The six-stage framework

Why this exact order

• Sources before hydration: know what candidates exist before paying to enrich them
• Hydration before filtering: many filters need metadata the source did not provide
• Filtering before scoring: scoring is the expensive stage; drop the ineligible first
• Scorer chain (not single scorer): real systems compose ML scoring + diversity reranking + business rules
• Selector after scoring: keeps scoring deterministic and cacheable
• SideEffects last and async: side effects must never block the user response

Workflow when invoked

Walk the user through these eight steps:

1. Clarify the use case (one round, three questions): items being ranked? input context? language/runtime?
2. Identify the candidate sources: usually in-network (followed/owned/subscribed) + out-of-network (ML retrieval / trending / similar-to-liked)
3. List required hydrations: for each filter and scorer, what data does it need that the source did not provide?
4. List the filters: duplicate, self, age, block/mute, previously-served, eligibility. Order matters — cheap before expensive.
5. Design the scorer chain: primary (ML) → combiner (multi-action with weights) → diversity → business rules
6. Selector: sort descending by final score, take top K (or stratified mix for in-network/out-of-network)
7. SideEffects: cache served IDs, emit impression events, update counters, log analytics — all fire-and-forget
8. Generate the scaffold in the user's stack

Key trade-offs to surface (don't default silently)

1. Single score vs multi-action prediction

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quarkus-verification

为 Quarkus 项目执行发布前验证闭环，涵盖构建、测试、扫描与差异审查。

recruiting-pipeline

帮助团队跟踪候选人招聘进度，统计各阶段人数并更新招聘状态。