by empower-ai · Agent Tool · ★ 215
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 215 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 69/100 |
| Open Issues | 1 |
| Last Updated | 2024-10-05 |
| Created | 2024-05-10 |
| Platforms | python |
| Est. Tokens | ~87k |
Empower Functions Empower Functions is a family of LLMs(large language models) that offer GPT-4 level capabilities for real-world "tool using" use cases, with full compatibility support to be served as a drop-in replacement. Live Demo • Huggingface Repo • Website • Discord Update New Empower Functions v1.1 We have just launched new v1.1 of the Empower Functions family.
Looking for a empower-functions alternative? If you're comparing empower-functions with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Easily create LLM tools and agents using plain Bash/JavaScript/Python functions.
Agent harness and tookit for building AI agents and agentic applications. CLI and SDKs included
A simple, intuitive toolkit for quickly implementing LLM powered applications.
An Automated AI-Powered Prompt Optimization Framework
User friendly CLI tool for AI tasks. Stop thinking about LLMs and prompts, start getting results!
Home Assistant add-on: Claude Code, OpenAI/GPT, Gemini
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: