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 oneal2000 · Agent Tool · ★ 94
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Skill Retrieval Augmentation for Agentic AI RAG retrieves knowledge. SRA retrieves capabilities. A community resource for studying and evaluating Skill Retrieval Augmentation (SRA). This repository releases SRA-Bench and SR-Agents, providing data, baselines, and evaluation pipelines for research on retrieval-based skill augmentation in LLM agents. ⭐ If you find this resource useful, we would be truly grateful if you could star this repo and cite our paper 🔥 Why Skill Retrieval Augmentation? Modern
| Stars | 94 |
| Forks | 12 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.7731638689566/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-02 |
| Created | 2026-04-16 |
| Platforms | python |
| Est. Tokens | ~17k |
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SR-Agents is SRA-Bench and SR-Agents: a benchmark and toolkit for skill-retrieval-augmented LLM agents.. It is categorized as a Agent Tool with 94 GitHub stars.
SR-Agents is primarily written in Python. It covers topics such as agent-memory, agent-skills, agentic-ai.
You can find installation instructions and usage details in the SR-Agents GitHub repository at github.com/oneal2000/SR-Agents. The project has 94 stars and 12 forks, indicating an active community.
SR-Agents is released under the MIT license, making it free to use and modify according to the license terms.
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