Personal Ontology

v1.0.0

Help users build and maintain a Personal Ontology - a Palantir-style graph of Objects (identity, beliefs, predictions, goals) and Links (relationships between them) that enables AI-driven decision-making and life alignment.

0· 18·0 current·0 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClawOpenClaw
Benign
high confidence
Purpose & Capability
Name/description (Personal Ontology) match the code and instructions: the skill bootstraps an ontology from the user's markdown notes, validates links, and renders a Mermaid graph. The included script and templates are consistent with that purpose.
Instruction Scope
SKILL.md explicitly instructs agents to scan the user's notes/vault (journal, project docs, archived notes) and to surface candidate Objects for review. That is expected for this purpose, but the instructions give the agent discretion to run regular passive scans and to read other local files (task board/journal) which could include sensitive content. The skill emphasizes user confirmation before committing, which reduces but does not eliminate privacy risk.
Install Mechanism
There is no install spec (instruction-only) which minimizes risk. The single JS utility (scripts/render-ontology.js) runs locally and optionally requires the third-party 'beautiful-mermaid' package for ASCII/SVG outputs; that dependency is not installed by the skill itself and is only used if those flags are requested. No remote downloads or obscure install URLs are present.
Credentials
The skill requests no credentials and declares no required env vars, but both SKILL.md/SETUP.md and the render script reference an optional ONTOLOGY_DIR env var. This is reasonable and optional for operation. No unrelated secrets or external tokens are requested.
Persistence & Privilege
The skill runs without always:true and does not request elevated privileges. It does instruct agents to create/update files under a canonical My_Personal_Ontology folder and to store small local state (example: memory/ontology-nudges.json) for nudges. These write operations are coherent with its purpose but mean the skill will persist data in your notes directory when you accept commits or enable automated scans.
Assessment
What to know before installing: - Privacy: The skill is designed to scan your local notes/journal (including archived notes) to extract beliefs, predictions, goals, and projects. If you allow bootstrap or enable daily/automated scans, the agent will read those files. Only enable scanning you are comfortable with and review candidates before committing. - Local writes: When you accept suggestions the skill will create/update markdown files in My_Personal_Ontology and may write small local state files (e.g., memory/ontology-nudges.json). Back up important notes first if you are cautious. - No network credentials requested: The skill does not ask for API keys or other credentials. If you later integrate it with external task boards or services, treat those integrations as separate and only supply credentials if you trust the code/service. - Optional dependency: The renderer script can use 'beautiful-mermaid' for ASCII/SVG export; that package is not installed automatically. If you use that feature, install it from the public registry or inspect it first. - Safety steps: (1) Run the bootstrap on a small subset of notes or a safe test vault to see what gets extracted. (2) Keep automated/passive scans disabled until you trust the suggestions and behavior. (3) Inspect scripts/render-ontology.js (present) before executing and set ONTOLOGY_DIR to a dedicated folder. (4) If you share the agent or machine, be aware the ontology files may contain sensitive personal content. Overall: the skill is internally consistent with its stated purpose. The main risks are privacy/accidental exposure from scanning and the fact it will write files locally — these are expected for a notes-scanning ontology tool. If you want stronger guarantees, run it in a sandboxed workspace or review candidate outputs before committing.

Like a lobster shell, security has layers — review code before you run it.

latestvk973a51fdfnt9p878sxpq97b21804nr9

License

MIT-0
Free to use, modify, and redistribute. No attribution required.

Comments