similar-work-rank

Similar-Work Rank

Purpose

Deterministically rank the asset's prior work orders by similarity to the current work order, detect a recurring SOP-default fix, and surface a superseding-fix recommendation from a prior fix note.

When to use

• After knowledge-retrieve, in every run.
• "What's the closest prior fix?" / "have we done this before?" / "is this a repeat failure?".

Inputs

• The contract payload with issue_keywords[], required_parts[], retrieved.prior_fixes[] and history.prior_work_orders[] (contract mfg.work-order-assist.v1).

Steps

1. Validate the payload against the contract.
2. Run scripts/similar_work.py --intake wo-intake.json --out similar-work.json.
3. Do not restate or recompute any similarity score in prose; quote the engine output verbatim.
4. Emit the {similar_work} and {superseding_fix} contract payload plus the preliminary next_best_action.

Output

Contract mfg.work-order-assist.v1 with populated similar_work[] (rank, wo_id, similarity_score, shared_signals, outcome, recommended_fix, repeat_failure, confidence, source=engine:similar_work, citation), superseding_fix{}, and history.repeat_promotion — the {similar_work} hop.

Grounding requirements

Every similar match carries its CMMS record citation; a recurring-failure promotion cites references/similar-work-rules.md #3.1; a superseding fix cites its fix-note ID and #3.3.

Constraints

• All similarity math happens in the engine. The model must never re-rank prior work itself.
• No parts-readiness talk here — that is parts-readiness's job.
• Never suppress a recurring-failure or superseding-fix finding because "the SOP says to do X".

Escalation / uncertainty

• Top match below the 0.75 confidence floor, no comparable history, or a repeat failure detected: the engine adds to escalations[]; surface it and route a recurring failure to an RCA (similar-work-rules.md #4.1-#4.3).