Skill UI

This skill provides defense-in-depth security controls for AI agents, covering prompt injection detection, input validation, tool abuse prevention, data exfiltration mitigation, and privilege escalation protection. It includes threat modeling with STRIDE, code examples for input sanitization, and guidance for secure agent deployment.

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Ensures graceful degradation and clear failure modes. Capable of agent architecture design, tool calling, memory management, and complex task decomposition. Includes knowledge of ReAct loops, Plan-and-Execute patterns, and safety guardrails.

Secure AI coding agents with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments. Covers Claude Code, Cursor, Copilot, and Codex security implementations including CLAUDE.md configuration, command allowlists, filesystem access controls, and pre-commit hooks for secret scanning.

Remote MCP server that lets AI agents search live AI/ML job listings, inspect companies and roles, match candidates to jobs, and retrieve salary or market statistics.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

This toolkit provides six expert-level workflows for AI engineering, including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior partners by offering structured methodologies, quantitative scoring, and decision frameworks. Suitable for optimizing LLM systems, designing secure agents, and planning product strategy before development.

Structures a bounded spec-build-review development loop for agentic workflows, with explicit scope, stop conditions, and human approval gates for risky work.

This comprehensive workflow bundle covers the full lifecycle of AI and machine learning development. It includes LLM application design, RAG implementation, AI agent architecture, ML pipeline development, observability, and security. Suitable for building production-ready AI systems.

A 98-rule design specification for building AI-native CLI tools across three certification levels. Covers structured JSON output, error handling, input contracts, exit codes, safety guardrails, self-description, and agent feedback loops.

Enables agents to perform authorized security assessments, blue team exercises, and penetration testing on AI products, agents, and infrastructure. Uses first-principles reasoning to identify vulnerabilities like prompt injection and tool abuse, generating evidence-based security reports without relying solely on payload libraries.

Forensic analyzer for AI coding sessions. Identifies root causes of scope drift and rework by classifying user specs, agent errors, and repo friction. Generates evidence-backed reports to improve future prompts and project health.

This skill provides a comprehensive playbook for setting up and orchestrating multi-agent systems using the standalone Antigravity 2.0 Agent Manager alongside the Antigravity IDE. It guides developers on creating a dual-window workspace to coordinate specialized AI agents (e.g., front-end, back-end, QA) to work in parallel on a single codebase, significantly boosting development efficiency and managing concurrent code changes.