axor-benchmarks

v0.3.0 suspicious
4.0
Medium Risk

Benchmark governed vs raw Claude on your codebase

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential low activity and incomplete maintainer information, despite showing no direct signs of malicious intent.

  • Metadata risk indicating potential low activity and lack of maintainer details
  • Shell risk requiring further scrutiny
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution may be used for running benchmarks or tests, but requires careful review to ensure it does not pose a security risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential low activity and lack of maintainer information, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. test_governed_runner.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5948 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 5.0

Partial type annotation coverage

  • 39 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 7 commits in Bucha11/axor-benchmarks
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • d]" try: result = subprocess.run( command, shell=True, cw
  • command, shell=True, cwd=cwd, timeout=timeout,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 axor-benchmarks 
Create a Python-based benchmarking tool named 'CodeSpeedAnalyzer' that leverages the 'axor-benchmarks' package to compare the performance of your codebase when executed through a standard Python environment versus when it's governed by Claude (a hypothetical AI governance layer). This tool should allow users to input their Python code snippets or file paths directly into the application, run benchmarks, and then output detailed reports comparing the execution times and resource usage between the two environments.

Key Features:
1. Code Input Interface: Users can paste their Python code directly into the interface or upload Python files.
2. Benchmark Execution: The tool should automatically run the provided code in both environments and capture relevant metrics such as execution time, memory usage, and CPU load.
3. Result Visualization: Offer visual representations of the benchmark results, including graphs and charts to highlight differences in performance.
4. Detailed Reports: Generate comprehensive reports summarizing the findings, including statistical analysis of multiple runs.
5. Customizable Parameters: Allow users to set parameters like number of iterations for each benchmark run and specific configurations for the Claude governance layer.

How to Utilize 'axor-benchmarks':
- Import the necessary modules from the 'axor_benchmarks' package at the beginning of your script.
- Use the 'benchmark_setup()' function to configure the benchmarking process according to user inputs.
- Implement the 'run_governed_code()' and 'run_raw_code()' functions to execute the provided code in the governed Claude environment and the raw Python environment respectively.
- Utilize the 'generate_report()' function to compile and format the benchmark data into readable reports.
- Integrate visualization libraries like matplotlib or seaborn to create insightful visualizations based on the benchmark results.

💬 Discussion Feed

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