ai-flow-architect

v0.1.1 safe
3.0
Low Risk

AI proposes. You decide. — Adversarial AI workflow engine with built-in quality arbitration

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories with no signs of malicious activities. The obfuscation and metadata risks are low and do not suggest any immediate threats.

  • No network calls or shell executions detected.
  • Low risk of credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical and safe.
  • Obfuscation: The code uses unusual import patterns but does not indicate malicious intent; it appears to be for logging and metadata purposes.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The maintainer has only one package and uses a common free email provider, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (5.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/wdnmd1265/ai-flow-architect#readme
  • Detailed PyPI description (15036 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 285 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 38 commits in wdnmd1265/ai-flow-architect
  • Single author but highly active (38 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • ult, "timestamp": __import__('time').time(), }) logger.info(f"质量审核完成，通过
  • blueprint.description} 审核时间: {__import__('datetime').datetime.now().strftime('%Y-%m-%d %H:%M:%S')} 质量评分: {quali
  • ned, "timestamp": __import__('time').time(), "type": "multi_audit", })
  • Field(default_factory=lambda: __import__('time').time(), description="创建时间") class ContextManager: """
  • ent, "timestamp": __import__('time').time(), "metadata": metadata or {}, }
  • ent, "timestamp": __import__('time').time(), }) def get_history(self) -> list:
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: qq.com>

  • Very short email domain: qq.com>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository wdnmd1265/ai-flow-architect appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "盛鑫" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-flow-architect 
Create a mini-application named 'DecisionAdvisor' that leverages the 'ai-flow-architect' package to assist users in making decisions based on adversarial AI workflows. This application will simulate a scenario where two AI models propose different solutions to a problem, and the user must choose the best solution after reviewing the quality of each proposal.

Step 1: Define the Problem Space
- Choose a specific domain where decision-making is critical, such as financial investments, healthcare diagnostics, or environmental conservation.
- Clearly define the types of problems that the application will solve within this domain.

Step 2: Implement AI Models
- Develop two distinct AI models using frameworks like TensorFlow or PyTorch. Each model should have its own unique approach to solving the defined problems.
- Ensure these models can generate proposals that are somewhat contradictory or complementary, reflecting real-world complexity.

Step 3: Integrate ai-flow-architect
- Use 'ai-flow-architect' to orchestrate the workflow between the two models. The package should handle the process of generating proposals from both models, evaluating their quality, and presenting them to the user.
- Leverage the built-in quality arbitration mechanism to assess the reliability and validity of each proposal.

Step 4: User Interface
- Design a simple yet effective user interface (UI) that presents the proposals from both AIs side-by-side, along with quality metrics.
- Include options for the user to provide feedback on the proposals, which could influence future predictions made by the AIs.

Step 5: Evaluation and Feedback Loop
- Implement a system where the user can select the best proposal, and this selection is fed back into the system to improve the models over time.
- Use 'ai-flow-architect' to manage the feedback loop, ensuring continuous learning and improvement of the AIs.

Suggested Features:
- Real-time proposal generation from both AIs.
- Detailed explanation of how each AI arrived at its proposal.
- Interactive UI elements allowing users to adjust parameters and see immediate changes in the proposals.
- Historical data storage to track user selections and model performance over time.
- Export functionality to save sessions for later review or sharing.

This project aims to showcase the power of adversarial AI workflows while giving users a tool to make informed decisions in complex scenarios.