A technical utility for exploring SQLite database schemas and transforming relational data into structured JSON maps or arrays.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install civ6-adjacency-optimizer-sqlite-map-parser
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 civ6-adjacency-optimizer-sqlite-map-parser 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 SQLite Map Parser?
The SQLite Map Parser is a developer-centric skill designed to bridge the gap between relational SQLite databases and JSON-based application environments. It provides a systematic workflow for analyzing unknown database schemas, identifying intricate table relationships, and extracting data into clean, hierarchical JSON structures. This tool is part of the broader ecosystem of Openclaw Skills and is particularly effective for developers needing to serialize spatial data, game assets, or legacy database content for modern web and mobile frontends.
By automating the discovery of primary keys, foreign keys, and table metadata, the skill reduces the manual overhead of database reverse-engineering. It emphasizes a structured approach: first exploring the sqlite_master and PRAGMA information, then applying transformational logic to create indexed or array-based JSON payloads that are ready for immediate use in production environments.
SQLite Map Parser Use Cases
- Reverse-engineering unknown SQLite databases to map out table schemas and constraints.
- Converting spatial or grid-based database records into JSON tilesets for game development.
- Serializing complex relational data with foreign key joins into a single, nested JSON file.
- Automating the extraction of configuration and metadata tables into frontend-friendly formats.
How SQLite Map Parser Works
- Discovery phase: The skill queries the sqlite_master table to list all available tables and their original CREATE statements.
- Schema Inspection: It uses PRAGMA table_info and PRAGMA index_list to identify primary keys, unique constraints, and column types.
- Relationship Mapping: The workflow identifies foreign key links to understand how tables should be joined during the extraction process.
- Extraction Logic: Using a Python-based row_factory, it retrieves database rows and transforms them into dictionary-like objects.
- Transformation: The data is mapped into either a unique-key dictionary or an ordered array based on the developer's requirements.
- Serialization: The final output is generated as a structured JSON object containing both metadata and the core data items.
SQLite Map Parser Setup
To use this Openclaw Skills utility, ensure you have a Python environment with sqlite3 support. No external database drivers are typically required for standard SQLite files.
# Verify python and sqlite3 availability
python --version
# Access the database through your preferred Openclaw agent interface
# No additional installation steps are required beyond the core agent setup.
SQLite Map Parser Data Schema & Taxonomy
The skill organizes extracted data into a standardized JSON format that typically includes a metadata header and a body of items. The taxonomy is as follows:
| Component | Format | Description |
|---|---|---|
| metadata | Object | Key-value pairs containing global DB info like dimensions or versioning. |
| items/tiles | Array or Map | The primary data records, indexed by ID or coordinate keys. |
| relational_fields | Nested Object | Data joined from secondary tables via foreign key lookups. |
Schema mappings are dynamic based on the PRAGMA info discovered during the initial exploration phase.
SQLite Map Parser Advanced Features
- Coordinate-based Key Generation: Automatically computes (x, y) coordinates from linear primary IDs for grid-based data structures.
- Graceful Error Handling: Includes safe query wrappers that handle missing tables or OperationalErrors without breaking the automation pipeline.
- Metadata-First Architecture: Prioritizes the extraction of configuration tables to define the context of the subsequent data payload.
- Support for Complex Joins: Handles LEFT JOIN operations and ID-based relationship mapping to ensure no data is lost when merging tables in Openclaw Skills.
SKILL.md
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