agents-shipgate

v0.11.0 safe
3.0
Low Risk

The deterministic merge gate for AI-generated agent capability changes. Agent release readiness for tool-using AI agents. CLI + GitHub Action. Scans MCP, OpenAPI, OpenAI Agents SDK, Anthropic, Google ADK, LangChain, CrewAI, OpenAI API, Codex plugin, n8n.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across most categories, with only shell execution and metadata raising minor concerns. There's no indication of malicious behavior.

  • Low network and obfuscation risks.
  • No credential harvesting detected.
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Shell execution is primarily related to Git operations and performance measurement, suggesting the package may be used in development or testing contexts but could potentially pose a risk if misused.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The author has only one package, suggesting a new or less active account which may warrant further investigation.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/ThreeMoonsLab/agents-shipgate/wiki
  • Detailed PyPI description (34732 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 235 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in ThreeMoonsLab/agents-shipgate
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • ime.perf_counter() proc = subprocess.run( [ sys.executable, "-m",
  • , "diff", "HEAD"] names = subprocess.run(names_cmd, **run_kwargs) body = subprocess.run(body_cmd,
  • cmd, **run_kwargs) body = subprocess.run(body_cmd, **run_kwargs) paths = [line for line in names.
  • revspec: untracked = subprocess.run( ["git", "ls-files", "--others", "--exclude-stan
✓ 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

Repository ThreeMoonsLab/agents-shipgate appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Agents Shipgate 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 agents-shipgate 
Develop a mini-application named 'AgentGateMonitor' that leverages the 'agents-shipgate' package to monitor and manage the release readiness of AI-generated agents across various platforms. This application will serve as a dashboard tool for developers and managers to track the status of their agents in different environments, ensuring they are ready for deployment based on predefined criteria.

**Core Functionality:**
1. **Agent Status Monitoring:** The application should continuously scan multiple platforms (MCP, OpenAPI, OpenAI Agents SDK, Anthropic, Google ADK, LangChain, CrewAI, OpenAI API, Codex plugin, n8n) for any updates or changes in the capabilities of AI-generated agents.
2. **Release Readiness Assessment:** Utilize the deterministic merge gate feature of 'agents-shipgate' to evaluate if the current version of each agent meets the specified release criteria. This includes checking for stability, performance metrics, and compliance with ethical guidelines.
3. **Dashboard Interface:** Provide a user-friendly web interface where users can view the real-time status of all monitored agents, including details such as last update time, current status (ready/not ready), and reasons for not being ready if applicable.
4. **Notification System:** Implement a notification system that alerts users via email or Slack when an agent's status changes or when new agents are detected that need to be monitored.
5. **Customization Options:** Allow users to customize the release criteria and monitoring intervals according to their specific needs.

**Utilizing 'agents-shipgate':** 
- Integrate 'agents-shipgate' into your application to handle the complex task of merging and validating capability changes in AI-generated agents. This package will be crucial in determining whether an agent is ready for deployment by applying deterministic rules to the data collected from different platforms.
- Use the CLI provided by 'agents-shipgate' for initial setup and configuration, and then leverage its GitHub Action capabilities to automate parts of the monitoring process.

This project aims to streamline the management of AI-generated agents, making it easier for teams to ensure their tools are always up-to-date and ready for use.