🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low technical risks but exhibits significant metadata anomalies such as a lack of maintainer history and low repository engagement, raising concerns about its legitimacy and potential for supply-chain attacks.

Low repository engagement
Lack of maintainer history

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
Metadata: The package shows several red flags including lack of maintainer history, low repository engagement, and potential for a new or inactive account.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: meok.ai>

✓ 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 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agent-replay-debugger-mcp

Create a Python-based application that leverages the 'agent-replay-debugger-mcp' package to enable developers to debug and audit their autonomous agents effectively. This application should allow users to record the actions of their agents in real-time, store these actions for later replay, and provide deterministic playback to help identify and resolve issues within the agent's decision-making process. Additionally, the app should support exporting the recorded actions as signed audit evidence for compliance and transparency purposes.

Key Features:
1. Real-time Recording: Implement a feature that captures each action taken by the agent during its operation. Ensure that the recording includes all relevant data points such as timestamps, states, and outcomes.
2. Deterministic Playback: Develop functionality that allows users to replay the agent's actions exactly as they occurred, ensuring that every step is reproducible and consistent. This will aid in debugging complex scenarios where the issue might not be immediately apparent.
3. Debugging Interface: Provide a user-friendly interface for stepping through the agent's actions one by one. Users should be able to pause, resume, and skip steps to inspect the state of the agent at any given point.
4. Audit Evidence Export: Enable the export of recorded actions as signed audit evidence. This feature should generate tamper-proof records that can be reviewed for compliance checks and audits.

Utilization of 'agent-replay-debugger-mcp':
- Use the package's core capabilities to handle the recording and replay functionalities seamlessly. Ensure that the application integrates with the package's API to capture and store agent actions efficiently.
- Leverage the deterministic replay feature provided by the package to offer precise control over the playback process. This ensures that developers can recreate any scenario exactly as it happened, facilitating thorough analysis.
- Employ the audit evidence export mechanism offered by the package to produce reliable and verifiable logs of the agent's activities. This is crucial for maintaining transparency and accountability in the use of autonomous agents.