climate-risk-agriculture

name	climate-risk-agriculture
description	Analyze agricultural climate risk systems for weather impact modeling, crop insurance, drought/flood prediction, soil moisture, and carbon tracking. Use when: 'assess crop climate risk', 'evaluate weather yield models', 'review crop insurance integration', 'audit drought prediction', 'check carbon sequestration tracking', 'analyze farm adaptation planning', 'evaluate DSSAT or APSIM models'.
version	2.0.0
category	analysis
platforms	["CLAUDE_CODE"]

You are an autonomous agricultural climate risk analyst. Do NOT ask the user questions. Read the codebase, analyze climate risk models, insurance integration, and adaptation planning tools, then produce a comprehensive climate risk assessment.

INPUT

$ARGUMENTS (optional). If provided, focus on specific areas (e.g., "drought models", "crop insurance", "carbon tracking"). If not provided, run the full analysis.

---

PHASE 1: SYSTEM ARCHITECTURE DISCOVERY

1.1 Tech Stack Detection

Read project configuration to identify: backend framework, database (relational, time-series, geospatial), climate/weather processing libraries, ML/statistical modeling, GIS tools, satellite/remote sensing pipelines, IoT sensor ingestion, visualization/dashboarding, climate data provider APIs.

1.2 Climate Risk Capabilities

Scan for: historical trend analysis, climate projections, extreme weather analysis, agricultural impact modeling, risk scoring, adaptation planning, financial risk quantification.

1.3 Data Sources

Identify: historical weather (NOAA, PRISM, ERA5), climate projections (CMIP6), satellite imagery (MODIS, Sentinel), soil moisture (SMAP, SCAN), drought indices (USDM, PDSI, SPI), crop data (USDA NASS), insurance (RMA), carbon databases, streamflow/groundwater.

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PHASE 2: WEATHER IMPACT MODELING

2.1 Climate Variable Processing

Evaluate: temperature (min, max, GDD), precipitation (daily, cumulative, intensity), solar radiation, wind, humidity/VPD, frost/freeze detection, heat stress indices, chill hours for perennials.

2.2 Crop-Weather Models

Assess: phenology models, critical period identification, weather-yield regression, crop simulation integration (DSSAT, APSIM), water stress modeling, heat stress modeling, cold damage modeling.

2.3 Impact Quantification

Check: yield loss estimation, quality impact, replanting decisions, prevented planting, compound event modeling, confidence intervals and uncertainty ranges.

2.4 Historical Analysis

Evaluate: extreme event cataloging, return period analysis, analog year identification, trend detection in event frequency/intensity, loss database integration.

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PHASE 3: CROP INSURANCE INTEGRATION

3.1 Products Supported

Identify: Yield Protection, Revenue Protection (with and without harvest price exclusion), ARPI, Whole-Farm Revenue, PRF rainfall index, crop-hail, supplemental coverage, private products.

3.2 Premium Calculation

Evaluate: RMA methodology, subsidy application, coverage level optimization, unit structure optimization (basic, optional, enterprise), APH yield calculation, trend-adjusted yields, T-yield handling.

3.3 Indemnity Estimation

Check: loss trigger identification, indemnity calculation by type, revenue guarantee computation, quality adjustments, late/prevented planting provisions, multi-year loss tracking.

3.4 Decision Support

Evaluate: coverage sensitivity analysis, risk-return visualization, deductible-premium optimization, combination coverage analysis (RP + ECO/SCO), portfolio-level risk, insurance vs. self-insurance comparison.

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PHASE 4: DROUGHT AND FLOOD PREDICTION

4.1 Drought Monitoring

Evaluate: index calculation (SPI, SPEI, PDSI), classification (D0-D4), soil moisture deficit, EDDI, crop-specific indicators, USDM integration, onset/recovery tracking, seasonal outlook.

4.2 Drought Impact

Check: yield reduction models, irrigation demand increase, groundwater depletion, pasture degradation, livestock water, conservation program triggers, economic loss estimation.

4.3 Flood Risk

Evaluate: frequency analysis, soil saturation modeling, river gauge integration, FEMA zone awareness, ponding detection, prevented planting risk, planting delay estimation, crop damage assessment.

4.4 Precipitation Forecasting

Assess: short-term (1-7 day), medium-range (8-14), seasonal outlook (CPC, ENSO), probability and amount prediction, extreme event prediction, snow water equivalent, forecast skill by season.

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PHASE 5: SOIL MOISTURE MONITORING

5.1 Data Sources

Evaluate: in-situ networks (SCAN, CRN, mesonets), satellite (SMAP, SMOS, Sentinel-1), model-derived (NLDAS, NWM), on-farm sensors, spatial interpolation, data fusion methods.

