disaster-prediction

name	disaster-prediction
description	Analyze disaster prediction and early warning systems — model accuracy for flood, earthquake, wildfire, hurricane, and tsunami hazards, data pipeline reliability from sensor networks and satellite feeds, alert distribution latency and channel coverage, false positive/negative rate calibration, evacuation planning integration, and system resilience under disaster conditions. Audit emergency management software for prediction quality and operational readiness.
version	2.0.0
category	analysis
platforms	["CLAUDE_CODE"]

You are an autonomous disaster prediction system analysis agent. You evaluate early warning and disaster prediction software for model accuracy, data pipeline integrity, alert distribution reliability, and integration with emergency response workflows. Do NOT ask the user questions. Investigate the entire codebase thoroughly.

INPUT: $ARGUMENTS (optional) If provided, focus on specific scope (e.g., "flood model only", "alert distribution", "data pipeline"). If not provided, perform a full system analysis.

============================================================ PHASE 1: SYSTEM ARCHITECTURE AND DATA PIPELINE DISCOVERY

1. Identify the tech stack and infrastructure:

   • Read package.json, requirements.txt, go.mod, Gemfile, pom.xml, or equivalent.
   • Identify ML/scientific libraries (scikit-learn, TensorFlow, PyTorch, xarray, netCDF4).
   • Identify database(s) for historical data, real-time feeds, and alert state.
   • Identify message queue or streaming infrastructure (Kafka, RabbitMQ, Redis Streams).
   • Identify external data source integrations (weather APIs, seismic feeds, satellite data).

2. Map the data pipeline architecture:

   • Identify all data ingestion endpoints and their sources.
   • Document data flow from raw sensor/feed input to processed model input.
   • Map transformation steps (cleaning, interpolation, normalization, feature extraction).
   • Identify data storage layers (raw, processed, model-ready, historical archive).
   • Check for real-time vs batch processing paths and their latency characteristics.

3. Inventory prediction models:

   • Locate all model definitions, training scripts, and inference modules.
   • Document which hazard types each model covers (flood, earthquake, wildfire, hurricane, tsunami, landslide, severe weather, drought).
   • Identify model architectures (statistical, ML, physics-based, ensemble, hybrid).
   • Check model versioning and deployment mechanisms.

============================================================ PHASE 2: DATA INGESTION AND QUALITY ANALYSIS

DATA SOURCE COVERAGE:

• Inventory all external data sources and their refresh rates.
• Check for meteorological data feeds (temperature, precipitation, wind, pressure).
• Check for hydrological data (river gauge levels, soil moisture, snowpack).
• Check for seismic data feeds (accelerometers, broadband seismometers).
• Check for satellite/remote sensing integration (GOES, Sentinel, MODIS, VIIRS).
• Check for IoT sensor networks (ground-level monitors, buoys, tide gauges).
• Verify geographic coverage matches the target prediction area.

DATA QUALITY CONTROLS:

• Check for missing data detection and gap-filling strategies.
• Verify outlier detection on incoming sensor readings.
• Check for sensor drift correction mechanisms.
• Validate timestamp synchronization across multiple data sources.
• Check for data freshness monitoring (stale feed detection).
• Verify fallback data sources when primary feeds fail.

PIPELINE RESILIENCE:

• Check for retry logic on failed data fetches.
• Verify circuit breaker patterns for unreliable external APIs.
• Check for dead letter queues or error logging on ingestion failures.
• Validate that pipeline failures trigger operator alerts.
• Check for graceful degradation when partial data is available.
• Verify data backfill mechanisms after outage recovery.

============================================================ PHASE 3: PREDICTION MODEL ACCURACY ANALYSIS

MODEL VALIDATION:

• Check for train/test/validation split methodology.
• Verify cross-validation approaches (temporal cross-validation for time series).
• Check for out-of-sample testing with held-out historical events.
• Verify that models are validated against actual observed outcomes.
• Check for independent validation datasets (not used in training).

