🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate network and shell execution risks, with no clear evidence of malicious intent. However, the combination of these factors raises suspicion.

moderate network risk due to complex API interactions
high shell risk due to subprocess usage

Per-check LLM notes

Network: Network calls appear to be for legitimate API interaction, but the use of multiple clients and varying timeouts may indicate complex or unusual behavior.
Shell: Execution of scripts and processes via subprocess suggests potential for local command execution, which could be misused for unauthorized actions.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The package shows signs of low maintenance and potentially risky links, but there's no clear evidence of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

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

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (20306 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 5.0

Partial type annotation coverage

334 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

try: with socket.create_connection((host, port), timeout=1): return exc
eption | None = None with httpx.Client(timeout=2.0, trust_env=False) as client: while time.
t_seconds=60.0) with httpx.Client(base_url=base_url, timeout=10.0, trust_env=False) as client:

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

gentseek" completed = subprocess.run( [sys.executable, "-m", "agentseek_api.cli", "do
log_output: process = subprocess.Popen( [ sys.executable,
put: seekdb_process = subprocess.Popen( [sys.executable, str(ROOT_DIR / "scripts" / "se
: serve_process = subprocess.Popen( [ sys.executable,
None) -> int: completed = subprocess.run(command, env=env, cwd=cwd, check=False) return completed
, str], cwd: str): return subprocess.Popen(command, env=env, cwd=cwd) def _run_managed_dev_server(

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 10.0

Found 5 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:2024/health
Non-HTTPS external link: http://127.0.0.1:2024/info
Non-HTTPS external link: http://127.0.0.1:2024/openapi.json
Non-HTTPS external link: http://127.0.0.1:2024/mcp
Non-HTTPS external link: http://127.0.0.1:2024/a2a/{assistant_id}

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentseek-api

Create a real-time data monitoring and alerting system using the 'agentseek-api' Python package. This system will leverage the capabilities of the OceanBase database to track specific metrics and trigger alerts based on predefined thresholds. The project aims to demonstrate the integration of 'agentseek-api' for efficient data checkpointing and monitoring functionalities.

**Step 1:** Set up your development environment by installing Python and the necessary libraries including 'agentseek-api'. Ensure you have access to an OceanBase database instance.

**Step 2:** Design the architecture of your system. It should include components for data collection, processing, storage, and alert generation.

**Step 3:** Implement the data collection module. This module should periodically fetch data from various sources (e.g., logs, APIs, sensors) and store it in the OceanBase database using the 'agentseek-api' package for efficient checkpointing.

**Step 4:** Develop the processing module which analyzes the collected data against predefined rules and thresholds. Use 'agentseek-api' to ensure data integrity and consistency during processing.

**Step 5:** Create the alerting module. When certain conditions are met (based on the analysis), this module should send out notifications via email, SMS, or other communication channels.

**Suggested Features:**
- Real-time data visualization dashboard.
- Configurable alert thresholds.
- Historical data analysis and reporting.
- Integration with external systems for automated responses.
- User-friendly interface for setting up new monitoring tasks.

In each step, focus on utilizing the 'agentseek-api' package effectively to handle data checkpointing, ensuring data is stored reliably and efficiently in the OceanBase database.