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atlas-commerce-ops-solver

Solve Atlas Commerce Operations benchmark tasks that provide a prompt, request payload JSON, answer_template.json, and authenticated task-environment API. Use for SQL-based operational analytics, cutoff/as-of metrics, strict answer.json generation, and approved canonical corrections with correction_audit verification.

experiments/codex_skill_creator_comparison_gpt5_5_xhigh/reports/skills/task_group_022/fewshot/codex/fewshot_attempt_01/SKILL.md

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# Atlas Commerce Ops Solver

## Core Workflow

1. Read the task prompt, every request payload under `input/payloads/`, and `answer_template.json` before querying data. Treat the request payload as the business contract and the template as the output contract.
2. Load the live schema and data dictionary from the task environment. Use [references/api.md](references/api.md) for the endpoint contract and `scripts/atlas_api.py` for repeatable calls.
3. Build the answer from SQL, not from denormalized intuition. Start with a cohort CTE, add one CTE per metric, then run independent count checks for every requested denominator, numerator, and sorted list.
4. Use [references/metric-patterns.md](references/metric-patterns.md) when the task involves effective source rows, cutoff state, refunds, fulfillment, carrier corrections, warehouse productivity, support health, or inventory movements.
5. Write exactly one JSON object to `answer.json`. Match required keys, nesting, array lengths, sorting, number precision, enum values, and `additionalProperties` restrictions from the template. Do not include commentary outside the JSON document.

## Query Discipline

- Preserve UTC text timestamps and compare them lexicographically only when they are all ISO-8601 UTC strings. Apply inclusive and exclusive boundaries exactly as stated.
- Distinguish current snapshot columns from append-only event/scans tables. When the request says `effective`, `as of cutoff`, or similar, derive state from the relevant source event stream at or before the cutoff.