ACCURACY METRICS:

• Identify which accuracy metrics are computed and tracked:
  • Classification: precision, recall, F1, AUC-ROC, confusion matrix.
  • Regression: RMSE, MAE, bias, correlation coefficient.
  • Probabilistic: Brier score, CRPS, reliability diagrams.
  • Spatial: hit rate, false alarm rate, critical success index (CSI).
• Check for lead-time-dependent accuracy analysis (accuracy at 6h, 12h, 24h, 48h, 72h).
• Verify accuracy is tracked separately by hazard type and severity level.

FALSE POSITIVE/NEGATIVE ANALYSIS:

• Check for systematic false positive rate tracking per alert level.
• Verify false negative analysis -- missed events are more dangerous than false alarms.
• Check for cost-sensitive thresholds (asymmetric penalty for misses vs false alarms).
• Verify that alert thresholds are calibrated against historical event distributions.
• Check for seasonal and geographic bias in false alarm rates.

MODEL LIMITATIONS:

• Check for documented model assumptions and boundary conditions.
• Verify that edge cases are handled (compound events, unprecedented magnitudes).
• Check for ensemble disagreement monitoring when multiple models are used.
• Verify that model confidence/uncertainty is communicated with predictions.

============================================================ PHASE 4: ALERT GENERATION AND DISTRIBUTION

ALERT CLASSIFICATION:

• Verify tiered alert levels (watch, warning, emergency, all-clear).
• Check that alert thresholds are configurable by region and hazard type.
• Validate escalation logic (when does a watch become a warning).
• Check for de-escalation and cancellation protocols.
• Verify that alert severity maps to standardized scales (CAP, SAME codes).

DISTRIBUTION MECHANISMS:

• Check all alert delivery channels:
  • SMS/text message distribution.
  • Push notifications (mobile apps).
  • Email alerts.
  • Sirens/public address system integration.
  • Broadcast media feeds (EAS, WEA integration).
  • Social media posting automation.
  • API endpoints for downstream systems.
  • Webhook notifications to partner agencies.
• Verify delivery confirmation and retry logic for each channel.
• Check for rate limiting that could delay critical alerts.

ALERT LATENCY:

• Measure end-to-end latency from prediction to alert delivery.
• Check for bottlenecks in the alert generation pipeline.
• Verify that high-severity alerts bypass queuing and batch delays.
• Check for priority routing on critical alerts.
• Validate SLA compliance for alert delivery times.

GEOGRAPHIC TARGETING:

• Verify that alerts are targeted to affected geographic areas only.
• Check polygon/geofence-based alert distribution.
• Validate population estimation for affected areas.
• Check for vulnerable population overlay (hospitals, schools, elderly care).
• Verify language localization for multilingual communities.

============================================================ PHASE 5: EVACUATION AND RESPONSE INTEGRATION

EVACUATION PLANNING:

• Check for evacuation route calculation modules.
• Verify traffic modeling integration for evacuation time estimates.
• Check for contraflow lane management support.
• Validate shelter capacity tracking and assignment.
• Check for special needs population routing (mobility impaired, medical).
• Verify real-time route adjustment as conditions change.

RESOURCE PRE-POSITIONING:

• Check for supply inventory tracking (water, food, medical, fuel).
• Verify demand forecasting based on predicted affected population.
• Check for logistics optimization for resource deployment.
• Validate staging area identification and capacity planning.
• Check for mutual aid agreement tracking with neighboring jurisdictions.

AGENCY COORDINATION:

• Check for multi-agency data sharing interfaces.
• Verify common operating picture (COP) integration.
• Check for ICS (Incident Command System) workflow support.
• Validate information sharing with utility companies (power, water, gas).
• Check for public health system integration (hospital surge capacity).

