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package for sports roster-impact analysis built on sports-impact-core.","maintainers":[{"name":"jayscambler","email":"jay@greyhaven.ai"}],"readme":"# pi-autocontext-sports\n\nPi package for sports roster-impact analysis using `sports-impact-core`.\n\nInstall from npm:\n\n```bash\npi install npm:pi-autocontext-sports@0.79.0\n```\n\nFor a project-local install:\n\n```bash\npi install npm:pi-autocontext-sports@0.79.0 -l\n```\n\nIt adds:\n\n- a consolidated `sports_impact` tool\n- CSV/JSON basketball baseline import, including user-provided approved WNBA roster/minutes exports via `league: \"WNBA\"`\n- deterministic run bundle summaries via `summarize_run`\n- manifest-driven scenario bundles via `validate_scenario_bundle` JSON/Markdown reports, `run_scenario_bundle` with optional `validateBeforeRun` and execution manifests, `gate_scenario_bundle_execution`, and bundle comparison via `compare_scenario_bundles`\n- dataset-derived injury and trade scenario creation\n- deterministic front-office trade-feasibility guardrails for salary matching, luxury-tax/apron thresholds, hard caps, aggregation restrictions, roster limits, and optional required salary/payroll/apron source-license provenance\n- BALLDONTLIE-primary internal-demo trade scenario search with objective presets, roster-building constraints, deterministic feasibility/simulation, ranked JSON/Markdown, and runnable top-candidate artifacts\n- fixture-backed draft candidate fit evaluation plus sensitivity/robustness analysis, model-vs-scout reconciliation, availability/contingency planning, live war-room state updates, manifest-driven full draft workflow runs, combined draft workflow smoke evidence, draft workflow artifact gating, and front-office decision briefs with board tiers, archetype picks, risk register, opportunity costs, analyst appendix, and next-review questions\n- shared availability constraints and coaches lineup matchup ranking via `rank_coaches_lineup_matchups`\n- historical absence/outcome fixture generation for injury backtests\n- historical trade/outcome fixture generation for trade backtests\n- multi-suite injury and trade calibration reporting\n- deterministic trade model parameter tuning and profile comparison\n- trade fixture quality validation, case-level provenance, historical fixture-pack audits, input-hash evidence, reproducibility and deterministic rerun verification with drift grouping, configurable regression gates, maturity-aware benchmarks, benchmark comparisons, train/validation/test split generation, readiness gates, holdout scoring, unified audits, and residual diagnostics\n- precomputed NBA game-footage tracking analysis for player/ball tracks, quality audit warnings, possession segments/estimates, and court-zone summaries without raw video inference\n- deterministic external handoff validation reports for schema/version, metadata, source-system provenance, bbox format, pixel-scale, raw-video boundary, coordinate completeness, detection completeness, and confidence checks\n- deterministic external handoff normalization into `game-footage-analysis-input/v1`, including direct `court_feet` coordinates and explicit-scale pixel/bbox-center conversion\n- reusable invalid external handoff fixture gates that require stable failed check IDs and normalization rejection before downstream analysis\n- downstream external handoff fixture-gate CI adoption plans, adoption verification, and adoption registries that generate/verify deterministic GitHub Actions YAML for both valid fixture-pack gates and invalid-fixture gates across projects\n- deterministic SVG/Markdown court visualizations for selected precomputed frames and track paths\n- precomputed Roboflow/YOLO-style, CSV, ByteTrack/Supervision-style detection/tracking, and valid external handoff normalization for game-footage analysis inputs\n- game-footage fixture-pack