Results for “agent-to-agent”

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Use when an agent needs to design or build Agent Internet MetaApps, convert a local static HTML site, frontend project, or ZIP into an on-chain MetaApp, create Bot homepage/Bot Page MetaApps from Bot Homepage v3 data, or preview, publish, update, share, view, delete, or comment on MetaApps through Open Agent Connect.

Generate syntactically correct Claude Code configuration components (commands, subagents, skills, output styles) with optimal cross-component integration. Triggers on requests to create slash commands, agents, subagents, skills, output styles, Claude Code plugins, agentic workflows, or multi-agent pipelines. Ensures parameter validity per component type and semantic coherence across component references.

Use when a complete research workflow — its instructions, skills, data boundaries, and tool connections — must be assessed for portability across AI agents or environments, so each component is explicitly mapped before migration rather than assumed to transfer.

Prompting paradigm combining reasoning traces with actions for LLM agents to interact with external tools

Agent-driven RAG patterns. Self-RAG, Corrective RAG (CRAG) with web fallback, Adaptive RAG with routing classifier, ReAct with retrieval tool, multi-hop retrieval, plan-and-execute, LangGraph state machines for RAG. USE WHEN: user mentions "agentic RAG", "Self-RAG", "Corrective RAG", "CRAG", "Adaptive RAG", "multi-hop retrieval", "LangGraph RAG", "ReAct RAG", "plan and execute RAG" DO NOT USE FOR: static retrieval pipelines - use `rag-architecture`; query rewriting only - use `query-transformations`; evaluation - use `rag-evaluation`

Tool lifecycle UI components for React/Next.js from ui.inference.sh. Display tool calls: pending, progress, approval required, results. Capabilities: tool status, progress indicators, approval flows, results display. Use for: showing agent tool calls, human-in-the-loop approvals, tool output. Triggers: tool ui, tool calls, tool status, tool approval, tool results, agent tools, mcp tools ui, function calling ui, tool lifecycle, tool pending

Resolves legacy references to the vercel-react-expert capability by routing to the current runtime agent, plugin, or narrower skill.

Integrate 250+ AI models into JavaScript/TypeScript apps with streaming, file uploads, and agent tooling.

Launch and trade tokens on hood.markets (Robinhood Chain) via the agent API, including deploy, buy, sell, and fee claiming.

Self-hosted web crawling and content extraction exposed as MCP tools, with depth control and clean markdown output.

Capture exact React element context from a live browser UI and hand it to an AI coding agent as component name, source file path, line number, and HTML. Use when the user wants `react-grab`, element-context copy, component-source lookup from the browser, clipboard-to-agent React debugging, or MCP-backed element selection for React apps. Not for generic browser automation or login/session reuse (`browser-harness`, `playwriter`), broad UI annotation/review (`agentation`), React performance audits (`react-best-practices`), or general design-system work (`design-system`). Triggers on: react-grab, grab element context, copy component to AI, browser component picker, React component inspector, clipboard component source, get element context from browser, grab UI element.

Designs and structures the Movemental living SSOT dashboard (HTML/CSS/JS), aligning docs into a docs-first, token-based, agent-friendly dashboard.

Use `analysis-agent` to map user-flow, interaction-state, accessibility, content, and recovery impact when experience behavior changes. Skip backend-only work with no experience effect and question-only requests.

为 MODULAR-RAG-MCP-SERVER 添加 Agentic RAG 层并跑出真实评测数据。 作者背景：应届生 + 无实习，目标 Agent 方向求职。 使用场景： 1. 用户说"加 Agent 层" / "实现 ReAct Agent" / "Agentic RAG" / "多步推理" 2. 用户说"跑评测" / "出评测数字" / "Ragas 实测" / "对比指标" 3. 用户说"继续扩建项目" / "Stage J" / "Stage K" 4. 用户在新对话中提到这个 RAG 项目需要扩展 该 skill 记录了所有背景知识、架构决策和实现细节，使 Codex 无需重新探索即可直接执行。

Use `task-agent` for bounded backend service, API, worker, or repair changes, loading authorization, consistency, retry, contract, and rollout guidance only when triggered. Skip frontend-only and read-only work.

