Tune the scorer

Done when: the borderline band has been labelled, any targeting change is saved, and the rescoring run reports finished: true.

Scores are only as good as the targeting and the counter-examples behind them. This loop is how a workspace teaches the scorer. Needs write; the rescore spends credits.

1. Pull the borderline bandCopy Anchor Link

rivomi_search_prospects { list_id, min_score: 55, max_score: 75, limit: 25 }

These are the people the scorer was least sure about, so a label here teaches it the most.

2. Label them with the userCopy Anchor Link

Show each person's title, company, signal and score reason. For each verdict:

rivomi_update_prospect { prospect_id, fit: "good" }
rivomi_update_prospect { prospect_id, fit: "not", notes: "Agency, resells rather than buys" }

fit: "not" suppresses the person and records them as a counter-example that every later scoring run sees. It is the main teaching signal, and it keeps the person in the workspace. fit: "none" undoes a label.

3. Tighten the targetingCopy Anchor Link

When the labels show a pattern (the wrong seniority, an industry to exclude), change the agent:

rivomi_get_agent { agent_id } → targeting
rivomi_update_agent_targeting { agent_id, exclude_keywords: [ …current…, "agency" ] }

Each array you send replaces the whole array. Read the current values first and send the full list you want to end up with.

4. RescoreCopy Anchor Link

rivomi_process_list { list_id, stages: ["score"], rescore: true } → run_id

Poll rivomi_get_run until finished, then rerun step 1 and report how the band moved.