What It Does
RAG packages a focused media & files workflow for an AI agent. Designs, tunes, and debugs retrieval-augmented generation (RAG) pipelines: chunking, embeddings, hybrid retrieval, reranking, and grounded answers. Use when a system returns the wrong passages, misses a document that is indexed, cites nothing, or hallucinates over good context; when choosing a vector store, an embedding model, a chunk size, or a reranker; when similarity scores collapse after a model swap; when a metadata filter empties the result set; when answers ignore mid-context facts; when follow-up questions retrieve the wrong thing; when indexing PDFs, scanned pages, tables, code, or transcripts; when GDPR erasure, tenant isolation, or prompt injection from indexed documents is the problem; or when per-query cost or p95 latency has to come down. Covers reindex migrations, corpus freshness, evaluation sets, and agentic and graph retrieval. Not for splitter internals (`rag-chunking`), scoring rubrics (`rag-evaluation`), or LangChain APIs (`langchain`). It is best suited to users who can review the resulting actions and provide only the accounts, files, or command access needed for the task. It is not a substitute for human approval on destructive, financial, security-sensitive, or public-facing actions.
Popular Use Cases
- Create or edit a common file format
- Extract information from files
- Prepare a polished deliverable
- Automate repetitive media operations
Install this Agent Skill
OpenClaw
clawhub install @ivangdavila/ragCommands derived from the public ClawHub API record. Checked 2026-11-03. Review the source before running them.
Requirements
| Review the source instructions before installation | Required / review |
| Paid service | Check source |
| Supported system | Check source |
Why It’s Popular
RAG addresses a recognizable media files workflow and is included automatically from current public adoption and activity signals.
