FuelAI Curation Loops

TL;DR

FuelAI curation loops는 raw item 수집, 관련성 판정, stack_slug 배정, report 품질을 반복 평가·수정하는 운영 루프다.

FuelAI 로컬 필터와 curate prompt는 coding agent, agentic coding, workflow, Codex, AI agent workflow를 relevant 신호로 보고, 실제 구축/사용 절차를 tutorial로 분류한다 [4].
이번 영상은 Codex /goal, Codex automations, AI coding agent loop, concrete operations workflow를 다루므로 FuelAI 큐레이션 대상에 해당한다 [2] [4].
Loop Library의 full product evaluation loop는 N개 realistic scenario와 success criteria, evidence 기록, 재실행 조건을 요구하므로 FuelAI 큐레이션 품질 평가 세트로 변형하기 좋다 [3].
Loop Library의 production data cleanup loop는 policy나 taxonomy에 맞지 않는 production records를 제거하고 classification logic을 고친 뒤 검증하는 구조라서 FuelAI raw/curated item 정합성 점검에 맞는다 [3] [4].

The first FuelAI loop candidates are curation quality evaluation, production data cleanup, pipeline error sweep, and docs/wiki sweep because each can be tied to existing artifacts such as raw items, curated entries, reports, journals, and wiki outputs [8] [9] [13].
FuelAI should treat no-op findings as valid outputs for scheduled loops, because recurring operations should report that nothing changed instead of manufacturing diffs [8] [9].
E2E tests and trace artifacts can serve as both sensors and executable specs, making them a better verifier for repair loops than another unconstrained LLM judgment [12].