by IBM · Agent Tool · ★ 50
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Responsible Prompting is an LLM-agnostic tool that aims at dynamically supporting users in crafting prompts that embed responsible intentions and help avoid harmful, adversarial prompts.
| Stars | 50 |
| Forks | 15 |
| Language | Jupyter Notebook |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 58.0386273606744/100 |
| Open Issues | 32 |
| Last Updated | 2026-09-17 |
| Created | 2024-08-02 |
| Est. Tokens | ~15k |
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responsible-prompting-api is Responsible Prompting is an LLM-agnostic tool that aims at dynamically supporting users in crafting prompts that embed responsible intentions and help avoid harmful, adversarial prompts.. It is categorized as a Agent Tool with 50 GitHub stars.
responsible-prompting-api is primarily written in Jupyter Notebook. It covers topics such as api, prompt-engineering, responsible-ai.
You can find installation instructions and usage details in the responsible-prompting-api GitHub repository at github.com/IBM/responsible-prompting-api. The project has 50 stars and 15 forks, indicating an active community.
responsible-prompting-api is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to responsible-prompting-api on Agent Skills Hub include J.A.R.V.I.S.2.0, agent-skills, agentic-ai-engineering. 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: