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 NiJingzhe · Agent Tool · ★ 77
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SimpleLLMFunc safe to install? View the security audit →
LLM as Function, Prompt as Code, Context-Centric Chinese README Update Notes (0.8.4) PyRepl terminal isolation: PyRepl worker processes now isolate fd // from host terminals, capture direct fd writes and subprocess stdout/stderr into tool output, and drop late child-process output so
| Stars | 77 |
| Forks | 5 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.243048553307/100 |
| Last Updated | 2026-07-20 |
| Created | 2025-04-16 |
| Platforms | python |
| Est. Tokens | ~298k |
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SimpleLLMFunc is A simple and well-tailored LLM application framework that enables you to seamlessly integrate LLM capabilities in the most "Code-Centric" and "Context-Centric" manner. LLM As Function, Prompt As Code.. It is categorized as a Agent Tool with 77 GitHub stars.
SimpleLLMFunc is primarily written in Python. It covers topics such as agent, agent-development-framework, agent-framework.
You can find installation instructions and usage details in the SimpleLLMFunc GitHub repository at github.com/NiJingzhe/SimpleLLMFunc. The project has 77 stars and 5 forks, indicating an active community.
SimpleLLMFunc is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to SimpleLLMFunc on Agent Skills Hub include ai-agent-tools-catalog, oreilly-ai-agents, mcp-server. 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: