🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to its network activity and the lack of associated metadata credibility.

network risk due to potential unauthorized data transmission
metadata risk from a new or inactive maintainer account and missing git repository

Per-check LLM notes

Network: The package makes network calls that could be indicative of external service interactions, possibly for scoring or similar operations, raising concerns about potential unauthorized data transmission.
Shell: No shell execution patterns were detected within the provided code snippets.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package maintainer has a new or inactive account and the git repository is not found, which raises some concerns but does not conclusively indicate malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

ip('/')}/v1/score" req = urllib.request.Request( url, data=payload, headers=
) try: with urllib.request.urlopen(req, timeout=30) as resp: data = _json.l
/div> """ async with httpx.AsyncClient() as client: response = await client.post(
try: response = httpx.get( candidate, timeout=timeout,

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Chinemeze" 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 agentcard-disco

Create a mini-application called 'AgentCardOptimiser' that leverages the 'agentcard-disco' package to help users optimize their A2A Agent Cards for better discoverability. The application should allow users to input details of their current Agent Card and receive recommendations on how to improve it based on various scoring metrics provided by the 'agentcard-disco' package.

Step-by-Step Instructions:
1. Begin by setting up a user-friendly interface where users can input the details of their existing Agent Card such as name, description, skills, and other relevant metadata.
2. Use the 'agentcard-disco' package to score the entered Agent Card data based on predefined criteria for discoverability.
3. Display the score along with a breakdown of strengths and weaknesses in the card’s current configuration.
4. Provide tailored suggestions for improvement, highlighting specific areas where changes could enhance discoverability.
5. Include an option for users to save their optimized Agent Card configurations or export them for use elsewhere.
6. Optionally, integrate a feature that allows users to compare different versions of their Agent Card to see improvements over time.

Suggested Features:
- Interactive form for entering Agent Card details.
- Real-time scoring and feedback during the input process.
- Detailed report generation upon completion.
- Comparison tool for tracking progress.
- Export functionality for optimized configurations.

Utilization of 'agentcard-disco':
- Import the 'agentcard-disco' package at the start of your application.
- Utilize its scoring functions to evaluate the inputted Agent Card data.
- Leverage its optimization capabilities to suggest improvements.
- Integrate any additional features from the package that enhance the user experience.