SkillCheatSheet
rag-implementation
An AI agent skill from wshobson/agents, scored 57 out of 100 by code.
Code score by group
It loses the most points in Procedure (15), the group that checks whether the steps are numbered, read the context first and say what to do when something fails.
What the author says it does
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.
This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.
- Repository
- wshobson/agents
- Folder
- plugins/llm-application-dev/skills/rag-implementation
- License
- MIT
- GitHub stars
- 42,433
- SKILL.md lines
- 139
- Other files
- 1
Add rag-implementation to your agent
npx skills add wshobson/agents --skill rag-implementationThe command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored.
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Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.