archiver-rag

v1.0.0 suspicious
4.0
Medium Risk

Semantic RAG for Obsidian vaults via MCP

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate suspicion due to shell execution risks, despite low scores in network, obfuscation, and credential risks. Further investigation is recommended.

  • Shell risk identified
  • No other significant risks detected
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell executions appear to be related to package management and logging, but could indicate potential for unauthorized system changes.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 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 (9046 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

  • 37 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

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • """ import subprocess subprocess.run(["tail", "-f", "/tmp/archiver-rag.log"]) @app.command() def
  • f plist.exists(): subprocess.run(["launchctl", "unload", str(plist)], capture_output=True)
  • .startswith("linux"): subprocess.run(["systemctl", "--user", "disable", "--now", "archiver-rag"],
  • def _get_exe(): result = subprocess.run(["which", "archiver-rag"], capture_output=True, text=True)
  • ATH.write_text(plist) subprocess.run(["launchctl", "load", str(PLIST_PATH)]) print("[gree
  • h.write_text(service) subprocess.run(["systemctl", "--user", "enable", "--now", "archiver-rag"])
✓ 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 10.0

5 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Package is very new: uploaded 3 day(s) ago
  • 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 archiver-rag 
Your task is to create a Python-based mini-application that integrates the 'archiver-rag' package to enhance the functionality of Obsidian vaults by enabling semantic retrieval and augmentation using Multi-Context Prompting (MCP). This application will serve as a powerful tool for researchers, writers, and knowledge workers who rely on Obsidian for their daily note-taking and information management needs.

The application should be designed to perform the following tasks:
1. Connect to an existing Obsidian vault and index its contents for efficient semantic search.
2. Allow users to query the indexed data using natural language queries.
3. Use the 'archiver-rag' package to process these queries and return relevant information from the vault, enriched with additional context and insights through MCP.
4. Provide a user-friendly interface (CLI or GUI) where users can input their queries and view the results.
5. Implement a feature that allows users to save frequently accessed queries for quick access.
6. Ensure that the application is secure, respecting the privacy and confidentiality of the vault's data.

Suggested Features:
- Real-time indexing updates as new notes are added to the vault.
- Ability to filter search results based on date, tags, or specific note types.
- Integration with external APIs for more comprehensive information retrieval.
- Support for multiple vaults and switching between them seamlessly.
- Export search results to common formats like PDF, CSV, or HTML.

How to Utilize 'archiver-rag':
- Use the package's capabilities to preprocess and understand the natural language queries posed by users.
- Leverage the semantic search functionalities provided by 'archiver-rag' to fetch and rank relevant notes from the vault.
- Employ MCP techniques to enrich the search results with contextual information, making the retrieved data more valuable and actionable for the user.
- Explore advanced features of 'archiver-rag', such as summarization and question answering, to further enhance the application's utility.

Your goal is to develop a fully functional application that not only integrates seamlessly with Obsidian but also pushes the boundaries of what is possible with semantic retrieval and augmentation in personal knowledge management systems.

💬 Discussion Feed

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