Supply an APL query and the incident question it should address. AI can explain the result and organize evidence for your next investigation step.
Copy a prompt into your agent with the oo Skill installed. Add your page links and source notes before running it.
Run an APL query for a chosen window and organize evidence related to your incident question.
/oo Axiom: Run this Axiom APL query for [start time] to [end time]: [APL query]. Explain the results for [investigation question] and identify missing context.
List datasets and compare their metadata with your expected logging inventory.
/oo Axiom: List Axiom datasets and read [dataset IDs or names]. Compare their metadata with this expected logging inventory: [inventory].
Set up oo CLI and the oo Skill in your agent.
Install OOMOL →Connect with an Axiom API token.
Connect Axiom →Use /oo with the pages to read, the changes to make, and where to save the result.
Connection Guide →Choose Your Agent
Axiom datasets store queryable event data. APL results and dataset metadata provide a basis for incident analysis while keeping the requested time window explicit.
Run a query with time windows, variables, and pagination options.
List datasets and retrieve one dataset by ID or name.
List Axiom datasets visible to the current token.
Leina 
Wanta 
axiom.list_datasets Get an Axiom dataset by ID or unique name.
axiom.get_dataset Create an Axiom dataset.
axiom.create_dataset Delete an Axiom dataset by ID or unique name.
axiom.delete_dataset Run an Axiom Processing Language query through the global API endpoint.
axiom.run_apl_query No matching tools.
Provide a valid APL query, time window, and investigation question. If you need query drafting, include dataset and field information.
Ask it to cite query values and mark conclusions that need additional data or context.
No. Queries run when requested. Continuous monitoring and alert rules are not part of these tasks.
Manage which optional storage OOMOL can use. You can change these choices from the footer at any time.