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.
npx skills add Zhonghao1995/agentic-swmm-workflow --skill swmm-modeling-memorygit clone --depth 1 https://github.com/Zhonghao1995/agentic-swmm-workflowWrote 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.
[](https://agentmods.dev/skills/zhonghao1995/agentic-swmm-workflow/swmm-modeling-memory)<a href="https://agentmods.dev/skills/zhonghao1995/agentic-swmm-workflow/swmm-modeling-memory"><img src="https://agentmods.dev/badge/skills/zhonghao1995/agentic-swmm-workflow/swmm-modeling-memory/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.
<a href="https://agentmods.dev/skills/zhonghao1995/agentic-swmm-workflow/swmm-modeling-memory"><img src="https://agentmods.dev/badge/skills/zhonghao1995/agentic-swmm-workflow/swmm-modeling-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.01357 |
| Opus 5 | $0.00039 | $0.00678 |
| Sonnet 5 | $0.00016 | $0.00271 |
| Haiku 4.5 | $0.00008 | $0.00136 |
Grade A, and why
swmm-modeling-memory 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 10d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SWMM Modeling Memory
Part of Agentic SWMM — install the project first for the executable toolchain (aiswmm CLI, SWMM solver, MCP servers).
What this skill provides
- A downstream memory layer for audited Agentic SWMM runs.
- Deterministic summaries of repeated assumptions, QA issues, failures, missing evidence, and run-to-run differences.
- Run-level
memory_summary.jsoncards that compress audit artifacts into reusable next-run context. - Project/case-level memory groups that keep Tod Creek, Tecnopolo, TUFLOW, Generate_SWMM_inp, acceptance, and other cases separate.
- Summaries of deterministic SWMM-specific diagnostics when
model_diagnostics.jsonis present. - Human-readable lessons learned from previous audit records.
- Controlled skill update proposals that require human review and benchmark verification.
This skill does not run SWMM, build SWMM models, modify existing skills, or claim autonomous self-improvement.
Agentic SWMM is not only an automation workflow. It is a memory-informed, verification-first modeling system that can learn from audited modeling history through controlled skill refinement.
When to use this skill
Use this skill after swmm-experiment-audit has produced run-level artifacts such as:
experiment_provenance.jsoncomparison.jsonexperiment_note.mdmodel_diagnostics.jsonwhen available
Use it when:
- multiple audited runs exist,
- the user wants lessons learned across runs,
- the user asks for recurring failure patterns or QA issues,
- the user wants evidence-informed skill refinement proposals.
The proposals may point to relevant workflow skills such as end-to-end orchestration, audit reporting, QA verification, model building, or result parsing. They are not accepted changes.
Output contract
The script writes these files to the selected modeling-memory output directory:
modeling_memory_index.jsonmodeling_memory_index.mdrun_memory_summaries.jsonproject_memory_index.mdprojects/<project-key>/project_memory.jsonprojects/<project-key>/project_memory.mdlessons_learned.mdskill_update_proposals.mdbenchmark_verification_plan.md
What ships with it
2 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.
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.
- 10d ago First seen · 135 lines · 79 tokens per session scan A 5f1ad237962f
swmm-modeling-memory is a skill published in the GitHub repository Zhonghao1995/agentic-swmm-workflow (27 stars, last pushed 3d ago), licensed MIT. It adds 79 tokens to every session and 1,357 once invoked, about $0.0004 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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