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atlas-ops-json-solver

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Solve Atlas Commerce Operations workplace tasks that require using TASK_ENV_BASE_URL, /api/schema, /api/data-dictionary, /api/sql, or documented controlled correction endpoints to produce an exact answer.json matching input/payloads/answer_template.json. Use for operational analytics, settlement reconciliation, fulfillment scorecards, warehouse productivity reviews, support-health reviews, and narrowly authorized data-quality corrections against the Atlas task service.

experiments/codex_skill_creator_comparison_gpt5_5_xhigh/reports/skills/task_group_022/fewshot/opencode/fewshot_attempt_03/SKILL.md

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# Atlas Ops JSON Solver

Use this skill when a task asks for an exact JSON answer from the Atlas Commerce Operations workplace service. The common pattern is: read the local prompt, request payload, and answer template; discover the live schema; compute the requested business metrics with SQL; validate the JSON contract; write only `answer.json`.

## Workflow

1. Read the task prompt, every file under `input/payloads/`, and especially `input/payloads/answer_template.json`. Treat the template as the output contract: required keys, no extra keys, exact nesting, ordering requirements, rounding, enum values, and array uniqueness.
2. Resolve the task environment base URL from the prompt, environment variables, or any provided environment-access file. Use that file only for connection details such as base URL and auth. Do not inspect server source, evaluator code, test answers, or unrelated task material.
3. Fetch the live schema and data dictionary before writing SQL. Use the bundled client at [scripts/atlas_api.py](scripts/atlas_api.py). From the task workspace, run it with the path to this skill directory:
   - `python <skill_dir>/scripts/atlas_api.py schema`
   - `python <skill_dir>/scripts/atlas_api.py dictionary`
   - `python <skill_dir>/scripts/atlas_api.py sql path/to/query.sql`
4. Translate the request payload into a cohort query before calculating metrics. Keep the cohort in a CTE with one row per business entity in the denominator, such as order, shipment, task, case, refund, or account. Apply production flags, account/warehouse/region/campaign filters, inclusive or strict timestamp boundaries, and cutoff semantics exactly as stated.
5. Build metric CTEs from the cohort. Prefer explicit `COUNT(DISTINCT ...)`, `EXISTS`/`NOT EXISTS`, window functions, and grouped rollups over manual sampling. Avoid row multiplication when joining one-to-many tables such as shipments, scans, refunds, reversals, events, tasks, or cases.
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