Ontology Engineer

Extract candidate ontology models from enterprise business systems AND build/maintain personal knowledge graphs from any file system. Use when: ontology extr...

This skill is broadly consistent with an on-device ontology/graph extractor, but exercise caution before running it against your real data. Key points to consider: - Metadata mismatch: The registry summary claims no install/requirements, but SKILL.md and scripts expect python3, optional LibreOffice or MS Word, and multiple pip packages. Request or confirm an explicit install spec before proceeding. - Filesystem scope: The scripts are designed to scan arbitrary directories and extract structured data from many file types. The SKILL.md promises a mandatory interactive 'Step 1.5' confirmation before reading files — verify the agent integration actually enforces this checkpoint (i.e., the agent must ask you and not auto-run scans). - Run safely: First run in a sandbox or container, or point the scripts at a small test folder. Use the provided dry-run options (scan_filesystem.py --dry-run, etc.) to inspect what would be processed. Do not point it at /, your home directory, or backups until you are confident of behavior. - Dependency installs: Installing the advertised Python packages pulls code from PyPI. If you plan to install, prefer doing so in an isolated virtualenv/container and review package versions. On Windows, COM automation requires Word and pywin32; on Linux/macOS LibreOffice is required for legacy .doc/.wps conversion — these are system-level dependencies. - Network & exfiltration checks: SKILL.md states 'no external API calls', and no remote endpoints are visible in the provided files. Still, before running, scan the code for network libraries (requests, urllib, socket) and monitor outbound connections (netstat) during a test run. - Review scripts for enforcement of user consent: Ensure the agent wrapper or local runner does not bypass Step 1.5. If the code does not enforce an interactive prompt, require the agent to ask for folder approval before running any extraction commands. - If in doubt: ask the publisher/maintainer for clarifications about the registry/install mismatch, whether the agent enforces the interactive confirmation, and provide an explicit install manifest (requirements.txt or setup script) and a short audit of network behavior. If you must run it on sensitive data, do so only in an isolated environment after these checks.

Type: OpenClaw Skill Name: ontology-engineer Version: 1.1.1 The ontology-engineer skill bundle is a legitimate and well-documented tool designed for extracting business models and building personal knowledge graphs from local filesystems. The included Python scripts (e.g., scan_filesystem.py, extract_tables.py, and convert_doc.py) perform standard file traversal and data extraction using reputable libraries like python-docx and openpyxl. The security model explicitly mandates local-only processing, avoids external API calls, and requires a user-confirmation step (Step 1.5) before any semantic analysis begins. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found across the code or instructions.