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Automated security scan — not a certification.
by ruc-datalab
EvoOntology: A Self-Evolving Ontology Layer for Data Agents ⚙️ EvoOntology插件为Claude Code/Codex 建立&进化本体层
# Add to your Claude Code skills
git clone https://github.com/ruc-datalab/EvoOntologyGuides for using ai agents skills like EvoOntology.
Last scanned: 9/18/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-09-18T08:47:51.622Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how EvoOntology compares with popular alternatives.
EvoOntology is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ruc-datalab. EvoOntology: A Self-Evolving Ontology Layer for Data Agents ⚙️ EvoOntology插件为Claude Code/Codex 建立&进化本体层. It has 1,106 GitHub stars.
Yes. EvoOntology passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/ruc-datalab/EvoOntology" and add it to your Claude Code skills directory (see the Installation section above).
EvoOntology is primarily written in Python. It is open-source under ruc-datalab on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh EvoOntology against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
Authors: Meiduo Chong, Shaolei Zhang*, Ju Fan, Xiaoyong Du Renmin University of China
EvoOntology is the first self-evolving ontology layer for data agents, aiming to bridge the agent-data gap over heterogeneous tables, files, and databases. It exposes a versioned Ontology Layer through MCP tools, grounds that layer in real workload evidence, and continuously adapts it from execution trajectories.
The Codex and Claude Code plugins build and evolve ontology layers over your data.
https://github.com/user-attachments/assets/e15f4acd-7161-4ae1-ba41-f2f3ea05488b
| Principle | Core idea |
|---|---|
| Active access | Retrieve only the semantics needed for the current step through MCP tools instead of injecting the full ontology. |
| Grounded construction | Build around the workload and commit semantic objects only after verification against the underlying data. |
| Targeted evolution | Diagnose interaction trajectories and apply localized updates to the interconnected Content, Schema, and Tool Layers. |
| Gated versioning | Publish a Candidate only when paired evaluation shows a reproducible improvement over its Parent. |
| Agent integration | Connect the ontology workspace and MCP runtime directly to supported agents through plugins. |
EvoOntology treats the Ontology Layer as trainable agent state—not model weights. A builder initializes grounded semantic objects from the workload and underlying data; an evolution agent then uses historical interactions to propose bounded updates and validates every Candidate against its Parent.
Three interconnected layers define the ontology's knowledge, representation rules, and runtime access:
| Layer | Role |
|---|---|
| Content Layer | A typed semantic graph with four node families: Terms, Mappings, Constraints, and Evidence. Semantic Relations connect Terms, while Structural References link Terms to Mappings and attach Constraints or Evidence to the objects they govern or support. |
| Schema Layer | Defines the fields of the four node families, the allowed Semantic Relation types, and the permitted Structural Reference patterns, thereby setting the ontology's representational boundaries. |
| Tool Layer | Exposes the ontology through browse_semantics, resolve_semantics, and a compact session manifest. The manifest initializes the session; detailed records and linked objects are retrieved on demand. |
ontology_v0.ontology_vN+1; otherwise retain the Parent and use the result in the next round.Install the plugin from the GitHub marketplace—no repository clone, virtual environment, or separate pip install is required.
claude plugin marketplace add ruc-datalab/EvoOntology
claude plugin install evoontology@evoontology
claude plugin list
Start a new session, then run:
/evoontology:build-ontology
/evoontology:evolve-ontology
/evoontology:explore-ontology
codex plugin marketplace add ruc-datalab/EvoOntology
codex plugin add evoontology-codex@evoontology
codex plugin list
Start a new thread, then ask Codex to use:
$build-ontology
$evolve-ontology
$explore-ontology
Both plugins use the same Build / Evolve / Explore workflow names; only the platform-native invocation syntax differs.
Codex prepares questions from user needs, relevant project history and grounded exploration; question/trajectory files are optional. Build and Evolve automatically open the outcome explorer. SQLite has built-in read-only task replay; other data sources use host tools with explicit observation recording. See Codex plugin.
Once built, the Data Agent can call browse_semantics and resolve_semantics without additional ontology configuration. See the usage guide for the full workflow and data boundaries.
Across the four-backbone analysis subset, the builder-constructed Initial Ontology Layer improves over ReAct without an Ontology Layer, and self-evolution produces a further gain with EvoOntology on all three benchmarks.
| Benc