audit/rerun verification with adapter source files including external handoffs, input hashes, validation evidence, normalized-output comparisons, visualization artifact hashes, lifecycle policy scaffolds/conformance checks/run audits/CI adoption generators/adoption verifiers/trend reports/CI-hardened trend gates/trend registries/dashboard aggregation/dashboard adoption checks/dashboard adoption registries, materialized, verified, compared, gated, baseline-checked, promotion-evidenced, promotion-verified, suite-checked, ledger-verified, registry-verified, registry-gated, registry-compared, and registry-comparison-gated SVG/Markdown artifact bundles, and golden quality/possession checks\n- deterministic injury/trade simulation and fixture-backed injury/trade backtest actions\n- a sports-impact analysis skill\n- an NBA roster-impact prompt template\n\nThe package is designed to assist analysis runs while keeping numeric projections and salary/apron feasibility checks deterministic and inspectable. Feasibility inputs can require source/license provenance for salary, payroll, apron-threshold, and rule-profile values, either embedded in the JSON input or ingested from CSV/JSON fixture files. Feasibility reports are preflights, not legal advice or authoritative CBA rulings.\n\n## Boundaries and external handoff\n\nFollow the package-local boundary document [`BOUNDARIES.md`](BOUNDARIES.md), the machine-readable handoff schema [`schemas/game-footage-external-handoff.schema.json`](schemas/game-footage-external-handoff.schema.json), and the canonical repository boundary contract in `docs/BOUNDARIES.md`. Game-footage tools accept precomputed court-coordinate tracking/detection outputs only. Use `validate_game_footage_external_handoff` to report contract readiness, source-system provenance, bbox format, pixel-scale, and raw-video boundary alignment, `normalize_external_handoff_game_footage` to bridge valid handoffs into the existing analysis input schema before analysis, `audit_game_footage_fixture_pack` with `normalization.adapter: \"external_handoff\"` plus `files.externalHandoffInput` when that bridge must be audited as fixture-pack evidence, `gate_game_footage_external_handoff_fixture_pack` when CI needs a hard pass/fail gate for validation, normalized-input matching, and raw-video boundary checks, and `gate_game_footage_external_handoff_invalid_fixtures` when CI needs manifest-backed negative validation coverage with stable failed check IDs. The package does not perform raw video inference, object detection, segmentation, homography estimation, or jersey OCR, and it does not provide betting/gambling recommendations, medical advice, legal advice, authoritative CBA rulings, or guaranteed-performance claims.\n\nAfter install, ask Pi to use `sports_impact` to initialize a run, import NBA/WNBA baseline data with `import_baseline_dataset` (`league: \"WNBA\"` requires user-provided approved WNBA roster/minutes paths and source metadata), summarize run artifacts with `summarize_run`, validate manifest paths with `validate_scenario_bundle` (set `reportPath` for a Markdown report), run manifest-driven trade/draft/lineup bundles with `run_scenario_bundle` (set `validateBeforeRun: true` to write the validation report before any failing bundle steps execute; successful runs return `executionManifestPath`), gate execution manifests with `gate_scenario_bundle_execution` (`scenarioBundleExpectedSteps` supports `pass`, `fail`, or `skipped`), compare bundle manifests with `compare_scenario_bundles`, create `create_injury_scenario` / `create_trade_scenario` artifacts, verify explicit front-office salary/apron inputs and optional strict financial provenance with `verify_trade_feasibility`, rank BALLDONTLIE demo trade candidates with `optimize_balldontlie_demo_trades` from a generated demo pack, rank coaches lineup matchup candidates with `rank_coaches_lineup_matchups`, evaluate