Individual skill

atlas-ops-analytics

Analyze Atlas Commerce Operations workplace tasks that require strict JSON answers from operational database records, including cutoff-based fulfillment, refunds, carrier corrections with audit records, warehouse productivity, and support-health reviews. Use when a task provides a prompt, JSON request payload, answer template, and environment access for Atlas-style schema/data-dictionary/query services or controlled correction transactions.

experiments/codex_gpt5_5_xhigh/reports/skills/task_group_022/reflect-3/reflect-3_attempt_03/SKILL.md

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Oct 29, 2026
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Apache-2.0

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# Atlas Ops Analytics

## Core Workflow

1. Read the prompt, request payload, and answer template before querying data.
2. Treat the answer template as the output contract. Produce exactly the required keys, value types, rounding, and array ordering. Do not add commentary or extra fields.
3. Read schema and data-dictionary context from the task-provided environment access. Use the dictionary to identify source rows, canonical fields, snapshot fields, flags, and relationship keys.
4. Build the cohort first. Keep cohort filters separate from metric logic so denominators are auditable.
5. Query only the rows needed for the cohort and related events. If a response is paginated or marked truncated, fetch all pages before computing.
6. Deduplicate imported source events before metrics. For tables with `source_system`, `external_event_id`, and `ingested_at`, keep the latest ingestion per `(source_system, external_event_id)`, using the stable row id as a tie-breaker.
7. Compute final metrics locally or in SQL, but keep intermediate counts for validation: cohort count, event count before and after dedupe, final-state counts, breach counts, and array lengths.
8. Round only final reported values. Use exact decimal arithmetic for money and final ratios when possible.
9. Generate the final JSON programmatically from computed objects. Do not hand-copy long sorted arrays.