Background We are building automated Post Incident Report (PIR) generation using Super Magic skills. The skills fetch the parent incident ticket thread to extract timeline, stakeholder, and resolution data. This works well for standard tickets, but breaks down on high-volume P1 incidents where thread length exceeds the current cap. Request 1 — Remove / Increase the message cap Currently include_messages returns a maximum of 75 messages per ticket (most recent first), with no way to retrieve earlier messages. We would like the cap removed entirely so that the full ticket thread is always available to skills and automations. Why: For major incidents, the earliest messages contain the most critical information — the initial report, first diagnosis, and escalation steps. These are exactly the messages that get cut off. A formal Post Incident Report cannot be accurately generated from a partial thread. There is no workaround that doesn't introduce inaccuracy or require additional manual effort from engineers. Request 2 — Message type filtering Allow callers to specify which message types to include or exclude when using include_messages. For example, the ability to filter to only: technician notes, customer messages, and external notes — excluding status change events, PSA system messages, blank notes, and merge notifications. Why: In our analysis of a real P1 incident ticket with 130 messages, 55% of the returned messages were pure system noise — status changes, blank notes, PSA events, and merge notifications. None of these are useful for AI processing. This noise consumes a significant portion of the available context window, reducing the quality and accuracy of AI-generated outputs. Filtering it out would make skills faster, more accurate, and more reliable across all ticket types — not just major incidents. Combined impact These two requests together would make Super Magic skills robust for any ticket size and type. Without them, skills working on high-volume tickets are fundamentally limited — they are processing incomplete, noisy data and producing outputs that require manual correction, which defeats the purpose of automation.