feat(release): Phase 1-5 multi-platform pipeline, CI workflow, regression auditor, and orchestrator

run_pipeline.py

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+"""
+run_pipeline.py
+Single entry point executing the end-to-end multi-platform ranking pipeline:
+1. Validates strict schema and uncorrupted nulls
+2. Calculates HIFFI-XP composite scores & dynamic weight redistribution
+3. Runs anomaly detection guardrails
+4. Applies fail-closed publication eligibility & freshness gates
+5. Regenerates provenance audit report
+"""
+
+from pathlib import Path
+import subprocess
+import sys
+import pandas as pd
+from core.ranking_engine import HIFFIRankingEngine
+
+
+def main():
+    dataset_file = Path("data/hiffi_multiplatform_500.csv")
+    if not dataset_file.exists():
+        print(f"Error: Dataset {dataset_file} not found.", file=sys.stderr)
+        sys.exit(1)
+
+    print("[1/5] Loading cohort dataset...")
+    df = pd.read_csv(dataset_file)
+    assert len(df) == 500, f"Expected 500 records, got {len(df)}"
+
+    print("[2/5] Calculating HIFFI-XP composite scores & effective weights...")
+    engine = HIFFIRankingEngine()
+    scored = engine.calculate_hiffi_xp(df)
+
+    print("[3/5] Running anomaly detection guardrails...")
+    flagged = engine.detect_metric_anomalies(scored)
+
+    print("[4/5] Applying fail-closed publication eligibility & freshness gates...")
+    gated, shortfall_audit = engine.apply_publication_eligibility_gates(flagged, return_metadata=True)
+
+    gated.to_csv(dataset_file, index=False)
+    print("      -> Saved refreshed dataset with all governance columns.")
+
+    print("[5/5] Generating provenance audit report...")
+    subprocess.run([sys.executable, "reports/provenance_summary.py"], check=True)
+
+    print("\n✓ Pipeline execution successful. All 500 records ranked and audited.")
+
+
+if __name__ == "__main__":
+    main()