autocontext

v0.6.0 suspicious
4.0
Medium Risk

autocontext control plane for iterative strategy evolution.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network and shell execution activities, although no severe threats like credential harvesting or advanced obfuscation techniques are observed.

  • Network calls to an external URL
  • Execution of shell commands
Per-check LLM notes
  • Network: The network calls to a specific URL other than openai suggest potential interaction with an external service which could be legitimate but requires verification of its purpose and security.
  • Shell: Executing shell commands within a package can be risky if not properly controlled and documented, especially regarding the command's purpose and input validation.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The presence of a non-HTTPS external link and an author with a potentially new or inactive account raises some concerns, but there are no clear signs of typosquatting or other severe issues.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/greyhaven-ai/autocontext/tree/main/autoco
  • Detailed PyPI description (30297 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 367 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in greyhaven-ai/autocontext
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • sts.get/post, httpx.get/post, urllib.request.urlopen) to a hardcoded non-openai URL *in the SDK subt
  • ry: http_client = httpx.Client(transport=transport, base_url="https://api.anthropic.com")
  • i_key="test-key", http_client=httpx.Client(transport=transport), max_retries=0) client = in
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • .CompletedProcess: return subprocess.run(cmd, capture_output=True, text=True, **kwargs) if shutil.w
  • he .whl path.""" result = subprocess.run( ["uv", "build", "--wheel", "--out-dir", str(dest)],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8000/`
✓ Git Repository History

Repository greyhaven-ai/autocontext 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 autocontext 
Create a Python-based mini-application that leverages the 'autocontext' package to implement an iterative strategy evolution system for optimizing a simple game-playing algorithm. This application will focus on evolving strategies for playing a basic game of tic-tac-toe against various opponents, including both random players and more sophisticated AI players. The goal is to develop a robust AI player that can adapt its strategy based on the opponent's playstyle over multiple iterations.

Steps to complete the project:
1. Set up a tic-tac-toe game environment within your application. This includes defining the game board, rules, and basic functions for making moves and checking game states.
2. Integrate the 'autocontext' package to manage the iterative process of evolving the AI's strategy. Use it to track context-specific information about each game played, such as the sequence of moves, outcomes, and opponent behavior patterns.
3. Develop a simple initial strategy for the AI player. This could be a basic heuristic or a set of predefined rules for choosing moves.
4. Implement a feedback loop where the AI plays multiple games against different types of opponents (random, fixed strategy, etc.). After each game, use the 'autocontext' package to analyze the game data and adjust the AI's strategy accordingly.
5. Incorporate metrics to evaluate the performance of the AI's evolving strategy. These could include win rates, move efficiency, and adaptability scores.
6. Visualize the progression of the AI's learning curve using graphs or charts to show how its performance improves over time.
7. Optionally, extend the application by adding features such as:
   - A user interface for human players to interact with the AI.
   - Support for different game modes (e.g., single-player, multiplayer).
   - Advanced analytics to provide insights into specific aspects of the AI's decision-making process.

Throughout the development process, make sure to utilize the 'autocontext' package effectively to capture and utilize contextual information from each game iteration, enabling the AI to learn and adapt dynamically.

💬 Discussion Feed

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