# Agentic SWMM v0.6.1

This release hardens the `aiswmm` runtime introduced in v0.6.0. The focus is a cleaner local agent experience: fewer confusing folders, fewer demo crashes, faster MCP startup after caching, and a smoother run-to-plot continuation flow.

## Install

macOS and Linux:

```bash
curl -fsSL https://aiswmm.com/install.sh | bash
```

Windows PowerShell:

```powershell
irm https://aiswmm.com/install.ps1 | iex
```

For a pinned install:

```bash
curl -fsSL https://aiswmm.com/install.sh | AISWMM_INSTALL_REF=v0.6.1 bash
```

```powershell
$env:AISWMM_INSTALL_REF = "v0.6.1"
irm https://aiswmm.com/install.ps1 | iex
```

## Highlights

- The interactive runtime now consistently presents itself as `aiswmm`, including the terminal prefix and executor banner.
- Interactive output folders are flatter and easier to inspect, using date-and-case paths such as `runs/YYYY-MM-DD/HHMMSS_tecnopolo_run`.
- The Tecnopolo prepared `.inp` demo can be run, audited, inspected for plot options, and continued into plotting from the same active run folder.
- Follow-up plot selections such as `Total_inflow J2 MACAO_94_23` now use the previous run directory instead of restarting input selection.
- MCP tool schema discovery is cached under `~/.aiswmm/mcp_schema_cache`, with timeout protection so slow MCP servers do not block the audited CLI path.
- Recursive search patterns generated by the planner, such as `**.inp`, are normalized to valid patterns such as `**/*.inp`.

## What users can try

Launch `aiswmm` and ask:

```text
你现在有多少个本地例子的 inp 文件？
```

Then run the prepared demo:

```text
examples/tecnopolo/tecnopolo_r1_199401.inp 帮我运行并审计
```

After the run finishes, choose a plot:

```text
Total_inflow J2 MACAO_94_23
```

The expected evidence folder contains the run-local SWMM files, audit note, final report, and generated plot artifacts.

## Evidence boundary

v0.6.1 is a runtime-quality release. It verifies the prepared-INP run, audit, plot-selection, previous-run continuation, MCP schema-cache, and search-normalization paths.

Sensitivity and uncertainty skills are present, but fully natural-language batch uncertainty requests still need additional routing and configuration generation before they should be claimed as complete end-to-end automation.
