Repozilla User Guide

This section provides basic metadata about the scanned repository:

• URL, Name, Description: Basic GitHub details.
• AI Project Purpose Summary: An AI-generated summary of what the project aims to do, based on its README and file structure. The confidence score indicates how sure the AI is.
• Key Technologies/Focus: Languages, frameworks, or domains identified by the AI.
• Creation & Update Dates: When the repository was created and last updated on GitHub.
• Fork Status: Indicates if the repository is a copy of another project.
• Branch: The specific branch that was analyzed.

Information about the repository owner and its most active contributors (up to top 3):

• Profile Details: Login, avatar, link to GitHub profile.
• Account Creation Date: When their GitHub account was created.
• Contributions to this Repo: Number of commits/contributions to the specific repository being analyzed.
• Followers/Following: Their GitHub social metrics.
• AI Note: A concise, objective insight from the AI based on their public activity related to *this* repository (e.g., "Established user, active contributor here" or "Owner with few direct commits, possibly managerial role"). These notes are based on metadata and are not judgments of skill or trustworthiness.

An AI-generated analysis of the repository's development lifecycle:

• Key Dates: Created and Last Updated dates are shown.
• Overall Activity Level: A category like "High," "Moderate," "Stagnant," "Initial Burst," etc.
• AI Activity Summary: A narrative describing the lifecycle (e.g., "Saw an initial burst then stagnated," or "Shows consistent development over years").
• Key Activity Observations: Bullet points highlighting specific findings (e.g., "Long gap between creation and update," "Owner is sole contributor").

An automated assessment of the repository's code based on general software engineering principles. This is AI-generated and not a substitute for expert human review.

• Overall Quality Score: A score from 0 (very poor) to 10 (excellent). Color-coding helps quickly identify the level.
• AI Quality Summary: A 2-4 sentence overview from the AI.
• Positive Aspects: Strengths identified in the code (e.g., "Good modularity," "Clear naming").
• Areas for Improvement: Weaknesses or potential issues (e.g., "High complexity in some functions," "Lack of comments").
• Key File Observations: Specific notes on up to 5 files that stood out, positively or negatively.

This section assesses various factors that could indicate potential scam-related risks. A higher score (out of 5) means higher potential risk.

• Overall Scam Risk Score: A holistic score from 0 (Low Risk) to 5 (High Risk). Color-coding (Green/Yellow/Red) helps visualize this.
• Granular Risk Scores: Scores (0-5) for specific categories:
  • Documentation & Hype: Quality of docs, presence of excessive hype or unrealistic claims.
  • Code Suspiciousness: Presence of potentially malicious code patterns (e.g., rug pull functions), or very little functional code.
  • Repo Activity: Risks related to repository age, update patterns (e.g., stagnation, sudden unexplained revival).
  • Contribution Patterns: Risks from owner inactivity (especially on forks), or contribution levels that seem too low for the project's scope/age.
  • Communication Channels: Risks from lack of clear communication channels, or use of anonymous/new channels.
  • Dependency Risks: Risks from using outdated, vulnerable, or obscure third-party libraries.
• Contribution Activity Analysis: AI's summary of owner/top contributor activity patterns and any notable observations.
• Flagged Patterns & Observations: Specific issues or characteristics found, like "README contains unverifiable claims," "Owner has very few commits on this old fork," or "Suspiciously recent update after long dormancy."
• Communication Channel Analysis: Summary of external links (websites, social media) and any red flags (e.g., missing channels, brand new/anonymous channels). X/Twitter handles are often extracted.
• Dependency & Library Risks: List of potential issues in project dependencies (e.g., outdated or obscure packages).
• Overall AI Scam Summary: The AI's concluding thoughts, highlighting key reasons for the assigned score.

Compares individual files in the repository against a broad database of known code. This helps identify code reuse, potential plagiarism, or reliance on common libraries.

• File Path: The path to the file within the repository.
• Similarity (%): The percentage of the file's content that matches known code. Higher percentages are highlighted (e.g., Red for ≥90%, Orange for ≥70%).
• Potential Source: If identifiable, the AI may suggest the origin of the similar code (e.g., a common library, a well-known project).
• AI Similarity Summary: The AI's overview, which often mentions if the project is a fork (expecting high similarity to its origin) and comments on contribution patterns if relevant to similarity (e.g., a fork with no new code).

An AI-driven attempt to identify how the analyzed repository might be related to other known projects or general code patterns, especially in the crypto space.

• Connections Map: A visual representation. The central dot is your analyzed repo. Other dots are potential connections. Bubble size roughly indicates similarity score. Color indicates category. Hover for details.
• Detailed Connections List:
  • Name: Name of the connected repo (e.g., 'owner/repoName') or project category (e.g., "Uniswap V3").
  • Category: Type of connection (e.g., "Closely Related Project" for forks/copies, "Well-known Crypto Project," "Smart Contract Pattern").
  • Similarity Score: A 0-1 score indicating strength of connection.
  • Details: Brief AI explanation (e.g., "Appears to be the upstream project," "Shares common smart contract logic").
  • Links are provided if a specific GitHub repository is identified.
• If the analyzed repository is a fork, its original upstream project will be listed as a "Closely Related Project" with a high similarity score.

If smart contract files (e.g., `.sol` for Solidity, `.vy` for Vyper) are detected, this section details potential issues. If no such files are found or analyzable, it will state that.

• Detected Vulnerabilities: Lists common smart contract vulnerabilities (e.g., Reentrancy, Integer Overflow) with severity (Critical, High, Medium, Low, Informational), description, and relevant file(s).
• Malicious Patterns: Identifies patterns indicative of scams (e.g., Honeypot, Rug Pull mechanisms) with descriptions and file(s).
• Standard Adherence: Checks if contracts seem to follow common token standards (e.g., ERC20, ERC721) and notes major deviations.
• Overall Smart Contract Assessment: The AI's summary of findings, risks, and contract quality from a security perspective.