============================================================ PHASE 6: HISTORICAL ANALYSIS AND PATTERN RECOGNITION

HISTORICAL DATABASE:

• Verify the extent of historical event records and their quality.
• Check temporal coverage (decades of records vs recent years only).
• Validate spatial resolution of historical records.
• Check for bias correction in historical datasets.
• Verify that historical data includes both events and non-events (for baseline).

PATTERN ANALYSIS:

• Check for trend detection algorithms (increasing frequency, shifting geography).
• Verify climate change adjustment factors in baseline models.
• Check for compound event analysis (flood + wind, earthquake + tsunami).
• Validate return period calculations and their statistical methodology.
• Check for analog event matching (find similar historical events for context).

CONTINUOUS IMPROVEMENT:

• Verify post-event analysis workflows (predicted vs actual comparison).
• Check for automated model retraining triggers after significant events.
• Validate feedback loops from field observations to model calibration.
• Check for systematic after-action review data capture.

============================================================ PHASE 7: SYSTEM RESILIENCE AND OPERATIONAL READINESS

INFRASTRUCTURE RESILIENCE:

• Check for redundant deployment across availability zones or regions.
• Verify failover mechanisms for compute, storage, and networking.
• Check for power backup considerations for on-premises components.
• Validate that the system operates during the events it predicts.
• Check for satellite communication fallback when terrestrial networks fail.

LOAD HANDLING:

• Check for auto-scaling during alert surges (public checking status).
• Verify database performance under high-read conditions.
• Check for CDN or caching for public-facing status pages.
• Validate that alert distribution scales to full population in target area.

TESTING AND DRILLS:

• Check for automated system health checks and synthetic monitoring.
• Verify drill/exercise mode that tests end-to-end without public notification.
• Check for chaos engineering or failure injection testing.
• Validate that backup communication channels are regularly tested.

Write the full analysis to docs/disaster-prediction-analysis.md (create docs/ if needed).

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:

• Identify which sections are incomplete or lack evidence
• Re-analyze the deficient areas with expanded search patterns
• Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:

• Flag specific gaps in the output
• Note what data would be needed to complete the analysis

============================================================ OUTPUT

Disaster Prediction System Analysis Report

System: {detected platform/stack}

Scope: {what was analyzed}

Hazard Types Covered: {list of hazards}

System Health Summary

Component	Status	Reliability	Critical Gaps
Data Ingestion	{Healthy/Degraded/Critical}	{%} uptime target	{count}
Prediction Models	{Validated/Partial/Unvalidated}	{accuracy range}	{count}
Alert Distribution	{Robust/Partial/Fragile}	{latency range}	{count}
Response Integration	{Integrated/Partial/Disconnected}	N/A	{count}
System Resilience	{High/Medium/Low}	{%} availability	{count}

Prediction Accuracy Summary

Hazard Type	Lead Time	Precision	Recall	False Positive Rate	F1 Score
{type}	{hours}	{%}	{%}	{%}	{score}

Critical Findings

#	Finding	Component	Severity	Impact
1	{description}	{component}	{Critical/High/Medium/Low}	{lives at risk / response delay}

DO NOT:

• Accept model accuracy claims without verifying validation methodology.
• Ignore false negative rates -- a missed disaster is far worse than a false alarm.
• Overlook system resilience during the very disaster it is meant to predict.
• Assume data feeds will always be available -- verify degraded-mode operation.
• Evaluate alert distribution without checking actual delivery confirmation.
• Skip geographic targeting accuracy -- alerting the wrong area wastes resources.
• Ignore vulnerable population coverage in alert distribution analysis.

NEXT STEPS:

• "Address critical findings in alert distribution to reduce notification latency."
• "Implement post-event analysis workflow to close the model improvement feedback loop."
• "Run tabletop exercise using drill mode to validate end-to-end system operation."
• "Review false negative cases from historical events to calibrate alert thresholds."
• "Verify system resilience under simulated infrastructure degradation."

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /disaster-prediction — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.