**Job:** `f5d622219fda` **Worker:** helpdesk **Status:** completed **Elapsed:** 75s ## Prompt ``` PHASE 1 — scope discovery for the helpdesk-learning track. Sonnet model, 60-min hard cap (no override). Use mcp-autotask: 1. get_picklist_values on Tickets.queueID. From the returned list, identify the queueIDs for these EXACT queue names: - "General Support" (queue H1.1) - "Advanced Support" (queue H1.2) - "Critical Support" (queue H1.3) Do NOT include Earney IT Support Queue, security alerts, monitoring alerts, or Archived Tickets. 2. For each of those 3 queueIDs, count tickets where queueID matches AND the ticket's company has isActive=true. Use count_entities or query_entities with a count semantic. 3. Count distinct active companies (Companies where isActive=true). 4. Sum the per-queue active-client ticket counts to estimate total tickets to process across the 3 queues. Reply with ONLY (under 10 lines): - queueID and label for the 3 queues - ticket count per queue (active-client-only) - distinct active company count - total tickets to process across all 3 queues - any queues whose IDs you couldn't find (flag, don't guess) Don't pull ticket bodies; just counts and IDs. ``` ## Output ``` Phase 1 scope: - **H1.1 General Support** — queueID `29683491` — **2960** active-client tickets - **H1.2 Advanced Support** — queueID `29683492` — **17** active-client tickets - **H1.3 Critical Support** — queueID `29683493` — **28** active-client tickets - Distinct active companies: **109** - **Total in-scope tickets: 3005** (H1.1 = 98.5% of volume) - All 3 queue IDs resolved cleanly — none missing. Note: counts are all-time per queue with no date/status filter (instructions specified only queueID + company.isActive). H1.1 alone is ~2960; if scope needs trimming, applying a date window or open-status filter would shrink it sharply. ```