties Included | Primary Output Targets |
|---|---|---|
| Nodes | id, type (Label), properties dictionary (metadata metrics) | Neo4j, RDF stores, JSON |
| Edges | source_id, target_id, type (Relationship direction/label) | Cypher Scripts, Turtle |
| Mapping Config | column_name, target_entity, transformation_strategy | YAML/JSON profiles |
CSV Graph Loader Generator Advanced Features
- Multi-Strategy Mapping: Switch between fully autonomous extraction patterns, custom hint overrides, or explicit rigid mapping files.
- Smart Deduplication Chains: Automates record merging by utilizing stable node keys, preventing duplicate edges across equivalent targets.
- Downstream Orchestration Hooks: Native compilation bindings feed directly into Graph Query Optimization tools, Schema Validation pipelines, and automated ETL workflows.
- Advanced Data Type Inference: Automatically transforms time formats and numerical types into graph-native schema constraints.
SKILL.md
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