---
name: climate-risk-agriculture
description: Analyzes agricultural climate risk systems for weather impact modeling, crop insurance integration, drought and flood prediction, soil moisture monitoring, carbon sequestration tracking, and climate adaptation planning tools.
version: "1.0.0"
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.

TARGET:
$ARGUMENTS

If arguments are provided, focus on specific areas (e.g., "drought models",
"crop insurance", "carbon tracking"). If no arguments, run the full analysis.

============================================================
PHASE 1: SYSTEM ARCHITECTURE DISCOVERY
============================================================

Step 1.1 -- Read project configuration to identify tech stack: backend, 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.

Step 1.2 -- Scan for climate risk capabilities: historical trend analysis, climate
projections, extreme weather analysis, agricultural impact modeling, risk scoring,
adaptation planning, financial risk quantification.

Step 1.3 -- Identify data sources: 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.

============================================================
PHASE 2: WEATHER IMPACT MODELING
============================================================

Step 2.1 -- Evaluate climate variable processing: temperature (min, max, GDD),
precipitation (daily, cumulative, intensity), solar radiation, wind, humidity/VPD,
frost/freeze detection, heat stress indices, chill hours for perennials.

Step 2.2 -- Assess crop-weather models: phenology models, critical period
identification, weather-yield regression, crop simulation integration (DSSAT,
APSIM), water stress modeling, heat stress modeling, cold damage modeling.

Step 2.3 -- Check impact quantification: yield loss estimation, quality impact,
replanting decisions, prevented planting, compound event modeling, confidence
intervals and uncertainty ranges.

Step 2.4 -- Evaluate historical analysis: extreme event cataloging, return period
analysis, analog year identification, trend detection in event frequency/intensity,
loss database integration.

============================================================
PHASE 3: CROP INSURANCE INTEGRATION
============================================================

Step 3.1 -- Identify products supported: Yield Protection, Revenue Protection (with
and without harvest price exclusion), ARPI, Whole-Farm Revenue, PRF rainfall index,
crop-hail, supplemental coverage, private products.

Step 3.2 -- Evaluate premium calculation: RMA methodology, subsidy application,
coverage level optimization, unit structure optimization (basic, optional,
enterprise), APH yield calculation, trend-adjusted yields, T-yield handling.

Step 3.3 -- Check indemnity estimation: loss trigger identification, indemnity
calculation by type, revenue guarantee computation, quality adjustments, late/
prevented planting provisions, multi-year loss tracking.

Step 3.4 -- Evaluate decision support: coverage sensitivity analysis, risk-return
visualization, deductible-premium optimization, combination coverage analysis
(RP + ECO/SCO), portfolio-level risk, insurance vs. self-insurance comparison.

============================================================
PHASE 4: DROUGHT AND FLOOD PREDICTION
============================================================

Step 4.1 -- Evaluate drought monitoring: index calculation (SPI, SPEI, PDSI),
classification (D0-D4), soil moisture deficit, EDDI, crop-specific indicators,
USDM integration, onset/recovery tracking, seasonal outlook.

Step 4.2 -- Check drought impact: yield reduction models, irrigation demand
increase, groundwater depletion, pasture degradation, livestock water, conservation
program triggers, economic loss estimation.

Step 4.3 -- Evaluate flood risk: frequency analysis, soil saturation modeling,
river gauge integration, FEMA zone awareness, ponding detection, prevented planting
risk, planting delay estimation, crop damage assessment.

Step 4.4 -- Assess precipitation forecasting: 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.

============================================================
PHASE 5: SOIL MOISTURE MONITORING
============================================================

Step 5.1 -- Evaluate data sources: in-situ networks (SCAN, CRN, mesonets),
satellite (SMAP, SMOS, Sentinel-1), model-derived (NLDAS, NWM), on-farm sensors,
spatial interpolation, data fusion methods.

Step 5.2 -- Check analysis: profile tracking (surface, root zone, deep), plant-
available water, anomaly detection, moisture trends, spatial mapping, yield
relationship modeling, stress threshold identification.

Step 5.3 -- Evaluate forecasting: water balance projection, coupled weather-soil
moisture prediction, horizon and accuracy, irrigation scheduling, trafficability
prediction, planting window prediction.

============================================================
PHASE 6: CARBON AND ADAPTATION
============================================================

Step 6.1 -- Evaluate carbon measurement: SOC baseline, sampling protocol, change
detection, lab integration, remote sensing proxies, model-based estimation
(COMET-Farm, DayCent, DNDC).

Step 6.2 -- Check practice tracking: cover crops, tillage classification, rotation
diversity, nutrient management, residue management, grazing management,
agroforestry, wetland restoration.

Step 6.3 -- Evaluate carbon credits: protocol compliance (Verra, Gold Standard,
ACR), additionality, MRV workflow, baseline modeling, permanence/reversal risk,
registry integration.

Step 6.4 -- Assess GHG accounting: 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).

Step 6.5 -- Evaluate adaptation planning: RCP/SSP scenario support, downscaled
projections, growing season changes, crop suitability shifts, new crop
opportunities, variety selection guidance, infrastructure investment analysis.

Step 6.6 -- Check resilience: farm/operation resilience score, vulnerability
index, adaptive capacity indicators, exposure by hazard, sensitivity by crop,
trend tracking, peer benchmarking.

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

## 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]

============================================================
NEXT STEPS
============================================================

- "Run `/crop-yield` to assess yield prediction model quality."
- "Run `/food-waste` to analyze post-harvest supply chain."
- "Run `/perf` for climate data processing performance."
- "Run `/security-review` to audit agricultural data access controls."

============================================================
DO NOT
============================================================

- 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.