🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high risks associated with executing shell commands and network communications, which could potentially be exploited for malicious purposes. Despite no direct evidence of malicious activity, the overall risk is elevated due to these factors.

High shell execution risk
Significant network communication risk

Per-check LLM notes

Network: The use of HTTP requests and async clients suggests network communication, which could be legitimate but might also indicate potential C2 or data exfiltration activities.
Shell: Executing shell commands without proper sanitization or user consent is highly suspicious and could be used to perform unauthorized actions on the system.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The repository's low engagement and the maintainer's limited package history suggest potential unreliability.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
6 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/santino456/AgentRoom#readme
Detailed PyPI description (10073 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

103 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 93 commits in santino456/AgentRoom
Single author but highly active (93 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

None): try: req = urllib.request.Request(f"{base_url}{path}", method="GET") req.add_h
add_header(k, v) with urllib.request.urlopen(req, timeout=5) as resp: return json.loa
port httpx async with httpx.AsyncClient(timeout=10) as client: for cfg in configs:
前成员") return r = httpx.delete(f"{BASE_URL}/rooms/{room_id}/members/{my_id}", headers=heade
# Update description r = httpx.put( f"{BASE_URL}/rooms/{room_id}/members/{my_id}/descri

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

count - 1: proc = subprocess.Popen(cmd) click.echo(f"🎧 [{agent}] 监听器 #{i+1} 启动 (PI
os.chdir(backend_dir) subprocess.run(cmd) except KeyboardInterrupt: click.echo("\n👋

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AgentRoom Contributors" 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 agentroom

Create a collaborative puzzle-solving game using the 'agentroom' package. This game will simulate a room escape scenario where players work together to solve puzzles and unlock the door to escape. The game will involve multiple agents, each representing a different player or NPC (Non-Player Character), collaborating in real-time to achieve common goals. Here’s how you can build it:

1. **Setup**: Begin by installing the 'agentroom' package and setting up your environment. Ensure you have Python installed and create a virtual environment for your project.
2. **Game Design**: Define the layout of the room, including obstacles, puzzles, and items that players need to interact with to progress. Each puzzle could represent a challenge that requires logical thinking, teamwork, or specific knowledge.
3. **Agents Creation**: Use 'agentroom' to create multiple agents. Each agent represents either a human player or an NPC. Agents should be capable of communicating with each other and sharing information about their discoveries within the room.
4. **Puzzle Mechanism**: Implement various puzzles that require collaboration between agents. For example, one agent might find a clue while another needs to decipher it. Puzzles should vary in difficulty and type, such as riddles, logic problems, or physical challenges.
5. **Interaction Interface**: Develop a simple interface through which agents can interact with the game world and communicate with each other. This could be a console-based interface or a more graphical one depending on your preference and technical capabilities.
6. **Real-Time Collaboration**: Utilize 'agentroom's real-time collaboration features to ensure that all actions and communications between agents are synchronized. This will enhance the realism and urgency of the escape scenario.
7. **Win Condition**: Define the conditions under which players win the game. This could be unlocking a secret code, finding a hidden key, or solving all puzzles correctly.
8. **Testing & Refinement**: Test the game thoroughly to ensure that all puzzles are solvable and that the collaboration mechanics work smoothly. Gather feedback from testers and refine the game based on their input.
9. **Deployment**: Once satisfied with the game, deploy it so that others can play and enjoy the collaborative experience. Consider hosting it online if possible, allowing for remote play.

Suggested Features:
- Detailed room exploration with dynamic descriptions based on agent actions.
- A variety of puzzles that cater to different types of intelligence (logical, spatial, linguistic).
- NPCs that provide hints or distractions, adding complexity to the game.
- A leaderboard to track scores and completion times.
- Customizable rooms and puzzles for replayability and personalization.

By following these steps and utilizing the 'agentroom' package effectively, you'll create an engaging and interactive collaborative puzzle game that showcases the power of multi-agent systems.