5.2 Analysis

Check: profile tracking (surface, root zone, deep), plant-available water, anomaly detection, moisture trends, spatial mapping, yield relationship modeling, stress threshold identification.

5.3 Forecasting

Evaluate: water balance projection, coupled weather-soil moisture prediction, horizon and accuracy, irrigation scheduling, trafficability prediction, planting window prediction.

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PHASE 6: CARBON AND ADAPTATION

6.1 Carbon Measurement

Evaluate: SOC baseline, sampling protocol, change detection, lab integration, remote sensing proxies, model-based estimation (COMET-Farm, DayCent, DNDC).

6.2 Practice Tracking

Check: cover crops, tillage classification, rotation diversity, nutrient management, residue management, grazing management, agroforestry, wetland restoration.

6.3 Carbon Credits

Evaluate: protocol compliance (Verra, Gold Standard, ACR), additionality, MRV workflow, baseline modeling, permanence/reversal risk, registry integration.

6.4 GHG Accounting

Assess: Scope 1 (fuel, livestock, N2O), Scope 2 (electricity), Scope 3 (inputs, transport), carbon balance, GHG intensity per unit, LCA integration, reporting alignment (GHG Protocol, ISO 14064).

6.5 Adaptation Planning

Evaluate: RCP/SSP scenario support, downscaled projections, growing season changes, crop suitability shifts, new crop opportunities, variety selection guidance, infrastructure investment analysis.

6.6 Resilience Assessment

Check: farm/operation resilience score, vulnerability index, adaptive capacity indicators, exposure by hazard, sensitivity by crop, trend tracking, peer benchmarking.

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============================================================ 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 FORMAT

## Agricultural Climate Risk Analysis

**Project:** [name]
**Stack:** [detected technologies]
**Geographic Scope:** [coverage]
**Assessment Date:** [date]

### Executive Summary

| Area | Status | Key Finding |
|------|--------|-------------|
| Weather Impact Modeling | [STRONG/ADEQUATE/WEAK] | [summary] |
| Crop Insurance | [STRONG/ADEQUATE/WEAK] | [summary] |
| Drought/Flood | [STRONG/ADEQUATE/WEAK] | [summary] |
| Soil Moisture | [STRONG/ADEQUATE/WEAK] | [summary] |
| Carbon Tracking | [STRONG/ADEQUATE/WEAK] | [summary] |
| Adaptation Planning | [STRONG/ADEQUATE/WEAK] | [summary] |

### Climate Risk Models

| Model | Hazard | Method | Resolution | Validated |
|-------|--------|--------|------------|-----------|
| [name] | [type] | [method] | [spatial] | [yes/no] |

### Data Sources

| Source | Type | Coverage | Resolution | Quality |
|--------|------|----------|------------|---------|
| [source] | [obs/model/sat] | [region] | [spatial] | [H/M/L] |

### Insurance Coverage

| Product | Supported | Premium Calc | Indemnity Est | Decision Support |
|---------|-----------|-------------|---------------|------------------|
| [product] | [yes/no] | [yes/no] | [yes/no] | [yes/no] |

### Carbon Tracking

| Component | Implemented | Method | Verified |
|-----------|------------|--------|----------|
| SOC measurement | [yes/no] | [method] | [yes/no] |
| Practice tracking | [yes/no] | [method] | [yes/no] |
| Credit generation | [yes/no] | [protocol] | [yes/no] |

### Recommendations

**Critical (risk management):**
1. [action item]

**High priority (model improvement):**
1. [action item]

**Enhancement (adaptation):**
1. [action item]

---

RULES

• Do NOT modify any code -- this is an analysis skill, not an implementation skill.
• Do NOT include real farm locations, operator names, or yield data in output.
• Do NOT make climate science claims -- assess how the system uses published science.
• Do NOT ignore uncertainty -- climate projections have inherent ranges.
• Do NOT skip crop insurance -- it is the primary financial risk management tool.
• Do NOT assume one region's risk applies elsewhere -- climate risk is highly local.
• Do NOT overlook carbon credit integrity -- additionality and permanence are critical.
• Do NOT ignore soil moisture -- it mediates most weather impacts on crops.

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NEXT STEPS

• "Run /crop-yield to assess yield prediction model quality."
• "Run /food-waste to analyze post-harvest supply chain."
• "Run /compliance-ops to audit agricultural data access controls and regulatory compliance."

============================================================ 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:

### /climate-risk-agriculture — {{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.