SkillRisk.org
Sentinel SCA
Microsoft Azure
LangChain
Auto-GPT
Snyk
LangSmith
Helicone AI
Commit Art
SkillRisk is a specialized security analysis tool designed for the AI Agent ecosystem, specifically focusing on Claude Code and Model Context Protocol (MCP) skills. As developers give AI agents more permissions (shell access, file manipulation), the risk of executing malicious code increases. SkillRisk acts as a static analysis firewall, auditing skill definitions before you install or run them. Key Features: Hook Hijacking Detection: Identifies malicious PreToolUse hooks that attempt to execute silent background commands or install malware. Permission Auditing: Flags skills requesting excessive privileges (e.g., unnecessary root/sudo access or write permissions to sensitive directories). Data Leak Prevention: Scans for hardcoded API keys, credentials, and potential data exfiltration patterns. MCP Server Integrity: Vets external MCP server configurations for known malicious endpoints. Privacy & Security: SkillRisk operates on a "Local-First" philosophy. It performs in-memory static analysis, meaning your uploaded code is processed in temporary RAM and immediately purged after the report is generated. It does not store user code. Pricing: Offers a Free Tier for basic scanning needs and a Premium plan for advanced hook redirection audits and priority support.
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SkillRisk.org's answer
SkillRisk is the first dedicated security scanner built specifically for the Claude Code and Model Context Protocol (MCP) ecosystem. Unlike general-purpose code linters, SkillRisk understands agent-specific attack vectors—such as PreToolUse hook hijacking, implicit permission leaks in JSON/YAML definitions, and data exfiltration patterns in MCP server configurations. It brings "Static Application Security Testing" (SAST) to the world of AI Agents.
SkillRisk.org's answer
Most traditional security tools audit application code but ignore the configuration layer of AI agents. You should choose SkillRisk because: Context-Aware: It detects risks specific to AI agents (e.g., giving an LLM rm -rf permissions) that standard linters miss. Pre-Runtime Safety: It allows you to audit third-party skills before you install them, preventing supply chain attacks. Privacy-First: Our "Local-First" architecture ensures your skill definitions are analyzed in-memory and never stored on our servers.
SkillRisk.org's answer
Our primary audience includes AI Engineers, DevOps professionals, and software developers who are building autonomous agents using Claude Code or implementing MCP servers. It is a must-have tool for anyone integrating community-contributed skills or third-party tools into their agent's workflow.
SkillRisk.org's answer
We built SkillRisk after realizing a terrifying gap in the AI workflow: developers scrutinize human code in Pull Requests but blindly copy-paste "Skills" that give AI agents shell access. After witnessing an incident where a malicious "Color Picker" skill silently exfiltrated credentials and caused $54,000 in cloud bills, we decided to build a "firewall" for AI skills. We treat Agent Skills as executable code that requires strict auditing.
SkillRisk.org's answer
The platform utilizes a custom-built Static Analysis Engine specifically tuned for parsing JSON, YAML, and Markdown skill definitions. It employs strictly typed rule sets to detect logic vulnerabilities and permission scopes without executing the code. The web interface is designed for zero-persistence data processing to ensure maximum security.
SkillRisk.org's answer
AI Engineers within the Anthropic developer community DevOps teams using Vercel Infrastructure developers at Nvidia Open source maintainers of MCP servers
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