No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by rapiddweller · MCP Server · ★ 113
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is datamimic safe to install? View the security audit →
DATAMIMIC — Governed Test Data for Regulated Enterprises This repository contains the DATAMIMIC Community Edition (CE). MIT-licensed, Python-native, MCP-ready. CE is fully usable standalone for deterministic synthetic data generation and PII-aware pseudonymization. The Enterprise Platform adds governed workflows, PII scanning, role-based access, audit logging, scheduling, multi-system execution, and the full operational layer that regulated enterprises require. 👉 Enterprise Platform: datamimic.io 📅 Book a strategy call: datamimic.io/contact What is DATAMIMI
| Stars | 113 |
| Forks | 3 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.1630051622538/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-21 |
| Created | 2024-12-09 |
| Platforms | mcp, python |
| Est. Tokens | ~22k |
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datamimic is Model-driven synthetic test data for CI/CD and analytics - deterministic, privacy-preserving, and domain-aware. Includes Python APIs, XML pipelines, and MCP/IDE integration to orchestrate realistic da. It is categorized as a MCP Server with 113 GitHub stars.
datamimic is primarily written in Python. It covers topics such as data-anonymization, data-generation, data-masking.
You can find installation instructions and usage details in the datamimic GitHub repository at github.com/rapiddweller/datamimic. The project has 113 stars and 3 forks, indicating an active community.
datamimic is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to datamimic on Agent Skills Hub include NoPII, skunit, misata. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: