aiswmm

v0.7.1 safe
4.0
Medium Risk

Unified CLI for reproducible and auditable Agentic SWMM workflows.

πŸ€– AI Analysis

Final verdict: SAFE

The package shows low risks across most categories, with shell execution being the highest concern due to potential local script execution. However, there are no clear indicators of malicious activity.

  • Shell risk at 3/10 requires further investigation into its necessity.
  • No network calls detected.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is expected to communicate externally.
  • Shell: Shell execution appears to be used for command-line interface interactions and possibly local script execution, which could be legitimate but requires further investigation into the package's intended functionality.
  • Obfuscation: The observed patterns seem to be part of a regex compilation and token definition, likely for linguistic or functional filtering purposes rather than obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious activities were flagged.

πŸ“¦ Package Quality Overall: Low (4.0/10)

β—‹ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
β—ˆ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Zhonghao1995/agentic-swmm-workflow#readme
  • Detailed PyPI description (13304 chars)
β—‹ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
β—ˆ Medium Type Annotations 5.0

Partial type annotation coverage

  • 348 type-annotated function signatures detected in source
β—ˆ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in Zhonghao1995/agentic-swmm-workflow
  • Single author but highly active (100 commits)

πŸ”¬ Heuristic Checks

βœ“ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • preserved. _RUN_EN_REGEX = re.compile(r"\b(?:run|runs|running|execute|executes|executing)\b") _RUN_ZH_TOKENS: tuple[str, ...] = ("θ·‘", "运葌") #
  • } salib_version = getattr(__import__("SALib"), "__version__", "unknown") if method == "morris":
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • _argv, "--help"] result = subprocess.run(cmd, check=False) return result.returncode # ---------
  • ght(command, args) proc = subprocess.Popen( [command, *args], cwd=repo_root(),
  • ndle.spec) proc = subprocess.Popen( [handle.spec.command, *handle.spec.args],
  • .utc) try: proc = subprocess.run(command, cwd=cwd, capture_output=True, text=True, env=runtim
  • ll.args["path"])]) proc = subprocess.run(command, cwd=repo_root(), capture_output=True, text=True)
  • encoding="utf-8") proc = subprocess.run(["git", "apply", str(patch_path)], cwd=repo_root(), capture_
βœ“ Credential Harvesting

No credential harvesting patterns detected

βœ“ Typosquatting

No typosquatting candidates detected

βœ“ Registered Email Domain

No author email provided

βœ“ Suspicious Page Links

All external links appear legitimate

βœ“ Git Repository History

Repository Zhonghao1995/agentic-swmm-workflow appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Zhonghao Zhang, Caterina Valeo" appears to have only 1 package on PyPI (new or inactive account)
βœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

πŸ’‘ AI App Starter Prompt

Use this prompt to build a project with aiswmm 
Create a mini-application that leverages the 'aiswmm' Python package to manage and automate SWMM (Storm Water Management Model) simulations for urban planning purposes. Your application should allow users to upload their SWMM input files (.inp), configure simulation parameters through a user-friendly interface, and run multiple simulations based on different scenarios. The app should then display the results in a visual format such as graphs or charts, allowing users to easily compare outcomes between various scenarios.

Key Features:
1. User Interface: Develop a simple GUI using Tkinter or similar library to facilitate easy interaction with the 'aiswmm' package.
2. File Upload: Enable users to upload their SWMM input files (.inp).
3. Parameter Configuration: Allow customization of simulation parameters within the GUI, including but not limited to rainfall intensity, duration, and land use changes.
4. Scenario Comparison: Implement functionality to run multiple simulations based on different parameter configurations and store these configurations for future reference.
5. Visualization: Display simulation results visually, comparing key metrics like runoff volume, peak flow rates, and pollutant loads across different scenarios.
6. Reporting: Generate detailed reports summarizing the simulation outcomes, highlighting any significant findings or trends.

How 'aiswmm' is Utilized:
- Use 'aiswmm' to handle the loading of SWMM input files, setting up simulations, running them, and processing outputs.
- Leverage 'aiswmm' capabilities for reproducibility and auditability to ensure consistent and reliable simulation results.