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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline. | wshobson/ | 40k | 9 repos | ~1.1k | Automated safety check: Pass | MIT | 6 days ago |
| 2 | Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from… | elementalsouls/ | 4.8k | — | ~2.6k | Automated safety check: Pass | MIT | yesterday |
| 3 | Expert in vector databases, embedding strategies, and semantic search implementation. | aiskillstore/ | 433 | 7 repos | ~563 | Automated safety check: Pass | No licence | yesterday |
| 4 | Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and… | open-edge-platform/ | 171 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 5 | Deploy and consume Intel Multimodal DataPrep from prebuilt images or a repository checkout. | open-edge-platform/ | 171 | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | yesterday |
