What problem does it solve? Running the RHESSys hydro-ecological model requires deep operational knowledge: strict unit conventions (m/day precipitation, not mm/day), whitespace-sensitive worldfiles, multi-century carbon spin-up, and dozens of silent failure modes. This Skill packages that expertise so an agent can build inputs, execute the real rhessys7.4 binary, parse outputs, and recover from known failures without substituting approximations. ## Core Features & Use Cases - End-to-end pipeline tools: Seven Python tools cover forcing conversion, soil parameter generation, rain-duration and Kdown splitting, carbon pool initialization, model execution, and output parsing to CSV. - Diagnostic recovery: 24 symptom-diagnosis-remedy triplets in diagnostics/triplets.yaml map known failure modes (unit traps, segfaults, zero-transpiration bugs) to validated fixes. - Machine-readable model identity: dag.yaml and knowledge_infrastructure.yaml declare inputs, outputs with units and validation ranks, sensitivity edges, and cited validation bars (NSE, PBIAS). - Use Case: Simulate daily streamflow, evapotranspiration, and carbon cycling for a small watershed by converting CMFD forcing to RHESSys ASCII, generating soil .def files from HWSD data, running the compiled binary with routing and BGC flags, and validating streamflow against observed gauge discharge. ## Quick Start Run python preflight_check.py in this directory, then ask the agent to convert your forcing CSV, build soil definitions, and execute a RHESSys run for your watershed and date range.
