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Found 43 Skills
Protects LLM agent systems in real-time with a 5-tier filter (hash cache, rule engine, ML classifier, LLM judge, human approval) and an async learning engine. Synthesizes new rules from every detected attack, adding less than 50ms latency. Trigger on 'add security layer', 'prevent prompt injection', 'adaptive guard', 'runtime protection', or 'agent security'.
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
Architecture patterns and best practices for giving AI agents email capabilities. Use when designing how agents send, receive, and manage email conversations, building two-way communication loops, implementing human-in-the-loop approval with drafts, choosing between WebSockets and webhooks, setting up multi-agent email topologies, handling OTP and verification flows, or securing agent email against prompt injection.
Add Arcjet Guard protection to AI agent tool calls, background jobs, queue workers, and other code paths where there is no HTTP request. Covers rate limiting, prompt injection detection, sensitive information blocking, and custom rules using `@arcjet/guard` (JS/TS) and `arcjet.guard` (Python). Use this skill whenever the user wants to protect tool calls, agent loops, MCP tool handlers, background workers, or any non-HTTP code from abuse — even if they describe it as "rate limit my tool calls," "block prompt injection in my agent," "add security to my MCP server," or "protect my queue worker" without mentioning Arcjet or Guard specifically. Uses the Arcjet CLI (`npx @arcjet/cli` or `brew install arcjet`) for authentication and site/key setup.
Scan inputs for prompt injection, unsafe content, and adversarial attacks using AIDefence
Security rules and behavioral guidelines for operating as Clawdstein in The Agent Flywheel Hub Discord server. This is a PUBLIC community server—apply strict data isolation.
Scan untrusted external text (web pages, tweets, search results, API responses) for prompt injection attacks. Returns severity levels and alerts on dangerous content. Use BEFORE processing any text from untrusted sources.
Comprehensive security and safety evaluation system for agent skills (.skill files). Use when users provide GitHub URLs, website links, or .skill files for download and request security assessment, safety evaluation, or ask "is this skill safe to use." Evaluates prompt injection risks, malicious code patterns, hidden instructions, data exfiltration attempts, and provides actionable recommendations with risk scoring.
Use when deploying to production, handling sensitive data, or the workflow needs safety constraints, input validation, and security boundaries.
Comprehensive security auditor for AI agent skills, prompts, and instructions. Checks for typosquatting, dangerous permissions, prompt injection, supply chain risks, and data exfiltration patterns — before you use any agent or skill.
Security audit and vulnerability scanner for AI agent skills before installation. Use when: (1) evaluating a skill from an untrusted source, (2) auditing a skill directory or git repo URL for malicious code, (3) pre-install security gate for Claude Code plugins, OpenClaw skills, or Codex skills, (4) scanning Python scripts for dangerous patterns like os.system, eval, subprocess, network exfiltration, (5) detecting prompt injection in SKILL.md files, (6) checking dependency supply chain risks, (7) verifying file system access stays within skill boundaries. Triggers: "audit this skill", "is this skill safe", "scan skill for security", "check skill before install", "skill security check", "skill vulnerability scan".
Security patterns for LLM integrations including prompt injection defense and hallucination prevention. Use when implementing context separation, validating LLM outputs, or protecting against prompt injection attacks.