aelfrice

v3.5.0 suspicious
4.0
Medium Risk

Persistent memory for AI coding agents. Local SQLite + UserPromptSubmit hook — matched beliefs in the prompt before the model sees it. Deterministic, auditable, no embeddings, no cloud.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks, but the incomplete maintainer profile and single package raises some concerns about its reliability and potential for supply-chain attacks.

  • Incomplete maintainer profile
  • Single package by maintainer
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Git commands suggest the package is checking its own version or commit hash, which is generally benign but could be part of a more complex behavior.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The maintainer has an incomplete profile and a single package, suggesting potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • > str: try: out = subprocess.run( ["git", "rev-parse", "HEAD"], check
  • str: try: return subprocess.check_output( ["git", "rev-parse", "HEAD"], text=True, stderr
  • letedProcess[str]: return subprocess.run(cmd, capture_output=True, text=True, check=False) # Per-ro
  • r={cursor}"] result = subprocess.run( args, check=True, capture_output=True, text=Tru
  • // empty', ] result = subprocess.run(args, check=True, capture_output=True, text=True) cid =
  • um) if sticky_id: subprocess.run( [ "gh", "api", "-X", "PATCH",
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: robotrocketscience.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository robotrocketscience/aelfrice appears legitimate

⚠ 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 aelfrice 
Create a Personal Knowledge Manager (PKM) application using Python that leverages the 'aelfrice' package for persistent memory storage and local processing. This PKM will allow users to store, retrieve, and manage their notes, ideas, and knowledge snippets without relying on cloud services or external embeddings. The application should have the following features:

1. **User Interface**: Develop a simple command-line interface (CLI) where users can interact with the PKM.
2. **Note Management**: Users should be able to create, read, update, and delete notes. Each note should have a title and content.
3. **Search Functionality**: Implement a search feature that allows users to find notes based on keywords within the titles or contents of the notes.
4. **Persistent Storage**: Utilize the 'aelfrice' package to store all note data locally in an SQLite database. Ensure that the application can handle data persistence effectively.
5. **Audit Trail**: Enable an audit trail for each operation performed on the notes (create, read, update, delete). This information should also be stored locally using 'aelfrice'.
6. **Local Processing**: When a user performs actions such as searching for notes, the application should process these requests locally without any dependency on external services.
7. **Deterministic Behavior**: Ensure that the behavior of the application is deterministic, meaning that given the same input and state, the application should always produce the same output.
8. **User Prompts**: Integrate a feature where users can submit prompts to the application, and the application should match these prompts against existing beliefs (notes) before executing any action. If a match is found, the application should inform the user about the existing belief.

The 'aelfrice' package should be used to manage the local storage and processing of notes and their metadata. It should ensure that all operations are handled deterministically and audited locally. Additionally, the package's ability to match user prompts against existing beliefs before executing actions should be leveraged to enhance the functionality and user experience of the PKM.