attune-help

v0.11.1 suspicious
6.0
Medium Risk

Lightweight help runtime with progressive depth and audience adaptation.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows significant risks related to credential harvesting and has a suspiciously inactive maintainer and repository. This combination raises concerns about its legitimacy and security.

  • High credential risk indicating potential credential harvesting attempts
  • Inactive maintainer and low repository activity suggesting unreliability
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution may be for package installation or testing purposes, but further investigation into the purpose of these commands is advised.
  • Obfuscation: No signs of code obfuscation detected.
  • Credentials: Detected patterns suggest potential credential harvesting attempts.
  • Metadata: The maintainer's lack of information and the repository's low activity suggest potential unreliability.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 22 test file(s) found

  • Test runner config found: pyproject.toml
  • 22 test file(s) detected (e.g. test_adapter_rag.py)
◈ Medium Documentation 7.0

Some documentation present

  • Detailed PyPI description (8313 chars)
  • Classifier: Documentation
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 226 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 28 commits in Smart-AI-Memory/attune-help
  • Single author but highly active (28 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • venv_dir / "bin" / "pip" subprocess.run( [str(pip), "install", "--quiet", "python-frontmatte
  • apture_output=True, ) subprocess.run( [str(pip), "install", "--quiet", "--no-deps", "-e",
  • utput=True, ) base = subprocess.run( [str(py), "-c", "import attune_help; print('ok')"],
  • put=True, ) result = subprocess.run( [str(py), "-c", f"import {shim_module}"], c
⚠ Credential Harvesting score 10.0

Found 5 credential access pattern(s)

  • rs._engine(template_dir="../../etc/passwd") # -----------------------------------------------------
  • rity-audit", "template_dir": "/etc/passwd"}, ) ) assert not r["success"]
  • ": "app.py", "template_dir": "/etc/passwd"}, ) ) assert not r["success"]
  • "security", "template_dir": "/etc/passwd"}, ) ) assert not r["success"]
  • validate_file_path("/etc/passwd") def test_proc_rejected(self) -> None: with p
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: smartaimemory.com>

✓ 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 attune-help 
Create a user-friendly educational app called 'LearnSmart' that leverages the 'attune-help' package to adapt its content based on the user's level of understanding and previous interactions. This app will focus on teaching basic programming concepts to beginners but will also cater to intermediate users by providing more advanced material as they progress.

**Features:**
1. **User Profiles:** Allow users to create profiles where they can track their learning progress and preferences.
2. **Adaptive Content Delivery:** Use 'attune-help' to deliver explanations and examples at the right level of detail based on the user's interaction history and feedback.
3. **Interactive Quizzes:** Implement quizzes after each topic to test the user's understanding and adjust future content delivery accordingly.
4. **Progressive Depth:** Start with simple concepts like variables and data types, then move to loops and conditionals, and finally introduce object-oriented programming.
5. **Audience Adaptation:** Based on the user's performance and feedback, the app should adapt the pace and complexity of the content.
6. **Feedback Loop:** Incorporate a feature where users can rate the clarity of explanations, which will be used by 'attune-help' to refine future content delivery.
7. **Community Forum:** Integrate a forum where users can discuss topics, ask questions, and share resources.
8. **Gamification Elements:** Include badges and points to motivate users to continue learning.

**How 'attune-help' is Utilized:**
- Initialize 'attune-help' at the start of the app to set up the adaptive learning environment.
- Use 'attune-help' functions to analyze user interactions, such as quiz results and feedback ratings, to determine the next steps in the learning path.
- Leverage 'attune-help' to dynamically generate content that matches the user's current understanding level, ensuring the material is neither too easy nor too difficult.
- Implement a system where 'attune-help' continuously evaluates user engagement and adjusts the difficulty and depth of the content to keep the learning experience engaging and effective.

💬 Discussion Feed

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