A comprehensive 45-module GIS knowledge base and automation framework optimized for spatial data processing, quality analysis, and multi-software integration.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install giser
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 giser 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 GIS Comprehensive Knowledge Base Skill?
The GIS Comprehensive Knowledge Base is a production-grade, modular skill framework designed for AI coding agents to navigate complex geospatial workflows. Covering coordinate transformations, Chinese national surveying standards (GB/T), and spatial databases, this repository acts as an expert-level reference architecture. By integrating it with Openclaw Skills, developers can immediately resolve cross-platform data conversion bugs, generate validated PyQGIS or ArcPy scripts, and automate quality inspections.
Built upon a strict 7-stage enterprise workflow, this skill bridges the gap between raw geographical data and high-performance automation. Whether you are managing LiDAR point clouds, deploying cloud-native WebGIS platforms (using PMTiles, COG, and GeoParquet), or implementing AI-driven GeoAI algorithms, this skill offers robust solutions to over 160 structural anti-patterns, ensuring clean, compliant, and highly reproducible spatial analysis.
GIS Comprehensive Knowledge Base Skill Use Cases
- Automating spatial data quality checks against Chinese national standards (such as GB/T 18316 and GB/T 24356) using automated QGIS or ArcPy scripts.
- Executing highly accurate coordinate system projections and datum conversions among WGS84, CGCS2000, Beijing 54, and Xian 80.
- Developing automated ETL pipelines to handle CAD to GIS transformations, mapping CAD layers (such as Southern CASS 11.0) directly into GDB/PostGIS spatial databases.
- Building custom ArcGIS Pro add-ins or automated workflow agents that leverage modern GIS architectures like DuckDB Spatial and GEE.
- Running point cloud classification and ground filtering pipelines (PTD vs CSF) dynamically for massive lidar datasets.
How GIS Comprehensive Knowledge Base Skill Works
- Demand Locking: The AI agent parses the user's GIS task and creates a formal demand specification sheet.
- Deep Data Insight: The engine probes the dataset to identify spatial formats, coordinate reference systems (CRS), and underlying anomalies before executing operations.
- Schema and Protocol Generation: The system evaluates standard OGC requirements and mapping criteria to generate a target execution blueprint.
- Environment Preprocessing: Preparing spatial virtual environments, loading libraries (like GeoPandas, terra, or GDAL/OGR), and establishing database connections.
- Core Execution: Running automated scripts, PyQGIS algorithms, or FME transformations based on verified templates.
- Self-Inspection: Programmatically verifying outputs against structural constraints and validating topology to generate quality inspection logs.
- Archiving and Feedback: Exporting deliverables while analyzing feedback logs to record knowledge gaps and upgrade the local indexing directory.
GIS Comprehensive Knowledge Base Skill Setup
To deploy this skill within your Openclaw Skills environment, configure your directory and verify dependencies.
Clone or copy the repository modules into your local agent environment:
mkdir -p ~/.workbuddy/skills/GIS
cp -r ./GIS_SKILL/* ~/.workbuddy/skills/GIS/
Ensure that your Python environment has spatial libraries installed:
pip install geopandas rasterio shapely pyproj gdal duckdb-spatial
Initialize the self-evolution logging system:
# Verify configuration parameters
cat ~/.workbuddy/skills/GIS/feedback/config.json
GIS Comprehensive Knowledge Base Skill Data Schema & Taxonomy
The GIS skill framework organizes its extensive knowledge repository into seven logical groups alongside tracking folders for automated self-evolution:
| Directory / File | Scope | Primary Purpose |
|---|---|---|
SKILL.md |
Master Neural Hub | Serves as the central router and version mapping registry for the agent. |
01_ to 04_ |
Foundation Layer | Contains fundamental coordinate theory, Chinese standard datum shifts, and spatial structures. |
05_ to 10_ |
Standards & Specifications | Lists national GB/T surveying criteria, mapping rules, QA procedures, and Mermaid workflows. |
12_ to 20_ |
Software Tools | Comprehensive automation manuals for ArcGIS Pro, PyQGIS, CASS, iData, SuperMap, GlobalMapper, and FME. |
21_ to 27_, 35_ |
Development & Automation | Core coding recipes for Python Spatial (GeoPandas, DuckDB), R Spatial (terra, sf), GEE, and GeoAI. |
28_, 29_, 30_ |
Practical Sandbox & Anti-patterns | Over 160 structured error-handling scenarios (Wrong/Correct/Why) and CAD-GIS transition scripts. |
37_ |
Self-Evolution Engine | Logic parameters for automatic knowledge gap detection and indexing updates. |
feedback/ |
Runtime Logs | Houses runtime telemetry including knowledge_gaps.md and user feedback entries. |
GIS Comprehensive Knowledge Base Skill Advanced Features
- Dual-Version Protocol Support: Co-maintains parallel reference architectures for multiple software versions (e.g., ArcGIS Pro 3.6 vs 3.7) to prevent agent hallucinations across breaking changes.
- Geospatial AI Agent Paradigms: Out-of-the-box system prompt templates for Reviewer, Inversion, Pipeline, and Orchestrator GIS agent architectures.
- Self-Evolutionary Loop: Continuously scans runtime errors and logs unresolved queries into a tracking table to trigger automated incremental knowledge searches.
- Unified Multi-Language Geometry Engine: Offers exact behavioral mapping across JTS, GEOS, Shapely, NTS, and JSTS to ensure cross-language geometric consistency.
- Cloud-Native Geo-Pipeline: Pre-engineered blueprints for PMTiles, COG (Cloud Optimized GeoTIFF), and GeoParquet deployment pipelines optimized for modern WebGIS stacks.
SKILL.md
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