🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to obfuscation techniques and low maintainer activity, raising concerns about its true purpose and potential for malicious intent.

High obfuscation risk
Low maintainer activity

Per-check LLM notes

Network: No network calls suggest the package does not engage in external communications, which is normal unless specific network interaction is expected.
Shell: No shell execution detected indicates the package does not execute system commands, reducing potential risks associated with command injection or privilege escalation.
Obfuscation: The code shows signs of deliberate obfuscation using base64 and zlib compression, which could be used to hide the true nature of the code or data.
Credentials: No clear patterns indicative of credential harvesting were found in the provided code snippet.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malintent.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (4969 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

64 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

.compress()) compressed = base64.b64decode(raw) decompressed = zlib.decompress(compressed) retu
ecode(raw) decompressed = zlib.decompress(compressed) return json.loads(decompressed.decode("utf-8

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-replay

Your task is to create a mini-application named 'ClaudeReplayer' using the Python package 'ai-replay'. This application will take Claude Code, Cursor, and Codex CLI session transcripts and convert them into interactive HTML replays, allowing users to relive their coding sessions as if they were live again. Here’s a detailed breakdown of what your application should do:

1. **Setup and Installation**: Begin by setting up a virtual environment and installing the 'ai-replay' package along with any other necessary dependencies.
2. **Session Input Handling**: Develop a feature that allows users to input their session transcripts either through file uploads or direct text input.
3. **Conversion Process**: Utilize the 'ai-replay' package to process these inputs and generate interactive HTML files. Ensure the HTML files include all elements from the original session such as code snippets, cursor movements, and chat interactions.
4. **Interactive Replay**: The generated HTML files should allow users to navigate through the session replay interactively. Features like play/pause, rewind/fast-forward, and zooming in on specific code segments should be included.
5. **Customization Options**: Offer customization options where users can choose themes for their replays or add comments to specific parts of the replay.
6. **Output Delivery**: Once the replay is ready, provide options for users to download the HTML file or view it directly within the application interface.
7. **Documentation and User Guide**: Finally, write comprehensive documentation and a user guide explaining how to use the application effectively, including setup instructions, input requirements, and usage examples.

This project aims to not only showcase the capabilities of the 'ai-replay' package but also provide a valuable tool for developers and learners looking to review and share their coding experiences.