accountable-surface-spec

v0.1.0 suspicious
5.0
Medium Risk

Canonical accountable surface manifest schema and validation for the Structural Explainability ecosystem.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low direct risks but high metadata risk due to recent creation and incomplete author details, raising suspicion for potential supply-chain attack.

  • High metadata risk
  • Recent creation and low activity
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: High risk due to recent creation, low activity, and incomplete author details.

🔬 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: structural-explainability.org>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository created very recently: 3 day(s) ago (2026-06-03T03:21:47Z)

  • Repository created very recently: 3 day(s) ago (2026-06-03T03:21:47Z)
  • Single contributor with only 4 commit(s) — possibly throwaway account
  • All 4 commits happened within 24 hours
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Package is very new: uploaded 3 day(s) ago
  • 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 accountable-surface-spec 
Develop a mini-application called 'ExplainIt' that leverages the 'accountable-surface-spec' package to ensure transparency and accountability in machine learning model predictions. This application will serve as a tool for data scientists and researchers to validate their models' outputs against predefined accountability criteria. Here’s a detailed breakdown of the project scope:

1. **Project Overview**: ExplainIt aims to provide users with a straightforward way to validate their ML model outputs using the Structural Explainability ecosystem standards. Users should be able to input their model predictions and have them checked against a set of accountability metrics defined by the 'accountable-surface-spec' schema.

2. **Core Features**:
   - **Model Input Interface**: Develop a user-friendly interface where users can upload their model predictions in CSV format.
   - **Schema Validation**: Utilize the 'accountable-surface-spec' package to validate the uploaded model predictions against predefined accountability schemas.
   - **Report Generation**: Automatically generate a report detailing whether each prediction meets the specified accountability criteria. The report should include a summary of passed/failed checks and any relevant statistics.
   - **Customizable Schemas**: Allow users to define their own accountability schemas if the default ones do not meet their needs.
   - **Visualization Tools**: Provide basic visualization tools to help users understand the distribution of passed/failed predictions.

3. **Implementation Steps**:
   - Step 1: Set up a Python environment with all necessary packages including 'accountable-surface-spec'.
   - Step 2: Design the schema validation logic based on the 'accountable-surface-spec' documentation.
   - Step 3: Create a simple web-based UI using Flask or a similar framework for uploading files and displaying results.
   - Step 4: Implement the schema validation process, ensuring it correctly interprets and applies the provided schemas.
   - Step 5: Develop the reporting and visualization functionalities to make the results easily understandable.
   - Step 6: Test the application thoroughly with different datasets and schemas to ensure reliability and accuracy.

4. **Utilization of 'accountable-surface-spec' Package**: The package will primarily be used for defining and validating the accountability schemas. Users will be able to specify these schemas either directly in the application or through configuration files. The validation process will involve loading the schemas into the application, parsing the user-provided model predictions, and then applying the validation rules to determine compliance with the accountability standards. The 'accountable-surface-spec' package ensures that the validation process adheres to industry best practices for structural explainability, thereby enhancing trust in the model outputs.