swmm-builder

swmm-builder is a skill for Claude Code, Codex from Zhonghao1995/agentic-swmm-workflow. It costs 52 tokens per session (1,850 once invoked), scanned A, original, MIT.

A builder that assembles a runnable SWMM input file from drainage areas, network data, model parameters, and rainfall references. SWMM is software used to model stormwater drainage systems.

In plain words
What is it for?
Use it to create a SWMM `.inp` model and manifest from the required CSV and JSON files, with optional configuration and validation diagnostics.
Why use it?
It combines inputs in a repeatable way and checks important sections of the resulting model. It also records source paths, file hashes, and metadata so the model can be reviewed later.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 skills/swmm-params/scripts/landuse_to_swmm_params.py \.

Good fit Use it to create a SWMM .inp model and manifest from the required CSV and JSON files, with optional configuration and validation diagnostics.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/Zhonghao1995/agentic-swmm-workflow
agentmods
npx agentmods add skills/zhonghao1995/agentic-swmm-workflow/swmm-builder

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for swmm-builder

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhonghao1995/agentic-swmm-workflow/swmm-builder/github.svg)](https://agentmods.dev/skills/zhonghao1995/agentic-swmm-workflow/swmm-builder)
Your own site
<a href="https://agentmods.dev/skills/zhonghao1995/agentic-swmm-workflow/swmm-builder"><img src="https://agentmods.dev/badge/skills/zhonghao1995/agentic-swmm-workflow/swmm-builder/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for swmm-builder

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhonghao1995/agentic-swmm-workflow/swmm-builder"><img src="https://agentmods.dev/badge/skills/zhonghao1995/agentic-swmm-workflow/swmm-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,850 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Agent Snooping · line 149
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00052 $0.01850
Opus 5 $0.00026 $0.00925
Sonnet 5 $0.00010 $0.00370
Haiku 4.5 $0.00005 $0.00185

Measured 9d ago against content hash 637f78b8681e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

swmm-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/build_swmm_inp.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/swmm-builder/SKILL.md · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SWMM Builder (INP assembly layer)

Part of Agentic SWMM — install the project first for the executable toolchain (aiswmm CLI, SWMM solver, MCP servers).

Contract

Build a runnable SWMM .inp using explicit file inputs:

  • subcatchments.csv (shape/area/outlet/routing basics)
  • merged params JSON from swmm-params
  • network JSON from swmm-network
  • rainfall/time-series references from swmm-climate
  • optional options config JSON

The builder writes:

  • final SWMM INP text (--out-inp)
  • manifest JSON (--out-manifest) with source paths + SHA256 + key metadata
  • strict validation diagnostics for critical sections ([OPTIONS], [RAINGAGES], [TIMESERIES], [SUBCATCHMENTS], [SUBAREAS], [INFILTRATION], and current network sections)

Inputs

Subcatchments CSV schema (required)

Required columns:

  • subcatchment_id
  • outlet
  • area_ha
  • width_m
  • slope_pct

Optional columns:

  • rain_gage (falls back to default gage from climate/config)
  • curb_length_m (default 0)
  • snow_pack (default blank)

Params JSON (required)

Expected to match skills/swmm-params/scripts/merge_swmm_params.py output:

  • sections.subcatchments (id, pct_imperv)
  • sections.subareas (id, runoff/subarea fields)
  • sections.infiltration (id, Green-Ampt fields)
  • Required fields are now validated strictly with type/range checks (for example %Imperv and routing percentages must be 0..100).

Network JSON (required)

Expected to match skills/swmm-network schema (junctions, outfalls, conduits, etc.). Builder now validates required network fields used to emit [JUNCTIONS], [OUTFALLS], [CONDUITS], [XSECTIONS], [COORDINATES], and [VERTICES].

Climate references (required in MVP)

Provide either:

  • --timeseries-text directly, or
  • --rainfall-json produced by swmm-climate/format_rainfall.py (must include outputs.timeseries_text)

For [RAINGAGES], provide either:

  • --raingage-json from swmm-climate/build_raingage_section.py, or
  • rely on default deterministic gage generation from rainfall series_name.

Read the full file on GitHub · 169 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 169 lines · 52 tokens per session scan A 637f78b8681e

Subscribe to this mod's changes

swmm-builder is a skill published in the GitHub repository Zhonghao1995/agentic-swmm-workflow (27 stars, last pushed 3d ago), licensed MIT. It adds 52 tokens to every session and 1,850 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

arxiv-summarizer-orchestrator

End-to-end orchestration skill for periodic arXiv collection and reporting using three sub-skills: arxiv-search-collector, arxiv-paper-processor, and arxiv-batch-reporter. Supports manual language control across all markdown outputs and Stage-B processing strategy (subagentparallel default max 5, or serial).

InternLM/WildClawBench · 75 tokens

academic-literature-search

A multi-database search tool for academic papers and other scholarly publications. It searches sources such as Semantic Scholar, Crossref, arXiv, and PubMed, which cover research across fields including computing, physics, and medicine.

InternLM/WildClawBench · 0 tokens

cuopt-numerical-optimization-formulation

LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.

NVIDIA/skills · 42 tokens

earth2studio-create-datasource

Create and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores. Do NOT use for fetching data with existing sources, model inference, or installation tasks.

NVIDIA/skills · 53 tokens

flux-analyzer

Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

aiming-lab/AutoResearchClaw · 44 tokens

earth2studio-data-fetch

Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.

NVIDIA/skills · 36 tokens