Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
mog install mogteam/wshobson-rag-implementation
Sourced from wshobson/agents under the MIT license.
After installing, your AI assistant needs a pointer to the skill file. Use the agent card below or the --wire flag.
Install + auto-wire
mog install mogteam/wshobson-rag-implementation --target cursor --wireCreates .cursor/rules/wshobson-rag-implementation.mdc pointing to the skill.
Agent card
## Wshobson Rag Implementation Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases. - **Install**: `mog install mogteam/wshobson-rag-implementation --target cursor` - **Type**: skill - **Path after install**: `.cursor/skills/wshobson-rag-implementation/SKILL.md` - **Targets**: cursor, claude-code, codex, gemini-cli, windsurf, generic When editing, read and follow @.cursor/skills/wshobson-rag-implementation/SKILL.md
Paste into AGENTS.md, .cursor/rules, or your agent's instructions.
Install paths by target
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Free
mog install mogteam/wshobson-rag-implementationSource
wshobson
github.com/wshobson/agentsThis package is sourced from the above repository and distributed on mog under its original license.
@mogteam
Official curated skills and prompt templates from the mog.md team.