swmm-end-to-end

swmm-end-to-end is a skill for Claude Code, Codex from Zhonghao1995/agentic-swmm-workflow. It costs 67 tokens per session (5,997 once invoked), scanned A, original, MIT.

A workflow guide for running Agentic SWMM, a toolkit for modelling how rainwater moves through drainage systems. It chooses the modelling steps and their order.

In plain words
What is it for?
Use it to build and run SWMM drainage models, review results, and optionally calibrate or test the model when the required project tools are installed.
Why use it?
It helps coordinate data preparation, simulation, quality checks, and optional calibration instead of deciding manually which module to run next.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: mentions Codex; built for openclaw.

Good fit Use it to build and run SWMM drainage models, review results, and optionally calibrate or test the model when the required project tools are installed.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zhonghao1995/agentic-swmm-workflow/swmm-end-to-end
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Zhonghao1995/agentic-swmm-workflow --skill swmm-end-to-end
Clone the repo
git clone --depth 1 https://github.com/Zhonghao1995/agentic-swmm-workflow

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-end-to-end

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhonghao1995/agentic-swmm-workflow/swmm-end-to-end"><img src="https://agentmods.dev/badge/skills/zhonghao1995/agentic-swmm-workflow/swmm-end-to-end.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,997 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 pass 7 Sept 2026
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 $0.00067 $0.05997
Opus 5 $0.00034 $0.02998
Sonnet 5 $0.00013 $0.01199
Haiku 4.5 $0.00007 $0.00600

Measured 11d ago against content hash 429e84d01a16, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

swmm-end-to-end 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 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/mcp_stdio_call.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-end-to-end/SKILL.md · 411 lines

How it starts

The opening of the file, as written. The whole thing — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SWMM End-to-End Orchestration

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

What this skill provides

  • A top-level orchestration contract for the agent runtime.
  • A stable handoff point for Agentic AI project memory in agent/memory/.
  • A deterministic execution order across the existing module skills:
    • swmm-canada (entry skill for Canadian AOIs — real municipal pipes where covered, synthesized elsewhere in Canada)
    • swmm-anywhere (entry skill for data-scarce regions outside Canada — no real pipe data)
    • swmm-gis
    • swmm-climate
    • swmm-params
    • swmm-network
    • swmm-builder
    • swmm-runner
    • swmm-design-review
    • swmm-plot
    • swmm-calibration
    • swmm-uncertainty
    • swmm-lid-optimization
    • swmm-experiment-audit

Routing rule — real-data path vs synth-data path

The orchestrator MUST inspect the user's inputs before choosing the entry skill:

  • If the request includes any of .shp, .csv, network.json, a CAD file, or an existing .inp path → the user has real data; route to swmm-network (or swmm-builder if the INP is already prepared).
  • If the request includes only a bbox or a location name with no pipe-network file attached → check the country first. For a Canadian AOI route to swmm-canada (the SWMMCanada upstream returns real published municipal storm pipes where a supported city covers the AOI, synthesized elsewhere in Canada). Outside Canada route to swmm-anywhere, which produces a synth .inp. Either way, downstream skills (swmm-runner, swmm-experiment-audit, swmm-plot) consume the result identically to a real-data INP.
  • If both signals appear (bbox and a SHP) → prefer the real-data path (swmm-network); only fall back to swmm-anywhere if the user explicitly asks for a synth baseline for comparison.
  • Clear stop conditions so the agent does not pretend a full model was built when critical inputs are still missing.
  • A minimal real-data fallback path for Tod Creek via scripts/real_cases/run_todcreek_minimal.py.
  • A mandatory audit handoff that consolidates artifacts, metrics, QA, comparison records, and default Obsidian audit notes after success or failure.

Read the full file on GitHub · 411 lines

Files

What ships with it

1 file 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. 11d ago First seen · 411 lines · 67 tokens per session scan A 429e84d01a16

Subscribe to this mod's changes

swmm-end-to-end is a skill published in the GitHub repository Zhonghao1995/agentic-swmm-workflow (27 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 5,997 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.

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