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Found 1,564 Skills
Implement LangGraph error handling with current v1 patterns. Use when users need to classify failures, add RetryPolicy for transient issues, build LLM recovery loops with Command routing, add human-in-the-loop with interrupt()/resume, handle ToolNode errors, or choose a safe strategy between retry, recovery, and escalation.
Security patterns for authentication, defense-in-depth, input validation, OWASP Top 10, LLM safety, and PII masking. Use when implementing auth flows, security layers, input sanitization, vulnerability prevention, prompt injection defense, or data redaction.
Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"
Generate and improve prompts using best practices for OpenAI GPT-5 and other LLMs. Apply advanced techniques like chain-of-thought, few-shot prompting, and progressive disclosure.
One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including LLM provider profile, MCP toggle, swarm preference context, sandbox type, and optional git init.
Execute complex tasks through sequential sub-agent orchestration with intelligent model selection, and LLM-as-a-judge verification
Convert documents (PDF, Word, Excel, PowerPoint, images, HTML) to Markdown using microsoft/markitdown. Use for document analysis, content extraction, preprocessing for LLMs, or batch document conversion. Supports images with OCR/LLM descriptions, audio transcription, and ZIP archives.
Use this skill to build, run, deploy, evaluate, and troubleshoot Go agents with Google's Agent Development Kit (`google.golang.org/adk`), including llmagent config, tools/integrations, callbacks/plugins, sessions/state/memory, workflows, streaming, MCP/A2A, and runtime/deployment patterns.
Bundle code context for AI. ALWAYS use --limit 49k unless user explicitly requests otherwise. Use for creating shareable code bundles and preparing context for LLMs.
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate exte...
Agent behavioral profiles that standardize how different LLMs behave. Load this skill when you need to: (1) adopt a specific behavioral mode for a task, (2) switch between creative/strict/talkative modes, (3) ensure consistent behavior across different models. Profiles define personality, decision heuristics, communication style, and quality standards.