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Found 55 Skills
Agent skill for security-manager - invoke with $agent-security-manager
Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
Security patterns for autonomous trading agents with wallet or transaction authority. Covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV protection, and key handling.
Comprehensive security review framework for AI agents to audit skills, repositories, URLs, on-chain addresses, and services in adversarial environments
Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployment. Do not use for general web application security or offensive testing.
Expert infrastructure security engineer specializing in DevSecOps, cloud security, and compliance frameworks. Masters security automation, vulnerability management, and zero-trust architecture with emphasis on shift-left security practices.
Designs, deploys, and secures Google Cloud Agent Gateway solutions. Use when the user needs to configure multi-agent security, ingress (CLIENT_TO_AGENT), or egress (AGENT_TO_ANYWHERE) patterns involving Model Armor, IAP, and Agent Registry. Don't use for general Cloud Load Balancing or basic VPC setup not related to Agent Gateways.
Epistemic verification framework for AI-generated assertions. Requires evidence before acting on LLM claims about code behavior, system state, API responses, or factual statements. Use when an AI agent makes claims that will drive decisions, before acting on research results, or when an agent asserts something is true without showing evidence.
Secret safety for AWS Secrets Manager, secret management, credentials, API keys, tokens, and passwords. Prevents AI agents from directly fetching secret values and teaches runtime dynamic references with asm-exec so plaintext never enters the LLM context window.
Use when THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits. Covers payment setup, policy, session budgets, and troubleshooting. Triggers on: "my agent hit a 402 while calling an API", "a tool call returned 402 Payment Required", "my agent needs to pay for x402-protected content", "let the agent pay for content, capped at $5 per session", "set a spend limit for the agent", "ProcessPayment failed", or "why did my agent refuse to pay". Not for BUILDING payment capability for end users, including wallets and framework middleware; use agents-build and references/payments.md. For non-paid APIs via Gateway use agents-connect. For inbound auth use agents-harden. For project scaffolding use agents-get-started.
Analyze agent skills for security risks, malicious patterns, and potential dangers before installation. Use when asked to "audit a skill", "check if a skill is safe", "analyze skill security", "review skill risk", "should I install this skill", "is this skill safe", or when evaluating any skill directory for trust and safety. Also triggers when the user pastes a skill install command like "npx skills add https://github.com/org/repo --skill name". Produces a comprehensive security report with a clear install/reject verdict.
Use when deploying to production, handling sensitive data, or the workflow needs safety constraints, input validation, and security boundaries.