alcove-search

v0.4.0 safe
2.0
Low Risk

Local-first document retrieval. Your data never leaves your disk.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with no suspicious activity detected. The presence of CI badges suggests active development and maintenance.

  • Active continuous integration
  • No direct evidence of malicious intent

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 33 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 33 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5588 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

  • 121 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in Spitfire-Cowboy/alcove
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • de("utf-8") request = urllib.request.Request( f"{self.base_url}{path}", d
  • POST", ) with urllib.request.urlopen(request, timeout=self.timeout) as response:
  • / entry["filename"] with urllib.request.urlopen(entry["url"]) as resp: data = resp.read()
  • / entry["filename"] with urllib.request.urlopen(entry["url"], timeout=30) as resp: data = re
  • rns_dict(ec): with patch("urllib.request.urlopen", return_value=_mock_response({"ok": True})):
  • ult_list(ec): with patch("urllib.request.urlopen", return_value=_mock_response(_SEARCH_RESPONSE)):
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • sys.exit(1) subprocess.check_call([sys.executable, str(script_path)]) def _add_search_parser
  • lp_exits_zero(): result = subprocess.run( [sys.executable, "-m", "alcove", "--help"],
  • ompatibility.""" result = subprocess.run( [sys.executable, "-m", "alcove", "query", "--help"]
  • lcove_version(): result = subprocess.run( [sys.executable, "-m", "alcove", "--version"],
  • e --mode.""" result = subprocess.run( [sys.executable, "-m", "alcove", "search", "--h
  • e --mode.""" result = subprocess.run( [sys.executable, "-m", "alcove", "query", "--he
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • rror): reg.get("../etc/passwd") def test_remove_with_traversal_name_raises(self, tmp
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Spitfire-Cowboy/alcove 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 alcove-search 
Create a personal knowledge management system called 'AlcoveNote' using the Python package 'alcove-search'. This system will allow users to store, organize, and search through their notes and documents locally on their device. Here are the steps and features you should include:

1. **Setup**: Install 'alcove-search' and other necessary Python packages such as 'Flask' for the web interface.
2. **User Interface**: Develop a simple yet intuitive user interface where users can add new notes/documents, edit existing ones, and delete them if needed.
3. **Document Storage**: Implement a feature that allows users to upload documents of various types (text files, PDFs, etc.) into a local directory on their machine.
4. **Search Functionality**: Utilize 'alcove-search' to enable full-text search across all stored documents. Users should be able to type in keywords and get relevant snippets back from the documents.
5. **Tagging System**: Allow users to tag documents for better organization. Tags should be searchable via 'alcove-search'.
6. **Security**: Ensure that all data remains on the user's local machine and never gets uploaded to any server. Emphasize the privacy aspect of 'alcove-search'.
7. **Export/Import**: Provide functionality for users to export their notes/documents into a portable format like JSON or CSV, and also import previously exported data.
8. **Backup**: Include an option for users to create backups of their local database of notes/documents.

The goal is to create a fully functional, user-friendly, and secure personal knowledge management tool that showcases the benefits of 'alcove-search' for local-first applications.

💬 Discussion Feed

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