Individual skill

atlas-commerce-ops-analysis

Use for Atlas Commerce Operations tasks that require producing exact JSON analytics or applying approved minimal data-quality corrections from workplace schema, dictionary, SQL data, and request payloads.

experiments/codex_gpt5_5_xhigh/reports/skills/task_group_022/reflect-3/reflect-3_attempt_02/SKILL.md

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Oct 29, 2026
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Apache-2.0

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# Atlas Commerce Ops Analysis

## Core Workflow

1. Read the user prompt, request payload, and answer schema before querying data.
2. Treat the answer schema as the contract. Return exactly the required JSON keys, types, rounding, array ordering, and no commentary.
3. Load schema and field definitions from the available workplace context, then identify the smallest table set needed for the request.
4. Build the cohort first. Apply business scope exactly: account production exclusions, segment/tier/region filters, warehouse/campaign/task/case windows, and inclusive or strict timestamp boundaries as stated.
5. Prefer canonical and event-derived operational truth over convenience snapshot columns when the request says cutoff, effective, final, current-at-cutoff, or corrected. Snapshot status fields can lag event history.
6. For imported source rows with `source_system`, `external_event_id`, and `ingested_at`, de-duplicate before analysis: keep the row with the latest `ingested_at`, breaking ties by the stable row id.
7. For final state at a cutoff, use effective rows with business event time at or before the cutoff, then order by event time and stable row id. Use the last state-changing row unless the request defines a different effective event.
8. If the SQL service rejects a large CTE, partitioned window, or complex join, query scoped rows in pages and aggregate locally. Always check truncation or page until complete.
9. Round only final reported values. Keep intermediate rates, currency amounts, and durations unrounded for ranking and status rules.