source-agnostic draft candidate fit fixtures with `evaluate_draft_candidate_fit`, test draft board robustness with `analyze_draft_board_sensitivity`, reconcile model and scout boards with `reconcile_draft_board`, plan pick-slot availability/contingencies with `plan_draft_availability`, update draft-night board state with `update_draft_war_room_state`, run manifest-driven full workflows with `run_draft_workflow`, run combined draft workflow smoke evidence with `run_draft_workflow_smoke`, gate draft workflow artifacts with `gate_draft_workflow`, build a GM/data-analyst decision-room artifact with `build_draft_decision_brief`, tune trade model parameters with `calibrate_trade_model`, validate fixtures with `validate_fixtures`, audit historical fixture packs with `audit_trade_fixture_pack`, benchmark fixture packs with `benchmark_trade_fixture_pack`, inspect input hashes, verify benchmark reproducibility with `verify_trade_fixture_pack_benchmark`, rerun deterministic benchmark verification by adding `rerunBenchmark: true`, compare benchmark artifacts with `compare_trade_fixture_pack_benchmarks`, normalize precomputed detections with `normalize_roboflow_game_footage`, `normalize_csv_game_footage`, or `normalize_tracking_game_footage`, and validate external handoff contracts with `validate_game_footage_external_handoff`.\n\nExample draft fit action parameters:\n\n```json\n{\n  \"action\": \"evaluate_draft_candidate_fit\",\n  \"runDir\": \"runs/draft-fit-demo\",\n  \"draftProspectsPath\": \"/tmp/sports-impact-draft/prospects.json\",\n  \"draftTeamNeedsPath\": \"/tmp/sports-impact-draft/team-needs.json\",\n  \"draftTargetTeam\": \"LAL\",\n  \"draftTopCandidates\": 10\n}\n```\n\nThis writes `draft/draft-candidate-fit.json` and `.md` under the run directory with deterministic fit evidence only; it is not a draft recommendation. Then test whether the board is robust under alternate assumptions:\n\n```json\n{\n  \"action\": \"analyze_draft_board_sensitivity\",\n  \"runDir\": \"runs/draft-fit-demo\",\n  \"draftFitReportPath\": \"runs/draft-fit-demo/draft/draft-candidate-fit.json\",\n  \"draftBoardSensitivityTopCandidates\": 10\n}\n```\n\nThe sensitivity report writes `draft/draft-board-sensitivity.json` and `.md` with default, win-now, upside, low-risk, minutes-pathway, skill-heavy, and source-confidence-penalty boards plus consensus/volatile/mover groups. When scout-board JSON/CSV is available, reconcile model and scout views:\n\n```json\n{\n  \"action\": \"reconcile_draft_board\",\n  \"runDir\": \"runs/draft-fit-demo\",\n  \"draftFitReportPath\": \"runs/draft-fit-demo/draft/draft-candidate-fit.json\",\n  \"draftBoardSensitivityPath\": \"runs/draft-fit-demo/draft/draft-board-sensitivity.json\",\n  \"draftScoutBoardPath\": \"/tmp/sports-impact-draft/scout-board.json\"\n}\n```\n\nThe reconciliation report writes `draft/draft-board-reconciliation.json` and `.md` with model/scout consensus, model-high/scouts-low, scouts-high/model-low, high-upside disagreement, source/data-gap disagreement, film-review, provenance-blocker, and meeting-agenda evidence. When market/mock-board JSON/CSV and pick slots are available, plan realistic paths through the board:\n\n```json\n{\n  \"action\": \"plan_draft_availability\",\n  \"runDir\": \"runs/draft-fit-demo\",\n  \"draftFitReportPath\": \"runs/draft-fit-demo/draft/draft-candidate-fit.json\",\n  \"draftBoardSensitivityPath\": \"runs/draft-fit-demo/draft/draft-board-sensitivity.json\",\n  \"draftReconciliationPath\": \"runs/draft-fit-demo/draft/draft-board-reconciliation.json\",\n  \"draftMarketBoardPath\": \"/tmp/sports-impact-draft/market-board.json\",\n  \"draftPickSlots\": [17, 47]\n}\n```\n\nThe availability plan writes `draft/draft-availability-plan.json` and `.md` with likely/maybe/unlikely availability, reach/value flags, contingency paths, and trade-up/down evidence only. During draft-night