Use your source text to produce a grounded answer or a relevance shortlist.
Copy a prompt into your agent with the oo Skill installed. Add your page links and source notes before running it.
Use source documents as context for a focused question.
/oo Cohere: Use Cohere model [model ID] to answer [question] with documents [documents]. Ask for citations where supported and explain unanswered points. Keep source-based statements distinguishable from general explanation.
Find the most relevant candidates within a supplied document set.
/oo Cohere: Use Cohere rerank model [model ID] for query [query] and candidate documents [documents], returning the top [N]. Map result indices back to the original document titles and show relevance scores as relative ranking signals, not truth or probability measures.
Set up oo CLI and the oo Skill in your agent.
Install OOMOL →Connect with a Cohere API key.
Connect Cohere →Use /oo with the pages to read, the changes to make, and where to save the result.
Connection Guide →Choose Your Agent
Generate chat responses with supplied documents, rerank document candidates for a query, and create text embeddings through Cohere. These operations work with the material you provide; they do not create a searchable knowledge base on their own.
Generate document-aware answers and rerank supplied candidates.
Create vectors for supplied text with the appropriate input type.
Generate a synchronous text response using the Cohere Chat API.
Leina 
Wanta 
cohere.chat Generate embeddings for text inputs using the Cohere Embed API.
cohere.embed_texts Rank text documents by relevance to a query using the Cohere Rerank API.
cohere.rerank_documents No matching tools.
Provide relevant text and identifying titles or IDs. A focused document set makes it easier to connect statements to their sources.
Use the indices and relevance scores to prioritize review of the supplied candidates. Reranking does not verify factual accuracy or search outside that set.
Yes, supply a compatible model, texts and input type appropriate to documents or queries. Store and search those vectors through a separate retrieval system.
Manage which optional storage OOMOL can use. You can change these choices from the footer at any time.