What problem does it solve? Running the Cell2Fire W wildfire simulator correctly requires operational expertise scattered across source code and docs: preparing co-registered landscape rasters, converting weather forcing into the exact units and conventions the model expects, selecting the right fire-behavior model and fuel lookup table, and diagnosing silent failures such as wind speed in m/s instead of km/h. This Skill packages that expertise so an agent can run the real C++ binary reliably instead of substituting approximations. ## Core Features & Use Cases - End-to-end simulation pipeline: Four validated tools cover landscape preparation, weather conversion to Weather.csv, fuel lookup table generation (Scott&Burgan, Canadian FBP, Kitral, Portugal), execution of the Cell2Fire binary, and parsing of outputs into burn probability maps and summary CSVs. - Unit and convention safety: Explicit conversion tables and 18 diagnostic triplets catch critical traps like wind direction convention errors, RH as fraction instead of percent, and raster dimension mismatches that cause segfaults. - Validation against cited benchmarks: Fire-scar outputs are graded against literature-derived CSI and burn-probability skill thresholds rather than intuition. - Use Case: Simulate the Vilopriu 2013 fire with the Scott&Burgan model over a 20m landscape grid, run a Monte Carlo ensemble, and compare the final burn scar against the observed perimeter using CSI. ## Quick Start Run python preflight_check.py in this directory, then ask the agent to simulate wildfire spread over your instance folder with the Scott&Burgan model and parse the resulting burn probability map.