updates, remove selected players and refresh the live state:\n\n```json\n{\n  \"action\": \"update_draft_war_room_state\",\n  \"runDir\": \"runs/draft-fit-demo\",\n  \"draftAvailabilityPath\": \"runs/draft-fit-demo/draft/draft-availability-plan.json\",\n  \"draftSelectedPicksPath\": \"/tmp/sports-impact-draft/selected-picks.json\",\n  \"draftCurrentPick\": 14,\n  \"draftPickSlots\": [17, 47]\n}\n```\n\nThe war-room state writes `draft/draft-war-room-state.json` and `.md` with unavailable candidates, remaining board, pick-slot target stacks, contingency changes, active/archived trade evidence, decision pressure, and a clock-ready briefing. To run the whole workflow from a manifest of actual/demo fixture paths, use:\n\n```json\n{\n  \"action\": \"run_draft_workflow\",\n  \"runDir\": \"runs/draft-workflow-demo\",\n  \"draftWorkflowManifestPath\": \"runs/draft-workflow-demo/draft-workflow-manifest.json\",\n  \"gateDraftWorkflow\": true\n}\n```\n\nThe manifest schema is `sports-impact-core/draft-workflow-manifest/v1` and includes `targetTeam`, `currentPick`, `pickSlots`, and input paths for `prospects`, `teamNeeds`, `scoutBoard`, `marketBoard`, and `selectedPicks`. The action writes all stage artifacts, `draft/draft-workflow-run.json`/`.md`, and optional `draft-workflow-gate.json`/`.md`; outputs remain evidence, not draft recommendations. For CI/demo evidence that the whole draft package still fits together, run the synthetic smoke workflow:\n\n```json\n{\n  \"action\": \"run_draft_workflow_smoke\",\n  \"runDir\": \"runs/draft-workflow-smoke-demo\",\n  \"draftCurrentPick\": 14,\n  \"draftPickSlots\": [17, 47]\n}\n```\n\nThe smoke action generates deterministic draft fixtures, runs fit → sensitivity → reconciliation → availability → war-room → brief, and writes `draft/draft-workflow-ci-smoke-summary.json` plus `.md` with stage status, output paths, and hashes. To gate real/demo workflow artifacts, validate the emitted files:\n\n```json\n{\n  \"action\": \"gate_draft_workflow\",\n  \"runDir\": \"runs/draft-workflow-smoke-demo\",\n  \"draftFitReportPath\": \"runs/draft-workflow-smoke-demo/draft-workflow-smoke/draft/draft-candidate-fit.json\",\n  \"draftBoardSensitivityPath\": \"runs/draft-workflow-smoke-demo/draft-workflow-smoke/draft/draft-board-sensitivity.json\",\n  \"draftReconciliationPath\": \"runs/draft-workflow-smoke-demo/draft-workflow-smoke/draft/draft-board-reconciliation.json\",\n  \"draftAvailabilityPath\": \"runs/draft-workflow-smoke-demo/draft-workflow-smoke/draft/draft-availability-plan.json\",\n  \"draftWarRoomStatePath\": \"runs/draft-workflow-smoke-demo/draft-workflow-smoke/draft/draft-war-room-state.json\",\n  \"draftDecisionBriefPath\": \"runs/draft-workflow-smoke-demo/draft-workflow-smoke/draft/front-office-draft-decision-brief.json\"\n}\n```\n\nThe gate checks schemas, linked source paths/hashes, candidate/team consistency, Markdown caveats, and not-a-draft-recommendation language. Then convert the fit evidence into a decision-room brief:\n\n```json\n{\n  \"action\": \"build_draft_decision_brief\",\n  \"runDir\": \"runs/draft-fit-demo\",\n  \"draftFitReportPath\": \"runs/draft-fit-demo/draft/draft-candidate-fit.json\",\n  \"draftDecisionBriefTopCandidates\": 10\n}\n```\n\nThe brief writes `draft/front-office-draft-decision-brief.json` and `.md` with tiers, archetype picks, risk register, opportunity costs, analyst appendix, and next-review questions; it is still not a draft recommendation.