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by dreadnode · Agent Tool · ★ 89
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Robopages Robopages are YAML based files for describing tools to large language models (LLMs). They simplify the process of defining and using external tools in LLM-powered applications. By leveraging the robopages function calling server, developers can avoid the tedious task of manually writing JSON declarations for each tool. This approach streamlines tool integration, improves maintainability, and allows for more dynamic and flexible interactions between LLMs and external utilities. Like man pages but for robots! Usage Install the robopages server by following the instructions in the robopages-cli repository. Download this repository and copy it to your system folder (or run the command). Start the server by running . Your tools are now available at for any LLM to use. Refer to the robopages-cli repository for usage information and examples. Syntax A robopage is a YAML file describing one or more tools that can be used by an LLM as functions. In its simplest form, a robopage is a YAML file with a and a section referencing a command line tool: yaml description: You can use this for a description.
| Stars | 89 |
| Forks | 4 |
| Language | Dockerfile |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62.6159641620719/100 |
| Last Updated | 2025-05-02 |
| Created | 2024-10-18 |
| Est. Tokens | ~14k |
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robopages is A YAML based format for describing tools to LLMs, like man pages but for robots!. It is categorized as a Agent Tool with 89 GitHub stars.
robopages is primarily written in Dockerfile.
You can find installation instructions and usage details in the robopages GitHub repository at github.com/dreadnode/robopages. The project has 89 stars and 4 forks, indicating an active community.
robopages is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to robopages on Agent Skills Hub include tutor-skills, repren. 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.
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