hypothesis-ranking-pipelinelisted

# hypothesis-ranking-pipeline Goal: - Update ranking artifacts for one reviewed hypothesis using canonical placement-opponent selection, ranked-frontier selection, tournament judgments, and Elo updates. Inputs: - candidate `hypotheses/<id>/HYPOTHESIS.json` - current viable hypothesis frontier - optional `state/PROXIMITY_GRAPH.json` - current tournament artifacts - numeric ranking settings from `state/RESOLVED_RUN_CONFIG.json` Outputs: - updated `tournaments/*.json` - updated candidate and opponent `hypotheses/<id>/HYPOTHESIS.json` artifacts with Elo and match references - updated `state/PIPELINE_STATE.json` - updated `state/CURRENT_STAGE.json` Sub-skills: - `hypothesis-placement-tournament` - `hypothesis-ranked-tournament` - `ranking-elo-update` Context Loading: - Open `skills/shared-references/schema-index.md`. - Read `packages/agent_contracts/hypothesis.py` and confirm the exact `HypothesisContract` shape before writing any touched `HYPOTHESIS.json` artifact. - Read `packages/agent_contracts/ranking.py` and confirm the exact `TournamentMatchContract` plus `HypothesisMatchupContract` shapes. - Read `packages/agent_contracts/resolved_config.py` for `placement_match_count` and `tournament_top_k`. - Read `packages/agent_contracts/pipeline_runtime.py` before updating `state/PIPELINE_STATE.json` or `state/CURRENT_STAGE.json`. - If `state/PROXIMITY_GRAPH.json` exists, read `packages/agent_contracts/state.py` and load the exact `ProximityGraphContract`. - Read `packages/a