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 rasinmuhammed · MCP Server · ★ 68
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is misata safe to install? View the security audit →
Misata You declare the outcome. Misata generates the data that provably matches it. Realistic, relational rows that hit exact revenue curves, fraud rates, referential integrity, and statistical structure. From a sentence, YAML, or your database. No real data, no ML model. [](https://misata.studi
| Stars | 68 |
| Forks | 4 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.5026354142845/100 |
| Last Updated | 2026-09-16 |
| Created | 2025-12-16 |
| Platforms | mcp, python |
| Est. Tokens | ~25k |
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misata is Synthetic data that hits the numbers you declare, exactly. Multi-table with verified foreign-key integrity, deterministic, no model in the data path. Python + MCP server. In simple terms, a powerful d. It is categorized as a MCP Server with 68 GitHub stars.
misata is primarily written in Python. It covers topics such as data-engineering, data-generation, database-seeding.
You can find installation instructions and usage details in the misata GitHub repository at github.com/rasinmuhammed/misata. The project has 68 stars and 4 forks, indicating an active community.
misata is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to misata on Agent Skills Hub include orchestkit, mcp-fusion, xclaude-plugin. 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: