AI Analysis
Final verdict: SUSPICIOUS
The package shows low individual risks in network, shell, obfuscation, and credential areas, but the metadata risk score suggests potential concerns regarding the maintainer's profile.
- Maintainer has a new or inactive account
- Lacks a proper author name
Per-check LLM notes
- Network: No network calls were detected, which is normal and expected.
- Shell: Shell execution patterns detected are likely related to performance testing or local git operations, indicating no immediate signs of malicious activity.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The maintainer has a new or inactive account and lacks a proper author name, which raises some suspicion but not enough to conclusively determine malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 6 shell execution pattern(s)
tripped stdout.""" proc = subprocess.run( ("git", *args), cwd=repo, capture_oth(tmp.name) try: subprocess.run( ( "hyperfine",r _ in range(warmup): subprocess.run( argv, check=False, capt= time.perf_counter() subprocess.run( argv, check=False, capttry: completed = subprocess.run( shlex.split(cmd_str), check=False,mitted changes.""" proc = subprocess.run( ("git", "diff-index", "--quiet", "HEAD", "--"),
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: git-pull.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository tony/agentgrep appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 agentgrep
Create a command-line utility called 'PromptExplorer' using Python that allows users to search through their local AI agent histories and prompts. This tool should leverage the 'agentgrep' package to provide read-only access to the histories of various AI agents like Codex, Claude Code, Cursor, Gemini, Grok, Pi, and OpenCode. Here’s a detailed plan on how to build this utility: 1. **Setup Environment**: Begin by setting up your Python environment. Install the necessary packages including 'agentgrep' and any other dependencies you might need. 2. **User Interface Design**: Design a simple yet effective command-line interface where users can interact with the application. The interface should allow users to specify which AI agent's history they want to search through, as well as the query terms they wish to use. 3. **Integration with 'agentgrep'**: Utilize the 'agentgrep' package to integrate with the specified AI agent's local data storage. Ensure that the integration is seamless and that the package is utilized efficiently to fetch and display relevant results. 4. **Search Functionality**: Implement a robust search functionality that allows users to perform searches based on keywords or phrases. The search should return accurate and relevant results from the agent's history. 5. **Display Results**: Once the search is executed, display the results in a readable format. Each result should include details such as the date and time of the interaction, the input prompt, and the AI agent's response. 6. **Additional Features**: - **Filtering Options**: Allow users to filter results based on specific criteria (e.g., date range). - **Export Results**: Provide an option to export the search results into a file (CSV or JSON). - **History Management**: Offer basic management functionalities such as viewing the entire history of interactions with an AI agent. 7. **Testing and Documentation**: Thoroughly test the application to ensure it works as expected. Document the setup process, usage instructions, and any troubleshooting tips. 8. **Deployment**: Package the application for easy deployment. Consider making it available on platforms like PyPI for others to install and use. By following these steps, you will create a useful and efficient tool for exploring and managing AI agent interactions.