name: ask description: Ask a question about past work and get a synthesized answer informed by conversation history from the RAG database disable-model-invocation: true allowed-tools: Bash(curl *) argument-hint: "[question]"
RAG Ask
Answer the user's question using RAG context: "$ARGUMENTS"
Instructions
-
Read the plugin config to get the backend endpoint:
cat ~/.claude/plugins/claude-rag/config.json 2>/dev/null || echo '{"connection":{"endpoint":"https://api.clauderag.io"}}' -
Search the RAG database for relevant context:
curl -s -X POST <endpoint>/api/v1/search \ -H "Content-Type: application/json" \ -d '{"query": "<user_question>", "limit": 10, "threshold": 0.4}' -
Synthesize an answer based on the search results:
- Combine information from multiple results to form a coherent answer
- Cite specific sessions, tools, and dates when referencing past work
- If the answer comes from code (tool_result from Read), include relevant code snippets
- If results are from sub-agents, mention which agent type found the information
- Be transparent about confidence: if results have low scores (<0.5), caveat accordingly
-
Structure the response:
- Start with the direct answer
- Follow with supporting evidence from RAG results
- End with "Sources" listing the sessions/events referenced
-
If insufficient context is found:
- Say clearly that the RAG database doesn't have enough context
- Suggest what the user could search for instead
- Offer to answer from general knowledge (without RAG)
Related skills
Prompt Engineering
Data & AI
Prompt engineering best practices and templates to maximize AI outputs.
claudeCursorWindsurf+1beginner
289
78
994
Data Visualization
Data & AI
Generates data visualizations and charts tailored to your data.
claudeCursorWindsurfintermediate
198
56
811
RAG Architecture Setup
Data & AI
Setup guide for RAG (Retrieval-Augmented Generation) architectures.
claudeCursorWindsurfadvanced
167
51
770