agricultural-insurance-best-practices
Supports applying best practices in agricultural insurance.
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Test AI agent systems for tool abuse, unauthorized actions, privilege escalation through tool chaining, and safety bypass via agentic workflows. Use this skill when assessing autonomous AI agents that use tool-calling (function calling, plugins, actions) to interact with external systems. Covers multi-step attack chains, implicit trust exploitation, and capability boundary testing for AI agents.
Provides production-grade AI agent patterns with MCP integration and agentic RAG. Covers handoff orchestration, multilayer guardrails, and observability.
Guides the architecture and implementation of autonomous AI agents. Covers tool use, function calling, memory systems, planning strategies, and multi-agent orchestration.
Start and run a listen-first braindump session for tickets, issues, ADRs, worktasks, PR descriptions, requirements, designs, or implementation plans. Use when the user says they want to braindump, think out loud, capture rough requirements, or provide context incrementally before asking the agent to synthesize, update a ticket, create docs, or perform implementation work.
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Exploit AI assistants equipped with web-browsing capabilities or internal API plugins to perform Server-Side Request Forgery (SSRF). This skill details injecting prompts that force the LLM to request sensitive internal endpoints, such as underlying cloud metadata services or internal networks.
Execute and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.
Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.
Covers AI governance, EU AI Act compliance, and OWASP LLM security. Addresses responsible AI practices for GitHub Copilot agents.
Bypass AI safety filters by encoding malicious prompts using ciphers and obfuscation techniques (e.g., Base64, ROT13, Leetspeak, Morse code). This skill exploits the gap where the LLM can decode the request, but intermediate keyword-based safety classifiers cannot.
Execute sophisticated Prompt Injection and Jailbreak techniques against Large Language Models (LLMs) to bypass safety filters, extract system prompts, and manipulate the AI's output to perform malicious or disallowed actions.
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