ai-cost-auditor

v0.1.0 suspicious
6.0
Medium Risk

Track AI API costs, token usage, and prompt quality across all providers

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk due to its metadata indicators, including low repository activity and a single contributor. However, no direct malicious activities have been confirmed.

  • Metadata risk score of 7/10
  • Low repository activity and single contributor
Per-check LLM notes
  • Network: The package makes network calls which could be for legitimate purposes like fetching configuration or cost data, but further investigation is needed to confirm its legitimacy.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The repository's low activity, single contributor, and new package status raise concerns about potential malicious intent.

📦 Package Quality Overall: Low (3.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2238 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

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 2 commits in jitentiwari82/ai-cost-auditor
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • : int = 5) -> dict: req = urllib.request.Request( _LITELLM_URL, headers={"User-Agent"
  • auditor/1.0"}, ) with urllib.request.urlopen(req, timeout=timeout) as resp: return json.l
✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 2 total
  • Single contributor with only 2 commit(s) — possibly throwaway account
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 ai-cost-auditor 
Develop a comprehensive AI Cost Tracker application using the 'ai-cost-auditor' Python package. This application will serve as a dashboard for tracking costs, token usage, and prompt quality from various AI service providers such as OpenAI, Anthropic, etc. The goal is to provide users with a real-time view of their AI-related expenses and help them optimize their usage based on cost and efficiency.

### Features:
1. **User Authentication**: Implement a simple login system to ensure data privacy and personalization.
2. **Provider Integration**: Allow users to connect multiple AI services (e.g., OpenAI, Anthropic) by providing API keys and other necessary credentials.
3. **Cost Tracking**: Automatically track costs incurred from each provider's API usage, displaying total spend over time and per provider.
4. **Token Usage Analysis**: Monitor token consumption across different APIs, showing trends and spikes in usage.
5. **Prompt Quality Assessment**: Evaluate the quality of prompts sent to the APIs, offering suggestions for improvement based on cost-efficiency.
6. **Report Generation**: Enable users to generate detailed reports on their AI API usage, including visualizations of cost trends and token usage.
7. **Alert System**: Set up alerts for when certain thresholds are reached, such as high costs or inefficient prompt usage.
8. **User Interface**: Design a user-friendly interface that allows easy navigation through the dashboard, viewing reports, and setting up alerts.

### Utilizing 'ai-cost-auditor':
- Use the package to integrate with various AI providers' APIs for real-time data collection.
- Leverage the package's capabilities to analyze and display token usage and cost information accurately.
- Apply the package's built-in functions to assess prompt quality and offer insights into optimizing usage patterns.
- Implement the package's reporting features to generate comprehensive usage reports for users.
- Utilize the package's alert functionalities to notify users of critical thresholds being met or exceeded.

This project aims to provide developers and businesses with a powerful tool to manage their AI API expenses efficiently and make informed decisions about their usage.

💬 Discussion Feed

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