Supports six vector database options (Pinecone, Weaviate, Milvus, Chroma, Qdrant, pgvector) and six embedding models optimized for different use cases and providers
Covers five advanced retrieval patterns: hybrid search combining dense and sparse retrieval, multi-query generation, contextual compression, parent document retrieval, and HyDE (hypothetical document embeddings)
Fetches rag-implementation from wshobson/agents and configures it for Cursor.
3
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The CLI shows a list of agents. Use arrow keys and space to select Cursor:
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/rag-implementation
Restart Cursor to activate rag-implementation. Access via /rag-implementation in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
from langgraph.graph import StateGraph, START, END
from langchain_anthropic import ChatAnthropic
from langchain_voyageai import VoyageAIEmbeddings
from langchain_pinecone import PineconeVectorStore
from langchain_core.documents import Document
from langchain_core.prompts import ChatPromptTemplate
from langchain_text_splitters import RecursiveCharacterTextSplitter
from typing import TypedDict, Annotated
classRAGState(TypedDict): question:str context:list[Document] answer:str# Initialize componentsllm = ChatAnthropic(model="claude-sonnet-4-6")embeddings = VoyageAIEmbeddings(model="voyage-3-large")vectorstore = PineconeVectorStore(index_name="docs", embedding=embeddings)retriever = vectorstore.as_retriever(search_kwargs={"k":4})# RAG promptrag_prompt = ChatPromptTemplate.from_template("""Answer based on the context below. If you cannot answer, say so.
Context:
{context}
Question: {question}
Answer:""")asyncdefretrieve(state: RAGState)-> RAGState:"""Retrieve relevant documents.""" docs =await retriever.ainvoke(state["question"])return{"context": docs}asyncdefgenerate(state: RAGState)-> RAGState:"""Generate answer from context.""" context_text ="\n\n".join(doc.page_content for doc in state["context"]) messages = rag_prompt.format_messages( context=context_text, question=state["question"]) response =await llm.ainvoke(messages)return{"answer": response.content}# Build RAG graphbuilder = StateGraph(RAGState)builder.add_node("retrieve", retrieve)builder.add_node("generate", generate)builder.add_edge(START,"retrieve")builder.add_edge("retrieve","generate")builder.add_edge("generate", END)rag_chain = builder.compile()# Useresult =await rag_chain.ainvoke({"question":"What are the main features?"})print(result["answer"])
Advanced RAG Patterns
Pattern 1: Hybrid Search with RRF
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever
# Sparse retriever (BM25 for keyword matching)bm25_retriever = BM25Retriever.from_documents(documents)bm25_retriever.k =10# Dense retriever (embeddings for semantic search)dense_retriever = vectorstore.as_retriever(search_kwargs={"k":10})# Combine with Reciprocal Rank Fusion weightsensemble_retriever = EnsembleRetriever( retrievers=[bm25_retriever, dense_retriever], weights=[0.3,0.7]# 30% keyword, 70% semantic)
Pattern 2: Multi-Query Retrieval
from langchain.retrievers.multi_query import MultiQueryRetriever
# Generate multiple query perspectives for better recallmulti_query_retriever = MultiQueryRetriever.from_llm( retriever=vectorstore.as_retriever(search_kwargs={"k":5}), llm=llm
)# Single query → multiple variations → combined resultsresults =await multi_query_retriever.ainvoke("What is the main topic?")
Pattern 3: Contextual Compression
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor
# Compressor extracts only relevant portionscompressor = LLMChainExtractor.from_llm(llm)compression_retriever = ContextualCompressionRetriever( base_compressor=compressor, base_retriever=vectorstore.as_retriever(search_kwargs={"k":10}))# Returns only relevant parts of documentscompressed_docs =await compression_retriever.ainvoke("specific query")
Pattern 4: Parent Document Retriever
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
from langchain_text_splitters import RecursiveCharacterTextSplitter
# Small chunks for precise retrieval, large chunks for contextchild_splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=50)parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)# Store for parent documentsdocstore = InMemoryStore()parent_retriever = ParentDocumentRetriever( vectorstore=vectorstore, docstore=docstore, child_splitter=child_splitter, parent_splitter=parent_splitter
)# Add documents (splits children, stores parents)
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
