{"id":77011,"plugin_id":"plugins_6ab41ed5b8c4819199eb5bbf91ce25f5","kind":"skill","collection_source":"plugin_package","comparison_source":null,"observed_at":"2026-11-03T00:12:18.756Z","digest":"23e25df2790664aa51316d33a363ec2972c743d714804bb4b1c95c39196a7d82","against":null,"payload":{"description":"Improve SQL, Spark, ETL/ELT, CDC and pipeline implementation code while preserving data grain, keys, correctness and replay safety.","included_files":[],"name":"implement-data-code","skill_md_contents":"---\nname: implement-data-code\ndescription: Improve SQL, Spark, ETL/ELT, CDC and pipeline implementation code while preserving data grain, keys, correctness and replay safety.\n---\n\n# Implement Data Code\n\nUse this skill only for its stated purpose. Follow the shared source-of-truth, permission and evidence rules in `../../references/workflow-contract.md`. Read `../../references/data-code-contracts.md` for detailed method when necessary.\n\n## Procedure\n1. Map source/output grain, keys, joins, null handling, ordering, schema and downstream consumers.\n2. Check duplicates, CDC merges, retry/idempotence, replay windows and late/deleted events.\n3. Implement minimal transformation or pipeline fix; avoid DISTINCT as a band-aid.\n4. Add representative quality and reconciliation tests including repeat/replay cases.\n5. Profile for tuning only after outputs are proven correct.\nFor full data-platform incident programs, advise appropriate specialized scope rather than overstating this coding skill.\n\n## Handoff rule\nState actual artifacts or grounded code findings and validation status. Never invent file paths, executed tests, tool access, or completed subagent work. Keep output proportionate to the user request.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}