Creator profile

Zhonghao1995

Review Skills published by Zhonghao1995, their source repositories, maintenance signals, and identity status.

Skills
4
Repository stars
22
Identity status
Source-linked

Provenance

Source and identity

Source-linked
Profile type
Creator
Canonical name
Zhonghao1995
Public sources
1
License context
MIT

Source entries

Agent Skills from Zhonghao1995

Repository stars and maintenance signals provide context, but do not automatically become an individual Skill's quality score.

Computed 9422

Zhonghao1995/agentic-swmm-workflow

swmm-anywhere

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.

Computed 9522

Zhonghao1995/agentic-swmm-workflow

swmm-end-to-end

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.

Computed 9422

Zhonghao1995/agentic-swmm-workflow

swmm-modeling-memory

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.

Computed 9122

Zhonghao1995/agentic-swmm-workflow

swmm-uncertainty

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 hy

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