agentic-swmm-workflow

Calibration and validation scaffold for EPA SWMM. Use when an agent needs to (1) compare simulated vs observed flow, (2) evaluate candidate parameter sets, (3) rank explicit candidates by an objective, (4) run a bounded random / LHS / adaptive search for the best-fitting parameters, (5) run a publication-grade SCE-UA calibration with KGE as the primary objective and (r, alpha, beta) decomposition reported, or (6) run a DREAM-ZS Bayesian calibration producing a posterior over parameters with Gelman-Rubin convergence checks. Dedicated sensitivity-analysis methods (OAT, Morris, Sobol') now live on the `swmm-uncertainty` skill.

Publication-grade plotting for SWMM rainfall–runoff time-series figures. Use when an agent needs to produce a paired rainfall (top, inverted) + node flow (bottom) figure from a SWMM .inp + .out, with strict style rules (SI units, Arial, ticks inward, no title), optionally cropped to an event window or focus day.

Run EPA SWMM (swmm5) simulations reproducibly and extract key metrics from the report file. Use when an agent needs to (1) run a .inp via swmm5 CLI, (2) generate a run directory with rpt/out + manifest, (3) extract peak flow/time for a node/outfall, (4) parse SWMM continuity (Runoff Quantity / Flow Routing) errors from .rpt, or (5) compare two .rpt files (e.g. GUI vs CLI) for equivalence.

GIS/DEM preprocessing for SWMM experiments using the user's own QGIS/GRASS layers. Use when the user asks to (1) delineate subcatchments through QGIS/GRASS (standard or entropy-guided), (2) preprocess QGIS-derived subcatchment polygons into builder-ready CSV, (3) identify high-entropy hotspot subcatchments, or (4) expose QGIS/GRASS-backed preprocessing as MCP tools for reproducible workflows. For bbox-only inputs WITHOUT real pipe data, use `swmm-anywhere` instead (it synthesises a plausible network from OSM streets + DEM).

Build, validate, and route SWMM pipe-network models for urban drainage from raw municipal shapefiles or structured GIS/CAD exports. Use when handling junctions, conduits, outfalls, xsections, network field-mapping configs, or wiring subcatchments to upstream nodes. Requires real pipe data as SHP / GeoJSON / CSV — native CAD (DXF/DWG) is not parsed and must first be exported to one of these. For data-scarce areas where only a bbox is available and no pipe inventory exists, use `swmm-anywhere` instead.

Parameter and forcing uncertainty propagation and sensitivity analysis for EPA SWMM. Use when an agent needs to (1) propagate parameter uncertainty through SWMM (fuzzy alpha-cut or Monte Carlo), (2) quantify hydrograph envelopes or output entropy without treating the run as calibration, (3) screen which parameters matter using OAT / Morris elementary-effects / Sobol' indices, (4) generate a rainfall ensemble (observed-series perturbation or IDF-curve design storms) and aggregate the resulting hydrograph envelope, or (5) build the integrated paper-reviewer-facing uncertainty source decomposition (`uncertainty_source_summary.md` + `uncertainty_source_decomposition.json`) over the raw outputs of the prior steps.

Synthesize a plausible SWMM drainage network from public data (OSM streets + DEM) when NO real pipe-network data exists — input is just a bbox. Use ONLY when the user has no pipe shapefile/CAD/GIS data, or to establish a baseline before real data arrives; if real pipe data exists, route to swmm-network or swmm-gis instead.

Score a completed SWMM run against a configurable YAML rulebook of design checks — GB50014-style standards for real catchments, or the reference-free physical-plausibility rulebook for synthesized networks. Reads the run's existing manifest.json / model.rpt / model.inp and never re-runs SWMM. Use it post-run for compliance or plausibility review; continuity gating stays with postflight.

Top-level orchestration skill for agentic SWMM modelling. Use when an agent needs one entrypoint that decides which module tools to run, in what order, and when to stop, for example to build, run, QA, and optionally calibrate a SWMM case from prepared or partially prepared inputs.

Consolidate Agentic SWMM run artifacts into auditable provenance, comparison records, and local Obsidian audit notes. Use after any SWMM build/run/QA attempt, successful or failed, when an agent or CLI workflow needs a traceable record of inputs, commands, artifacts, metrics, QA checks, run-to-run differences, and first-user-friendly Obsidian visualization.

Draft a well-formed new skill (a SKILL.md scaffold, optionally with scripts/references) from a described recurring need, for human review and approval. Use whenever a repeated workflow gap has no existing skill covering it, when someone wants to propose or create a new skill or capability, or when an agentic system detects a recurring problem that warrants a brand-new skill rather than editing an existing one. Domain-general — works for any modeling or workflow domain; SWMM is just one example.

Deterministic rainfall/climate formatting for SWMM. Use when converting timestamped rainfall CSV files into SWMM-ready [TIMESERIES] lines and [RAINGAGES] helper snippets for swmm-builder.

Assemble a runnable SWMM INP deterministically from subcatchment geometry/attributes, merged parameter JSON, network JSON, and climate references. Use when creating auditable INP + manifest artifacts for downstream swmm-runner/calibration.

Deterministic mapping from land use and soil texture to SWMM runoff/subarea and Green-Ampt infiltration parameters. Use when generating first-pass subcatchment parameter tables for swmm-builder.

Read historical Agentic SWMM experiment audit artifacts and summarize repeated assumptions, QA issues, failures, missing evidence, run-to-run differences, lessons learned, and controlled skill update proposals. Use downstream of swmm-experiment-audit when multiple audited runs exist or when a user asks for modeling memory, failure-pattern extraction, lessons learned, or human-reviewed skill refinement proposals.

Generate a client-deliverable Word (.docx) report from an audited SWMM run directory. Reads manifest.json, experiment_provenance.json, model_diagnostics.json, comparison.json, and any PNG figures — SWMM is never re-run. Supports custom YAML/JSON section templates.

Complete SWMM engine coverage: pollutant buildup/washoff simulation support and load reporting. Validate water-quality config JSON, build INPs with WQ sections, and extract pollutant load summaries from completed runs.

Retrieve relevant Agentic SWMM modeling memory from audited runs, modeling-memory summaries, and Obsidian-compatible notes at query time. Use when a user asks for RAG, similar past runs, evidence-linked memory retrieval, historical QA/failure patterns, or memory-grounded answers.