RAG

Build, optimize, and debug RAG pipelines with chunking strategies, retrieval tuning, evaluation metrics, and production monitoring.

This skill is an offline documentation pack for building RAG systems and is internally coherent. Before installing/using: (1) enforce prompt isolation and input sanitization at runtime to mitigate prompt-injection risks documented here; (2) follow the security.md guidance when you connect to external embedding/vector APIs — avoid sending sensitive PII/PHI to third-party APIs unless you have the proper agreements and controls; (3) test any ingestion code in a staging environment to confirm metadata-based access control (filters/namespaces) works as expected; (4) because the skill is instruction-only, it cannot itself exfiltrate data, but any implementation you build following these instructions can — review network/credential handling in your runtime. If you want lower risk, use the docs as a read-only reference rather than enabling autonomous agent invocation of the skill.

Type: OpenClaw Skill Name: rag Version: 1.0.0 This skill bundle provides a comprehensive guide for building, optimizing, and securing Retrieval-Augmented Generation (RAG) pipelines. All files contain documentation and illustrative code snippets that are aligned with the stated purpose. Notably, the `security.md` file explicitly addresses critical security concerns such as PII detection, access control, compliance (GDPR, HIPAA, SOC2), and prompt injection prevention within RAG systems, demonstrating a strong security-first approach. There is no evidence of malicious intent, data exfiltration, unauthorized execution, or prompt injection attempts against the OpenClaw agent itself. The content is purely educational and best-practice oriented.