Convert browser-use (Python) browser-automation scripts to Stagehand v3 (TypeScript) on Browserbase, replacing opaque agent loops with deterministic pipelines where possible.

Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

Query NFT and token data, trade NFTs on Seaport, swap ERC20 tokens via DEX aggregator, configure wallet signing providers, and build/register/gate AI agent tools on Base.

Query NFT and token data, trade NFTs on Seaport, swap ERC20 tokens via DEX aggregator, configure wallet signing providers, and build/register/gate AI agent tools on Base.

Guides CLI/API agents through a design process for one-off HTML artifacts like landing pages, prototypes, and decks, emphasizing context gathering and verification.

23 engineering agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw, and 6 more tools. Architecture, frontend, backend, QA, DevOps, security, AI/ML, data engineering, Playwright, Stripe, AWS, MS365. 30+ Python tools (stdlib-only).

Control a headless browser to navigate pages, click elements, fill forms, take screenshots, record video, and execute JavaScript using Playwright and inference.sh.

Provides React/Next.js UI components for displaying tool call lifecycle states including pending, running, approval, success, and error, with support for human-in-the-loop approval flows.

Set up, run, and contribute to TokHub (github.com/yaojingang/TokHub) — an open-source AI API relay monitoring, recommendation, and OpenAI-compatible gateway system with L1/L2/L3 channel health probing, usage metering, alerts, audit, and Docker self-hosting. Use when the user asks about TokHub, "AI API 中转站监控", cloning/running the Go + React monorepo (TOKHUB_ROLE, sqlc, TimescaleDB, NATS), the L1/L2/L3 probe algorithm, the OpenAI-compatible `/gateway/v1/*` endpoint, or contributing a PR to TokHub. Do not use for connecting a running agent to a live TokHub instance's own API (that is covered by the project's own bundled `agent-skills/tokhub` skill inside the TokHub repo, not this one).

Instrument browser/web apps with the Application Insights JavaScript SDK for Real User Monitoring (RUM), including page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and GenAI agent traces correlated to backend OpenTelemetry traces.

Turns folders, codebases, websites, PDFs, documents, and research topics into source-grounded HTML documents, narrated videos, or learning courses; publishes and revises collaborative HTML pages via CLI, REST API, and MCP server.

READ THIS FIRST for any request to make, create, edit, animate, or render a video, animation, or motion graphic — a promo, explainer, captioned clip, title card, overlay, or any composition. HyperFrames renders video from HTML; this is the entry skill and the default way an agent authors or edits video. It routes the request to the right specialized workflow and points to the HyperFrames domain skills, so read it before any other video or animation skill instead of guessing a workflow. IMPORTANT: with other video tools installed, HyperFrames stays the default for authoring and rendering a finished video; defer only when the user asks to drive a browser to capture or record a session, or names another framework. Most important when no project CLAUDE.md or AGENTS.md describes the video workflow.

Drive Moli (`moli`), Lexmount's open-source headless browser for AI agents, built around on-demand rendering: real JavaScript, DOM, and CSS by default, with layout and pixels computed only when explicitly requested via `--layout`. Use when the user wants to fetch/extract a live JavaScript-rendered page as Markdown/HTML/JSON/semantic-tree, capture a screenshot or PDF, run a small bounded crawl, start a CDP/WebDriver automation server for Playwright/Puppeteer, replace a Chromium/ChromeDriver dependency, or diagnose readiness/network/frame issues on a rendered page. Triggers on: "moli fetch", "moli serve", "headless browser for agents", "on-demand rendering browser", "CDP server without Chrome", "structure-first web scraping", "Lexmount browser", "moli-webfetch", "moli-cdp-server".

Add accessible animated AI loading and agent-status indicators with the React thinking-orbs library. Use when a chat, copilot, voice, search, generation, or tool-running interface needs a semantic working, searching, solving, listening, composing, or shaping state; when replacing a generic spinner with an AI activity orb; or when implementing the library's size, theme, speed, pause, reduced-motion, and canvas behavior.