agentic-data-contracts

v0.22.0 suspicious
4.0
Medium Risk

YAML-first, domain-driven data governance for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is generally clean with no signs of obfuscation or credential harvesting. However, the maintainer's incomplete profile and new account suggest potential risks that cannot be ignored.

  • Low obfuscation risk
  • No credential harvesting detected
  • Suspicious maintainer profile
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
  • Metadata: The maintainer has an incomplete profile and a new account, which raises some suspicion but does not strongly indicate malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository flyersworder/agentic-data-contracts appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 agentic-data-contracts 
Create a mini-application named 'DataGovernanceTool' using Python that leverages the 'agentic-data-contracts' package to manage and enforce data governance policies for AI agents. This tool will enable users to define, validate, and apply data contracts that ensure compliance with predefined rules and standards across various datasets. The application should include the following key features:

1. **Data Contract Definition**: Users should be able to create and edit data contracts in YAML format. Each contract should specify rules related to data types, formats, constraints, and other relevant criteria.
2. **Contract Validation**: Implement functionality to validate datasets against defined data contracts. This includes checking if the data adheres to the specified rules and generating reports on any violations.
3. **Policy Enforcement**: Integrate a feature that automatically applies the data governance policies enforced by the contracts when new data is ingested or processed. This ensures continuous compliance without manual intervention.
4. **Audit Trail**: Maintain an audit log of all actions performed by the tool, including creation, modification, validation results, and enforcement outcomes of data contracts.
5. **User Interface**: Develop a simple web-based interface for managing data contracts and viewing validation reports. This UI should allow users to easily navigate through their contracts, view validation statuses, and interact with the audit logs.
6. **Documentation and Examples**: Provide comprehensive documentation and sample data contracts to help users understand how to use the tool effectively.

To achieve these objectives, you'll utilize the 'agentic-data-contracts' package extensively. For instance, you might use it to parse and interpret YAML-formatted contracts, perform validations based on those definitions, and generate meaningful reports from the validation process. Additionally, explore integrating other Python libraries as necessary to enhance functionality and user experience.