Update to Airside Labs Aviation Tools

{
  "name": "aviation-use-cases",
  "description": "Find and evidence AI use cases for an aviation organisation or role with the Airside Labs Aviation Tools, including the data each needs and the EASA AI-level screen, without overstating what the catalogue shows.",
  "included_files": [],
  "skill_md_contents": "---\nname: aviation-use-cases\ndescription: Find and evidence AI use cases for an aviation organisation or role with the Airside Labs Aviation Tools, including the data each needs and the EASA AI-level screen, without overstating what the catalogue shows.\n---\n\n# Aviation AI use cases: landscape, search, fetch, screen\n\nUse this workflow when someone asks what AI could do for an airline, airport, ANSP, MRO,\nregulator or a role inside one, what data it would need, or how a use case sits against\nEASA's AI framework.\n\n## Steps\n\n1. **Start with the landscape.** Call `use_case_landscape` first, grouped by `org_type`, then\n   narrow with `org_type` and `group_by=role` or `sector`. This tells you what the catalogue\n   covers and the exact spellings the filters accept. Counts describe the catalogue, not the\n   industry; never present a count as market evidence.\n2. **Search with operational nouns.** `search_use_cases` works best on the things people do and\n   the systems they touch (turnaround, stand allocation, NOTAM triage, engine health), not on\n   abstractions. Take the top handful, not the whole list.\n3. **Fetch the few records the argument needs** with `get_use_case`. Each record carries the\n   role, the organisation type, every data requirement with its update cadence, the EASA screen\n   where assessed, and nearest neighbours. Three well-chosen records beat thirty summaries.\n4. **Go sideways when asked for alternatives:** `similar_use_cases` for the neighbours of one\n   record; `data_requirements` for a role's or organisation type's aggregate data profile;\n   `trace_data_lineage` to say where a data requirement's knowledge comes from.\n5. **Screen with the framework, page-cited.** `easa_ai_framework` returns the AI-level, hazard\n   class and technique-ceiling tables from the public EASA document with page references. The\n   level and hazard on a use case are a title-level screen with a confidence, not a certification\n   finding; say so.\n6. **File what you could not find.** If the user needed something the catalogue lacks, call\n   `report_unmet_need` once with what was asked and what came back. If they have a correction or\n   an addition, `submit_suggestion`. Both are read by a person; `feedback_status` says what\n   happened later.\n\n## Output requirements\n\n- Name the organisation type and role the answer is scoped to.\n- For each use case cited, give its ID, the data it needs with cadence, and the EASA screen if\n  present, with the confidence stated.\n- Separate what the catalogue records from what you infer.\n- Carry the provenance note: the catalogue is Airside Labs' proprietary corpus for use inside\n  the user's analysis, not for redistribution as a dataset; the EASA tables are public text that\n  may be quoted with their page reference.\n\n## Do not\n\n- Quote landscape counts as adoption rates or market size.\n- Describe a use case as certified, approved or compliant on the strength of the screen.\n- Fetch dozens of full records when the question needs three.\n"
}