axiom-stack

v0.5.0 safe
4.0
Medium Risk

Verifiable on-chain attestations for AI agents — Solana-anchored equity + crypto data oracle.

🤖 AI Analysis

Final verdict: SAFE

The package appears safe based on the analysis notes. It has a low risk score due to the absence of shell execution, obfuscation, or credential harvesting patterns. However, there are some concerns regarding incomplete maintainer information and missing repository.

  • Incomplete maintainer information
  • Missing repository
Per-check LLM notes
  • Network: The use of httpx.Client and AsyncClient suggests the package is performing network requests, which is not inherently suspicious but should be reviewed to ensure it aligns with the package's intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is not found and the maintainer's information is incomplete, raising some concerns.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

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

Some documentation present

  • Documentation URL: "Documentation" -> https://axiomstack.dev/developers/api
  • Detailed PyPI description (5398 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
  • 66 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • self._http = http_client or httpx.Client(timeout=self._timeout, base_url=self.base_url) self.
  • self._http = http_client or httpx.AsyncClient(timeout=self._timeout, base_url=self.base_url) self.
  • e client = http_client or httpx.Client(timeout=timeout) try: last_status = 0 la
  • e client = http_client or httpx.AsyncClient(timeout=timeout) try: last_status = 0 la
  • e client = http_client or httpx.Client(timeout=30.0) try: verify_resp = _facilitator_po
  • e client = http_client or httpx.AsyncClient(timeout=30.0) try: verify_resp = await _facilita
✓ 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: axiomstack.dev>

✓ 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 4.0

2 maintainer concern(s) found

  • 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 axiom-stack 
Develop a mini-application called 'CryptoAttestor' that leverages the 'axiom-stack' Python package to create verifiable on-chain attestations for AI agents regarding cryptocurrency data. This application will serve as a bridge between AI-driven analysis tools and blockchain technology, ensuring the integrity of the data provided by these AI agents.

### Features:
1. **AI Data Analysis**: Integrate an AI model that analyzes real-time cryptocurrency market data (prices, volumes, trends) from popular exchanges like Binance or Coinbase.
2. **Attestation Generation**: Use 'axiom-stack' to generate verifiable attestations that prove the authenticity of the analyzed data on the Solana blockchain. These attestations should include timestamps, cryptographic signatures, and relevant data points.
3. **Data Verification**: Implement a feature that allows users to verify the attested data directly through the application or via a web interface, ensuring transparency and trust.
4. **User Interface**: Develop a simple yet effective user interface where users can input their queries about specific cryptocurrencies and receive both the AI analysis and the corresponding attestation details.
5. **Security Measures**: Ensure that all interactions with the Solana blockchain are secure and that sensitive information is protected using best practices in cryptography and data security.
6. **Documentation & Tutorials**: Provide comprehensive documentation and tutorials for integrating 'axiom-stack' into other applications or services.

### Utilization of 'axiom-stack':
- **Integration**: Begin by setting up the 'axiom-stack' package in your Python environment. Follow the official documentation to understand how to initialize the stack and connect to the Solana network.
- **Data Attestation**: Use 'axiom-stack' functions to create and sign attestations for the AI-generated data. Each attestation should contain a unique identifier, timestamp, and cryptographic signature.
- **Blockchain Interaction**: Employ 'axiom-stack' methods to submit these attestations onto the Solana blockchain, ensuring they are stored immutably and verifiably.
- **Verification Process**: Implement a verification process within the application that allows users to check the validity of any given attestation against the data recorded on the blockchain.

This project aims to demonstrate the potential of combining AI-driven insights with blockchain technology to enhance the reliability and transparency of financial data analysis.

💬 Discussion Feed

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