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 sambanova · Agent Tool · ★ 181
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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ToolBench Recent studies on software tool manipulation with large language models (LLMs) mostly rely on closed model APIs (e.g. OpenAI), as there is an significant gap of model accuracy between those closed models and all the rest open-source LLMs. To study the root cause of the gap and further facilitate the development of open-source LLMs, especially their capabilities on tool manipulation, we create the ToolBench. The ToolBench is a benchmark consisting of diverse software tools for real-world tasks. We also provide easy-to-use infrastructure in this repository to directly evaluate the execution success rate of each model. Contributions to this repo are highly welcomed! We are excited to see new action generation algorithms and new testing tasks. #
| Stars | 181 |
| Forks | 11 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 69.279476379072/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-27 |
| Created | 2023-05-19 |
| Platforms | python |
| Est. Tokens | ~22k |
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toolbench is ToolBench, an evaluation suite for LLM tool manipulation capabilities.. It is categorized as a Agent Tool with 181 GitHub stars.
toolbench is primarily written in Python.
You can find installation instructions and usage details in the toolbench GitHub repository at github.com/sambanova/toolbench. The project has 181 stars and 11 forks, indicating an active community.
toolbench is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to toolbench on Agent Skills Hub include agent-skills, claude-code-skill-factory, claude-plugins. 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: