Imported from kathir-iTech/ScamShield (docs/archive/AGENTS.md). Install upstream with npx skills add kathir-iTech/ScamShield --skill archive. Copyright stays with the author.

ScamShield — Agent State

Active Summary

Phase 1.5 (FPR Regression Fix) COMPLETE (2026-08-30). FPR reduced from 48% to 14%. Recall 77%. Both targets met.

Current Best Model

• Backend model: TF-IDF + LogisticRegression, trained on v2 gamma dataset (2,531 samples)
• Performance: Test Acc=0.9513, F1=0.9622, ROC-AUC=0.9898, FPR=1.92%
• Top scam indicators: http, xyz, verify, update, pay, tk, pan, fee, kyc
• Top safe indicators: valid, aug, credited, order, ref, delivered

Full Pipeline Gold Eval (refined_prediction, 308 samples)

• Acc=0.8084, Prec=0.8854, Rec=0.7722, F1=0.8249
• FPR=0.1406, FNR=0.2278 (targets: FPR<=15%, Recall>=75%)
• TP=139, FP=18, FN=41, TN=110
• Gold labels fixed: 24 LEGITIMATE_* entries corrected

Dataset

• v2 gamma (2531 samples: 1734 scam, 797 safe, 25 categories)
• Model registry: backend/models/registry.json
• Training log: backend/models/training_log.json
• Production artifacts: backend/models/model.joblib, vectorizer.joblib

Production Readiness — Phase 2-8 Complete

Phase 2: Observability ✓

• Prometheus metrics endpoint (17 metric instruments, OpenMetrics format)
• Request tracing with contextvars
• Structured logging with trace/span IDs, log_duration context manager
• Grafana dashboard (monitoring/grafana/dashboard.json)
• Alertmanager rules (9 alert rules)
• Prometheus + Grafana Docker Compose overlay
• Health diagnostics improvements

Phase 3: Scalability ✓

• Thread-safe SlidingWindowRateLimiter (threading.Lock)
• Redis-backed distributed rate limiter with graceful in-memory fallback
• create_rate_limiter() factory auto-selects Redis or in-memory
• FastAPI lifespan pattern (replaced deprecated @app.on_event)
• OCR thread pool cleanup on shutdown (configurable max_workers)
• Shared connector thread pool (lazy-init, reused across calls)
• Rate limit headers on auth endpoints

Phase 4: ML Operations ✓

• ModelRegistry with JSON persistence (models/registry.json)
• PredictionLogger with daily JSONL files (logs/predictions/)
• DriftDetector (accuracy, confidence, data, latency checks)
• EvalScheduler for automated evaluation runs
• Model rollback support in registry
• Model info endpoint (GET /model/info)
• Prediction logging integrated into /analyze/text and /analyze/image

Phase 5: Quality ✓

• Proper __init__.py exports for all packages (core, domains, services, routers, etc.)
• Removed unused import re from connectors/utils.py
• Added type hints (core/metrics.py, scripts/quality_gate.py)
• Created pyproject.toml with ruff/mypy/pytest config
• Quality and core-init test suites added

Phase 6: Performance ✓

• Cold start eliminated: Model + pipeline warmup in FastAPI lifespan
  • Model load: 11,551ms (cold) → 0ms (warmup)
  • Knowledge service: 463ms (cold) → 0ms (warmup)
  • Connector step: 146ms (cold) → 0ms (warmup)
• API text P50: 42.88ms (min 32ms, max 202ms)
• Pipeline P50: 66.29ms / P95: 98ms
• Inference (1000 chars): P50 2.86ms, P95 12.15ms, P99 24.14ms
• Performance baseline at backend/performance_baseline.json

Phase 7: DevOps ✓

• .dockerignore (root + backend)
• K8s manifests validated: probes (/live//ready), configmap (SCAMSHIELD_*), ingress (SPA routing), secrets template, HPA
• Rollback script (scripts/rollback.sh)
• Backup/DR documentation (docs/BACKUP.md)
• GitLeaks secret scanning in CI
• Docker security scan (Trivy) in CI backend workflow
• Coverage gate rewritten in pure Python

Phase 8: Final Validation ✓

• 820 tests passed, 0 failed, 3 skipped (pre-existing)
• All K8s YAML files valid
• Docker Compose valid
• Full validation report at backend/VALIDATION_REPORT.md

Known Issues

1. Transformer model (DistilBERT) won't train: module 'datasets' has no attribute 'Dataset' — library compat
2. Embedding evaluation is slow (per-sample SentenceTransformer inference on CPU)
3. A few categories still below 60 samples (legitimate categories mostly)
4. No non-English samples yet (Tamil/Hindi/Telugu)
5. Benchmark/gamma still uses beta DATA_PATH in run_beta_benchmark.py — need separate gamma benchmark
6. Frontend bundle build fails due to @sentry/core dependency resolution — pre-existing
7. OCR profiling not possible without Tesseract installed on dev machine

Next Priorities

1. Fix frontend Sentry dependency issue for production builds
2. Add real-world data collection to fix remaining 13 FNs
3. Add REST API endpoint for model retraining
4. Implement Helm charts for Kubernetes deployment
5. Add Terraform infrastructure-as-code for cloud deployment