\n\nExample optimizer action parameters:\n\n```json\n{\n  \"action\": \"optimize_balldontlie_demo_trades\",\n  \"runDir\": \"runs/lal-balldontlie-search\",\n  \"balldontliePackDir\": \"/tmp/sports-impact-balldontlie-nba-demo-pack\",\n  \"targetTeam\": \"LAL\",\n  \"partnerTeams\": [\"BKN\", \"ATL\", \"DAL\"],\n  \"objectivePreset\": \"production_preserving\",\n  \"maxOutgoing\": 8,\n  \"maxIncomingPerTeam\": 8,\n  \"tradeFamilies\": [\"1-for-1\", \"1-for-2\", \"2-for-1\", \"2-for-2\"],\n  \"maxCandidateCount\": 500,\n  \"topCandidates\": 10,\n  \"protectTopNMinutes\": 3,\n  \"maxOutgoingSalaryUsd\": 20000000,\n  \"minFantasyDelta\": -12,\n  \"excludePlayers\": [\"LeBron James\", \"Luka Doncic\"]\n}\n```\n\nUse this as internal demo/source-fit scenario search only: BALLDONTLIE is third-party aggregated best-effort data, derived payroll is known-contract salary only, and output rankings are deterministic objective scores rather than official recommendations. If `tradeFamilies` is omitted, the optimizer preserves the original `1-for-1` search behavior; add `1-for-2`, `2-for-1`, or `2-for-2` explicitly for aggregate-salary multi-player exploration.\n\nExample explicit multi-team impact action parameters:\n\n```json\n{\n  \"action\": \"evaluate_balldontlie_multi_team_demo_trade\",\n  \"runDir\": \"runs/lal-bkn-atl-balldontlie-trade\",\n  \"balldontliePackDir\": \"/tmp/sports-impact-balldontlie-nba-demo-pack\",\n  \"multiTeamTradeLegs\": [\n    { \"fromTeam\": \"LAL\", \"toTeam\": \"BKN\", \"player\": \"Rui Hachimura\" },\n    { \"fromTeam\": \"BKN\", \"toTeam\": \"ATL\", \"player\": \"Day'Ron Sharpe\" },\n    { \"fromTeam\": \"ATL\", \"toTeam\": \"LAL\", \"player\": \"ATL Player\" }\n  ]\n}\n```\n\nUse this only for explicit supplied legs; it evaluates per-team salary/apron feasibility and target-team-perspective impact for every participating team, not a legal/CBA ruling or transaction recommendation.\n\nExample generated three-team search action parameters:\n\n```json\n{\n  \"action\": \"optimize_balldontlie_three_team_demo_trades\",\n  \"runDir\": \"runs/lal-three-team-balldontlie-search\",\n  \"balldontliePackDir\": \"/tmp/sports-impact-balldontlie-nba-demo-pack\",\n  \"targetTeam\": \"LAL\",\n  \"partnerTeams\": [\"BKN\", \"ATL\", \"DAL\"],\n  \"maxPlayersPerTeam\": 4,\n  \"maxCandidateCount\": 250,\n  \"topCandidates\": 10,\n  \"objectivePreset\": \"balanced_multi_team\",\n  \"threeTeamTradeFamilies\": [\"1-for-1-for-1\", \"2-for-1-for-1\"],\n  \"protectTopNMinutes\": 3,\n  \"maxTargetOutgoingSalaryUsd\": 30000000,\n  \"minTargetFantasyDelta\": -15,\n  \"minAllTeamFantasyDelta\": 0,\n  \"minTeamFantasyDelta\": -8,\n  \"maxTeamNetSalaryIncreaseUsd\": 5000000,\n  \"maxTeamIncomingSalaryUsd\": 30000000,\n  \"requireNoTeamFantasyLossBelow\": -12,\n  \"requireAllTeamsFeasible\": true,\n  \"dedupeByTargetSwap\": true,\n  \"maxCandidatesPerTargetOutgoingPlayer\": 3,\n  \"maxCandidatesPerTargetIncomingPlayer\": 3,\n  \"excludePlayers\": [\"LeBron James\", \"Luka Doncic\"],\n  \"generateCandidateComparison\": true,\n  \"candidateComparisonRetainedLimit\": 5,\n  \"candidateComparisonAlternativesPerCandidate\": 3,\n  \"targetSalaryReliefWeight\": 1,\n  \"targetFantasyDeltaWeight\": 1,\n  \"targetNetRatingWeight\": 10,\n  \"allTeamFantasyDeltaWeight\": 0.25\n}\n```\n\nUse this as generated scenario search only; it enumerates bounded one- or multi-player three-team cycles, applies explicit player/protection/salary/impact guardrails (including top-minute protection across every participating team's outgoing pool), applies partner-team fairness constraints and candidate diversity controls to reduce one-sided or near-duplicate target swaps, writes guardrail audit artifacts with protected-player/exclusion/fairness/filter counts plus verification status, evaluates every participating team, emits source-fit confidence, per-top-candidate explanation artifacts with filtered alternatives grouped by failure reason plus closest guardrail-boundary details, and `filtered-candidates/` near-miss artifacts grouped by failed guardrail, then ranks by transparent objective weights rather than making recommendations. Set `generateCandidateComparison: true` to also emit retained-vs-filtered `candidate-comparison.json` / `.md` artifacts during optimization without changing ranking. Use `inspect_balldontlie_three_team_filtered_candidates` with `inputPath` or `filteredCandidateIndexPath` plus optional `filteredCandidateReason`, `filteredCandidateTeam`, and `filteredCandidateLimit` to summarize top failure reasons, closest near misses, and by-team guardrail gaps. Use `compare_balldontlie_three_team_candidates` with `inputPath`, optional `filteredCandidateReason`/`filteredCandidateTeam`, `candidateComparisonRetainedLimit`, and `candidateComparisonAlternativesPerCandidate` to pair retained top candidates with nearest filtered alternatives by target-swap similarity and guardrail gap when a standalone comparison step is preferred. Use `gate_balldontlie_three_team_candidate_comparison` with `candidateComparisonPath`/`candidateComparisonReportPath` to fail on comparison schema/content drift. Preset defaults are intentionally opinionated but overridable: `balanced_multi_team` adds an aggregate all-team fantasy floor, `apron_escape` adds a target salary-relief floor, and `star_protected` adds target-swap diversity caps. In CI or Pi, gate emitted audit artifacts with `gate_balldontlie_three_team_guardrail_audit` or `npm run gate:balldontlie-three-team-guardrail-audit -- --audit <run>/balldontlie-three-team-trade-search/three-team-guardrail-audit.json --out <gate.json> --report <gate.md>` to fail when returned-candidate verification is not clean or required audit count fields are missing. Use Pi action `gate_balldontlie_three_team_source_fit_explanations` with `inputPath`/`sourceFitSummaryPath` and optional `minimumConfidence`, or `npm run gate:balldontlie-three-team-source-fit-explanations -- --summary <run>/balldontlie-three-team-trade-search/summary.json --out <source-fit-gate.json> --report <source-fit-gate.md>`, to fail when retained candidates lose `sourceFit` fields, explanation artifacts drift, or filtered-candidate near-miss artifacts disappear/schema-drift. The workspace CI/release-preflight smoke uses `npm run gate:balldontlie-three-team-guardrail-audit:smoke` to generate one combined synthetic guardrail + source-fit + candidate-comparison gate evidence summary/report plus the underlying gate, comparison, and filtered-near-miss artifacts.\n\nContinue with sports-impact analysis by asking Pi to normalize valid external handoffs with `normalize_external_handoff_game_footage`, gate external handoff fixture-pack CI evidence with `gate_game_footage_external_handoff_fixture_pack`, gate invalid external handoff manifests with `gate_game_footage_external_handoff_invalid_fixtures`, generate downstream external handoff gate CI YAML with `generate_game_footage_external_handoff_gate_ci`, verify adopted external handoff gate CI YAML with `verify_game_footage_external_handoff_gate_ci`, verify downstream adoption registries with `verify_game_footage_external_handoff_gate_ci_registry`, gate downstream adoption registry policy/drift with `gate_game_footage_external_handoff_gate_ci_registry`, generate registry-gate CI YAML with `generate_game_footage_external_handoff_gate_ci_registry_gate`, verify adopted registry-gate CI YAML with `verify_game_footage_external_handoff_gate_ci_registry_gate`, verify registry-gate CI adoption registries with `verify_game_footage_external_handoff_gate_ci_registry_gate_registry`, gate registry-gate CI adoption registry policy/drift with `gate_game_footage_external_handoff_gate_ci_registry_gate_registry`, generate/verify downstream CI YAML for that verifier+gate pair with `generate_game_footage_external_handoff_gate_ci_registry_gate_registry_gate` / `verify_game_footage_external_handoff_gate_ci_registry_gate_registry_gate`, verify adoption registries for that generated workflow with `verify_game_footage_external_handoff_gate_ci_registry_gate_registry_gate_registry`, analyze precomputed tracking outputs with `analyze_game_footage`, render deterministic court SVGs with `render_game_footage_court`, materialize fixture-pack visualization artifacts