🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate metadata risk due to potential low activity and lack of maintainer information, which raises some suspicion. However, other specific risks such as network, shell, obfuscation, and credential risks are all very low.

Metadata risk at 6/10
Low activity and lack of maintainer details

Per-check LLM notes

Network: Network calls are typical for packages that interact with external services or APIs.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
Metadata: The package shows signs of potential low activity and lack of maintainer information, raising suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

client = self._client or httpx.AsyncClient(timeout=timeout) try: if provider == "an
" try: async with httpx.AsyncClient(timeout=_HTTP_TIMEOUT) as client: res = await cl
}) client = httpx.AsyncClient() with patch.object(client, "post", new=AsyncMock(si
"anthropic") client = httpx.AsyncClient() with patch.object(client, "post", new=AsyncMock(re

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ai-manifests.org>

✓ 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 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 adp-agent

Create a mini-application called 'Calibration Hub' using the Python package 'adp-agent'. This application will serve as a platform where multiple agents can deliberate on various topics, sign their proposals, record the outcomes of these deliberations, and periodically publish their calibrated views in the form of signed snapshots. Here’s a detailed breakdown of what your application should include:

1. **Agent Registration**: Users/agents must be able to register on the platform, providing necessary credentials for authentication.
2. **Topic Discussion Forums**: Agents can create discussion forums around specific topics. Each forum should allow agents to post proposals, comment on others’ proposals, and vote on them.
3. **Proposal Management**: Implement functionality for agents to draft, sign, and submit proposals. Once a proposal is submitted, it should be visible to all agents in the forum.
4. **Outcome Journaling**: After deliberation, agents can record the outcome of the discussion. This could be a consensus decision or a summary of the discussion points.
5. **Calibration Snapshots**: Periodically, agents can take snapshots of their current calibrated views based on the discussions. These snapshots should be cryptographically signed to ensure authenticity.
6. **Snapshot Publishing**: Provide a mechanism for agents to publish their signed calibration snapshots. These snapshots can then be reviewed by other agents on the platform.
7. **User Interface**: Develop a simple yet effective user interface where agents can easily interact with the platform, including registering, posting, voting, and publishing.
8. **Security Measures**: Ensure that all transactions (posting, signing, etc.) are secure and authenticated using the capabilities provided by the 'adp-agent' package.

To achieve these functionalities, you will heavily rely on the 'adp-agent' package for handling agent deliberation protocols, signing mechanisms, and snapshot publication processes. Additionally, utilize FastAPI for building the backend server to handle RESTful API requests efficiently. Use SQLAlchemy for database management to store agent details, forum posts, proposals, outcomes, and snapshots. Finally, develop a frontend using HTML/CSS/JavaScript to provide an interactive experience for agents.