Individual skill

asteria-fleet-data-quality

Reconcile Asteria Fleet Data Quality Hub collections and produce strict JSON audit or certification answers for contact readiness/contact mastering, fuel purchases, freight charges, and maintenance history. Use when a task mentions Asteria Fleet Data Quality Hub, source snapshots, answer_template.json, case_scope.json, contact control codes, fuel/freight normalization, maintenance integrity, or internal decision-code panels.

experiments/codex_skill_creator_comparison_gpt5_5_xhigh/reports/skills/task_group_021/fewshot/deepagents/fewshot_attempt_01/SKILL.md

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Oct 29, 2026
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Apache-2.0

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# Asteria Fleet Data Quality

## Workflow

1. Read the task prompt, `payloads/case_scope.json`, and `payloads/answer_template.json`. Treat the template as the output contract: required keys, exact names, enum values, precision, and ordering rules override intuition.
2. Read runtime access details from `environment_access.md`. Use only the listed endpoints. Do not invent data or use prior task answers.
3. Fetch the catalog, schema, source snapshots, and all rows for the scoped `collection_id`. The REST endpoints use `collection` for collection filters, `domain` for aliases, `kind` for unit conversions, and `currency` for FX. They page with `limit` and `offset`.
4. Prefer row-level calculations over shortcut inference. Reconcile duplicates, classify exceptions, compute totals, then fill the decision-code panels from the same evidence.
5. Return only the final JSON object. Before finalizing, check every array is sorted as specified, every count is internally consistent, and every rounded number uses the contract precision.

Use `scripts/fetch_hub_bundle.py` to collect a complete local JSON bundle for a scoped collection:

```bash
python skill/scripts/fetch_hub_bundle.py \
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