autochecklist

v0.2.3 suspicious
5.0
Medium Risk

A library of checklist generation and scoring methods for LLM evaluation

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network and shell execution activities, although these alone do not conclusively indicate malicious intent. Further review is recommended.

  • moderate network risk
  • subprocess execution risk
Per-check LLM notes
  • Network: The package makes network calls which are not inherently suspicious but should be reviewed to ensure they are necessary and secure.
  • Shell: Subprocess execution can be risky as it allows the package to run arbitrary commands on the host system. This needs further investigation to confirm legitimacy.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which could indicate a new or less active user, but no other red flags are present.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 26 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 26 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://autochecklist.github.io
  • Detailed PyPI description (5568 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

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 5 commits in ChicagoHAI/AutoChecklist
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • key}" self._client = httpx.Client( base_url=self.base_url, headers=hea
  • provider) async with httpx.AsyncClient( base_url=self.base_url, headers=hea
  • rameter." ) with httpx.Client(timeout=60.0) as client: response = client.post(
  • import httpx r = httpx.get(f"{VLLM_BASE_URL}/models", timeout=2) if r.is_succes
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • s.chdir(repo_root / "ui") subprocess.run(cmd) def _add_provider_flags(parser: argparse.ArgumentPars
✓ 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

Repository ChicagoHAI/AutoChecklist appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ChicagoHAI" 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 autochecklist 
Create a mini-application named 'LLMQualityChecker' that leverages the 'autochecklist' Python package to evaluate the quality of responses generated by Large Language Models (LLMs). This application will serve as a tool for developers and researchers to test and improve their LLMs by generating detailed checklists based on specific criteria and then scoring these responses according to predefined standards.

Step 1: Define the Application Structure
- Set up a virtual environment for Python.
- Install the 'autochecklist' package along with other necessary libraries such as pandas for data manipulation and matplotlib for visualization.

Step 2: Develop Checklist Generation Functionality
- Utilize 'autochecklist' to create customizable checklists based on user-defined criteria. For example, one checklist could focus on factual accuracy, another on coherence, and so forth.
- Implement a feature where users can upload their own criteria for checklist creation.

Step 3: Integrate LLM Response Scoring
- Use 'autochecklist' to score responses from LLMs against the generated checklists.
- Allow users to input multiple responses from different LLMs to compare performance.

Step 4: Visualize Results
- Employ matplotlib to display scores visually, making it easier for users to understand the strengths and weaknesses of various LLM responses.
- Create charts and graphs that show how well each response meets the checklist criteria.

Suggested Features:
- User-friendly interface for adding and modifying checklist criteria.
- Option to save and load checklists for future use.
- Detailed report generation summarizing the scores and providing insights into areas for improvement.
- Integration with popular LLM APIs like OpenAI’s GPT series, allowing direct comparison between models.

How 'autochecklist' is Utilized:
- For checklist generation, 'autochecklist' provides a flexible framework that allows you to define criteria and generate corresponding questions or tasks.
- During the scoring phase, 'autochecklist' evaluates each response against the checklist, assigning scores based on how well they meet the specified criteria. These scores are then used to provide feedback and insights into the quality of the LLM responses.

💬 Discussion Feed

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