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Found 2,377 Skills
Use this skill when the user wants to check AI agent logs, automation execution logs, org-level usage stats, AI credit consumption, or export automation job history. Covers 11 MCP tools.
Use when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents. Provides architecture guidance, implementation patterns, deployment strategies, observability, quality evaluations, multi-agent orchestration, and MCP server integration.
Create a durable handoff file that captures important conversation state for agent continuity. Use when the context window is getting full, when switching agents/sessions, when handing off work, or when asked to summarize progress without losing decisions, constraints, risks, and pending tasks.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
Orchestrates multiple skills to achieve high-level goals. Acts as the brain of the ecosystem to coordinate complex workflows across the SDLC.
Build production-ready AI agents using Google's Agent Development Kit with AI assistant integration, React patterns, multi-agent orchestration, and comprehensive tool libraries. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Controls InnerClaude instances on Sprites.dev VMs for testing workflows, install patterns, and Claude-to-Claude interaction. INVOKE BEFORE any 'sprite exec', 'inner Claude', 'test this workflow', 'Claude controlling Claude', or remote VM operations. Documents the critical tmux+pipe-pane pattern that makes OuterClaude/InnerClaude interaction work. Also covers checkpoint/restore and bootstrap. (user)
FORGE SM Agent — Decomposes requirements into stories with test specs. Usage: /forge-stories
Model Context Protocol development expert. Use when creating MCP servers, clients, or tools that enable AI agents to interact with external systems, APIs, and development environments.
Design and scaffold the code execution pattern for MCP-based agent systems. Use when building agents that interact with many MCP tools, when intermediate data is too large for model context, when you need loops/conditionals across tool calls, or when PII must stay out of the model context. Based on Anthropic's engineering guidance.
This skill guides the agent in identifying and replacing AI model-specific cliches and formulaic expressions with more natural, human-like language, grounded in external search for better alternatives.
Expert-level AI implementation, deployment, LLM integration, and production AI systems