with `materialize_game_footage_visualizations`, verify materialized visualization bundles with `verify_game_footage_visualization_bundle`, compare visualization bundles with `compare_game_footage_visualization_bundles`, gate visualization bundle drift with `gate_game_footage_visualization_bundle_drift`, check committed visualization baselines with `check_game_footage_visualization_baseline`, create promotion evidence with `promote_game_footage_visualization_baseline`, verify promotion evidence with `verify_game_footage_visualization_baseline_promotion`, check baseline suites with `check_game_footage_visualization_baseline_suite`, append and verify promotion ledgers with `append_game_footage_visualization_baseline_promotion_ledger_entry` / `verify_game_footage_visualization_baseline_promotion_ledger`, verify promotion-ledger registries with `verify_game_footage_visualization_baseline_promotion_ledger_registry`, gate promotion-ledger registry policy with `gate_game_footage_visualization_baseline_promotion_ledger_registry`, compare promotion-ledger registries over time with `compare_game_footage_visualization_baseline_promotion_ledger_registries`, gate registry-comparison policy with `gate_game_footage_visualization_baseline_promotion_ledger_registry_comparison`, scaffold lifecycle policy presets with `scaffold_game_footage_visualization_baseline_lifecycle`, verify lifecycle conformance with `verify_game_footage_visualization_baseline_lifecycle`, audit lifecycle run directories with `audit_game_footage_visualization_baseline_lifecycle`, generate lifecycle CI adoption YAML with `generate_game_footage_visualization_baseline_lifecycle_ci`, verify adopted lifecycle CI with `verify_game_footage_visualization_baseline_lifecycle_ci`, compare lifecycle audit trends with `compare_game_footage_visualization_baseline_lifecycle_runs`, gate lifecycle trend policy with `gate_game_footage_visualization_baseline_lifecycle_trend`, verify/gate reusable lifecycle trend registries with `verify_game_footage_visualization_baseline_lifecycle_trend_registry` / `gate_game_footage_visualization_baseline_lifecycle_trend_registry`, compare registry history with `compare_game_footage_visualization_baseline_lifecycle_trend_registries`, gate registry deltas with `gate_game_footage_visualization_baseline_lifecycle_trend_registry_comparison` using scaffolded policy files, aggregate registry/gate history into a dashboard with `create_game_footage_visualization_baseline_lifecycle_dashboard`, verify dashboard adoption with `verify_game_footage_visualization_baseline_lifecycle_dashboard_adoption`, verify/gate/compare dashboard adoption registries with `verify_game_footage_visualization_baseline_lifecycle_dashboard_adoption_registry`, `gate_game_footage_visualization_baseline_lifecycle_dashboard_adoption_registry`, `compare_game_footage_visualization_baseline_lifecycle_dashboard_adoption_registries`, and `gate_game_footage_visualization_baseline_lifecycle_dashboard_adoption_registry_comparison`, audit game-footage fixture packs with `audit_game_footage_fixture_pack` (including external handoff validation/normalization evidence and optional visualization artifact hashes), verify deterministic game-footage fixture-pack reruns with `verify_game_footage_fixture_pack_rerun`, split trade backtests with `split_trade_backtests`, evaluate readiness gates with `evaluate_trade_model_readiness`, score reserved holdouts with `evaluate_trade_holdout`, run unified audits with `audit_trade_fixtures`, diagnose residuals with `diagnose_trade_model`, or execute the fixture-backed workflows in `examples/nba-injury/`, `examples/nba-trade/`, `examples/nba-trade-calibration/`, `examples/nba-trade-fixture-quality/`, `examples/nba-trade-historical-template/`, and `examples/nba-game-footage/`. See `docs/PI_INSTALL.md` in the repository for a full prompt and guardrails.\n","readmeFilename":"README.md"}