What problem does it solve? Running the Open Global Glacier Model correctly requires operational expertise scattered across documentation and practice: preparing RGI glacier inventories, calibrating mass balance against geodetic observations, avoiding silent errors like hydrological-year misalignment and double-counted melt, and recovering from known failure modes. This Skill packages that expertise so an AI agent can run, validate, and couple OGGM simulations reliably. ## Core Features & Use Cases - Six-stage glacier pipeline: 18 executable tools covering glacier inventory (RGI intersection), preprocessing (GDir creation at levels L0-L5), climate input (W5E5/CRU/CMIP6), mass-balance calibration against Hugonnet 2021 geodetic data, historical and SSP-scenario simulation, and VIC hydrological coupling. - Diagnostic recovery: 26 symptom-diagnosis-remedy triplets in diagnostics/triplets.yaml plus a preflight_check.py that verifies the environment, model package, tools, and cached data before any run. - Validation contract: dag.yaml defines outputs (specific_mass_balance as the rank-1 headline variable, volume, area, length, hydro components) with units, observability, and cited validation bands. - Use Case: Model glacier contribution to discharge in the Yarlung Tsangpo basin under CMIP6 SSP scenarios, then inject OGGM melt into a VIC+CaMa-Flood routing pipeline while avoiding the double-counting trap. ## Quick Start Run python preflight_check.py in this directory, then ask the agent to find glaciers in your basin shapefile and run an OGGM historical simulation with monthly hydrological output.
