flagrix
Enables AI agents to scan GitHub repositories and user profiles for malware signals before cloning, providing risk verdicts pinned to specific commits.
README
flagrix
Scan GitHub repositories and profiles for malware before you clone — from the terminal, CI, or an AI agent. The same commit-pinned verdict as the Flagrix browser extension, made callable.
npx flagrix scan https://github.com/some-org/coding-assignment
some-org/coding-assignment @ 3f9c2a1
HIGH RISK — Do not clone security score 12/100
3 files scanned · 10 dependencies · 2 issues
CRITICAL Data exfiltration patterns detected: Keylogger Pattern
assignment.js:14
14 document.addEventListener("keydown", (e) => send(e.key))
Built after real fake-recruiter campaigns ("coding assignment" repos that steal wallets, SSH keys, and browser sessions) started targeting developers.
Commands
flagrix scan <url | owner/repo> # scan a repository (--ref <branch|sha>)
flagrix scan-user <username> # score a GitHub profile for scam signals
flagrix mcp # MCP server (stdio) for AI agents
Exit codes
| code | meaning |
|---|---|
| 0 | low risk |
| 1 | scan failed |
| 2 | medium risk — review before proceeding |
| 3 | high risk — do not clone |
--json (automatic when stdout is piped) emits the full result. The verdict is
pinned to the scanned commit (commitSha in the JSON): every file is read at
that SHA, so a push mid-scan or after the verdict can't silently invalidate it.
AI agents
claude mcp add flagrix -- npx -y flagrix mcp
Tools: scan_github_repo, scan_github_user. A Claude Code hook that gates every
git clone on a scan ships in hooks/ — see
docs/agent-gating.md.
Tokens & rate limits
Unauthenticated scans use GitHub's 60 req/h budget (a scan issues one request per
scanned file, up to ~50). Set GITHUB_TOKEN (or FLAGRIX_GITHUB_TOKEN, or
--token) to raise it to 5,000/h and to scan private repositories.
Privacy
Fully local. No telemetry, no accounts, no Flagrix backend — the only network calls go to the GitHub/npm APIs and the public detection-rules repository (signature refresh, cached 6 h, with a bundled offline snapshot).
How it works
Scanning logic lives in @flagrix/scanner-core (MIT), signatures in flagrix-detection-rules (MIT) — the same engine and rules the browser extension uses. Verdicts are risk assessments, not definitive fraud determinations; always verify through official channels.
AI Disclosure
This project leverages Claude AI for boilerplate generation, test-suite expansion, and optimization. All AI-generated code is strictly reviewed, refactored, and verified by human maintainers before merging.
License
MIT — see LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。