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 caspel26 · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is django-ninja-aio-crud safe to install? View the security audit →
Async CRUD framework for Django Ninja Automatic schema generation · Filtering · Pagination · Auth · M2M management <a href="https://github.com/caspel26/django-ni
| Stars | 51 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.153018268572/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-28 |
| Created | 2024-09-16 |
| Platforms | browser, mcp, python |
| Est. Tokens | ~18k |
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django-ninja-aio-crud is Async dynamic CRUD framework for Django Ninja — built-in auth, filtering, pagination, automatic serialization, and MCP tools for AI agents.. It is categorized as a MCP Server with 51 GitHub stars.
django-ninja-aio-crud is primarily written in Python. It covers topics such as ai-agents, api, async.
You can find installation instructions and usage details in the django-ninja-aio-crud GitHub repository at github.com/caspel26/django-ninja-aio-crud. The project has 51 stars and 1 forks, indicating an active community.
django-ninja-aio-crud is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to django-ninja-aio-crud on Agent Skills Hub include django-orm-lens, augments-mcp-server, action_mcp. 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: