DataGate is a boundary layer skill that parses untrusted files through a deterministic tool to separate data from model instructions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-boundary
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 data-boundary 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 DataGate?
DataGate serves as a critical security and organization layer for AI agents handling external datasets. By utilizing this within the ecosystem of Openclaw Skills, developers can ensure that raw file text from CSV or JSON sources never enters the model context directly. This approach enforces a strict boundary where tools handle the raw data and the model only interacts with the structured, sanitized results.
Its primary purpose is to maintain a separation of concerns: the tool reads the data, emits structured metadata, and the model reasons over that structured output. This prevents suspicious text within a dataset from being treated as a model instruction, significantly improving the safety and reliability of data-heavy AI workflows.
DataGate Use Cases
- Analyzing large CSV exports without overflowing the model context window.
- Summarizing untrusted JSON data while flagging potential prompt injection patterns.
- Inspecting structured data schema and field types before performing statistical analysis.
- Securing AI workflows against instruction-like text embedded within external data sources.
How DataGate Works
- The AI agent identifies a local CSV or JSON data source that requires analysis.
- The system runs a bundled Python script to ingest the data using deterministic parsing logic.
- The tool generates a structured JSON output containing high-level summaries, schema information, and alerts.
- The model inspects the structured results, including any warnings about instruction-like text patterns.
- The agent provides a final answer or analysis based solely on the structured tool output rather than the raw file text.
DataGate Setup
To integrate this functionality into your Openclaw Skills environment, ensure you have Python 3 installed. You can trigger the parser using the following commands:
Basic CSV parse:
python3 {baseDir}/scripts/ingest_data.py --input /path/to/file.csv
Explicit JSON parse with bounded limits:
python3 {baseDir}/scripts/ingest_data.py --input /path/to/file.json --format json --max-preview-rows 10 --max-string-length 120
DataGate Data Schema & Taxonomy
The parser organizes information into a consistent JSON contract to help Openclaw Skills process data efficiently:
| Section | Description |
|---|---|
| source | Includes the file path, detected format, and applied parser limits. |
| summary | Provides a high-level overview of the total size and shape of the data. |
| schema | Details field-level metadata and inferred primitive types (e.g., integers, strings). |
| alerts | Contains findings from heuristic scans for suspicious text or parsing errors. |
| preview_rows | A bounded array of structured data rows for direct model analysis. |
DataGate Advanced Features
- Automated format detection for files lacking extensions or using non-standard headers.
- Configurable input byte limits to block unexpectedly large files before processing begins.
- Heuristic scanning for prompt injection patterns such as "ignore previous instructions" or shell-like exfiltration.
- Metadata-first reasoning that allows agents to discuss dataset structure without loading the entire file into memory.
- Truncation flags that signal to the model when only a partial view of the data is available.
SKILL.md
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