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asteria-fleet-dq

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Solve Asteria Fleet Data Quality Hub reconciliation and certification tasks for contacts, roster readiness, fuel purchases, freight charges, and maintenance logs. Use when Codex must read environment_access.md, payloads/case_scope.json, and payloads/answer_template.json, query the Asteria hub endpoints, reconcile overlapping snapshots and duplicates, normalize aliases, units, and FX, infer compact Asteria control codes, and return a strict JSON answer.

experiments/codex_skill_creator_comparison_gpt5_5_xhigh/reports/skills/task_group_021/fewshot/codex/fewshot_attempt_03/SKILL.md

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Oct 29, 2026
License
Apache-2.0

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# Asteria Fleet Data Quality

## Core Workflow

1. Read the task prompt, `payloads/case_scope.json`, `payloads/answer_template.json`, and `environment_access.md`. Treat the template as the output contract: no extra keys, no commentary, and all arrays sorted exactly as specified.
2. Pull live hub data instead of guessing from IDs. Use the REST endpoints listed in `environment_access.md`; if a read-only `/api/query` credential is available, SQL is fine, but do not depend on it.
3. For large collections, write a task-local analysis script and compute every count, ID set, ranking, and total from fetched JSON. Avoid manual tallying.
4. Apply the domain rules in [reconciliation_rules.md](references/reconciliation_rules.md) for snapshot retention, alias matching, contact canonicalization, maintenance history reconstruction, compact codes, and status decisions.
5. Emit exactly one JSON object. Validate it with `python -m json.tool` and, when the template is JSON Schema and `jsonschema` is available, validate against the template too.

## Fetch Helper

Use [scripts/fetch_hub.py](scripts/fetch_hub.py) to dump paged hub responses into a local working directory:
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