Results for “build-tool”

Build lightweight AI agents with HuggingFace Smolagents — use CodeAgent (writes Python to act) or ToolCallingAgent (JSON tool calls), add built-in or custom Tools, orchestrate multi-agent pipelines with ManagedAgent, and run locally or via HF Inference API.

Provides expert guidance for building modern C++20/23 applications, covering template metaprogramming, memory optimization, concurrency, and build tooling.

Guides the creation of MCP servers that enable LLMs to interact with external services through well-designed tools.

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK, with support for function tools, hosted tools, MCP servers, conversation threads, and streaming responses.

Build, register, and gate AI-callable tool endpoints using the OpenSea Tool Registry (ERC-8257) on Base. Scaffold HTTPS tools with JSON Schema interfaces, register them onchain, gate access via NFT ownership or x402 pay-per-call (USDC), and call gated tools.

Validate whether an OSS tool is worth building through staged namespace, existing-solution, and feasibility review

SQLAlchemy Python SQL toolkit and ORM with powerful query builder, relationship mapping, and database migrations via Alembic

CSS design system standards — custom properties, BEM naming, spacing/typography/color scales, no build tools

Build .NET MVVM ViewModels with CommunityToolkit.Mvvm source generators, base classes, and commands for WPF, WinUI, MAUI, Uno, and Avalonia.

Builds or debugs Anthropic Claude integrations using @anthropic-ai/sdk, covering messages, tool use, extended thinking, prompt caching, and streaming.

Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python.

Creates, reviews, debugs, and modernizes Python project structure with deterministic tooling including pyproject.toml, uv, Ruff, Pyright, and pytest.

Python SDK for inference.sh - run AI apps, build agents, and integrate with 150+ models. Package: inferencesh (pip install inferencesh). Supports sync/async, streaming, file uploads. Build agents with template or ad-hoc patterns, tool builder API, skills, and human approval. Use for: Python integration, AI apps, agent development, RAG pipelines, automation. Triggers: python sdk, inferencesh, pip install, python api, python client, async inference, python agent, tool builder python, programmatic ai, python integration, sdk python

Vite build tool configuration, plugin API, SSR, and Vite 8 Rolldown migration. Use when working with Vite projects, vite.config.ts, Vite plugins, or building libraries/SSR apps with Vite.

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...

Turn an app idea into a precise build prompt for an AI app builder (Lovable, v0, Bolt, Replit, and others). Trigger on "build me an app", "prompt for Lovable", "v0 prompt", "vibecode this", "I want to build a tool that", "prompt to build a web app", or any request to spin up an app with an AI builder.

Provides guidance on using Anthropic's Claude API, covering message formats, context windows, tool use, and best practices for building conversational AI applications.

Reference for building LLM-powered applications with the Claude API and Anthropic SDKs, covering model selection, parameters, streaming, tool use, and migration.

Configure Next.js build tooling, deployment, and developer workflow. Use when setting up Turbopack, standalone Docker output, bundle analysis, CI caching, environment variable validation, or ESLint integration for Next.js projects.

Build or refactor CLI tools designed for AI agent consumption: non-interactive, flag-driven, idempotent, with --json output and --dry-run preview. Use when creating a new script the agent will call, adding agent-friendly flags to an existing tool, or debugging why an agent keeps failing to use your CLI.

Provides expert guidance for using n8n-mcp MCP server tools to build, validate, and manage n8n workflows efficiently.

Build production-ready AI agents with type-safe tool use, structured outputs, dependency injection, and multi-model support using PydanticAI.

Builds reusable CircleCI Orbs with the Orb SDK and orb-tools pipeline, packaging commands, executors, and jobs with automated semantic versioning.

Iteratively builds websites with Stitch by reading a baton file, generating pages via MCP tools, integrating them, and writing the next task.

Scaffold and implement Model Context Protocol (MCP) servers that expose external services, APIs, and data sources as typed tools and resources for LLM agents. Use when the user says "build an MCP server", "give Claude access to X", "create an MCP tool", "expose my API to an agent", or "AI agent integration".

Build and modify UI screens, panels, popups, responsive layouts, and UI Toolkit documents in QuizVerse.

Build, configure, and troubleshoot GraalVM Native Image applications using native-image CLI, Maven, or Gradle Native Build Tools, including reachability metadata and issue diagnosis.

Build Livewire components with patterns for lists, forms, validation, and events.

MCP (Model Context Protocol) - Build AI-native servers with tools, resources, and prompts. TypeScript/Python SDKs for Claude Desktop integration.

Builds Google Stitch UI projects and generates screens from feature descriptions or export files by calling Stitch MCP tools directly, following Stitch prompting best practices.

Vite build tool patterns including config, plugins, HMR, env variables, proxy setup, SSR, library mode, dependency pre-bundling, and build optimization. Activate when working with vite.config.ts, Vite plugins, or Vite-based projects.

Configure Vapi server URLs and webhooks to receive real-time call events, transcripts, tool calls, and end-of-call reports. Use when setting up webhook endpoints, building tool servers, or integrating Vapi events into your application.

Adds GraalVM native image support to Java applications by analyzing the project structure, configuring the build tool, and iteratively resolving build errors.

Modern Java 21+ language standards including Records, Pattern Matching, and Virtual Threads. Use for Java language constructs or Java-to-Java upgrades; defer Kotlin, tests, formatting/build tools, CompletableFuture, builder patterns, and isolated null-handling questions.

Build LLM-powered applications with Claude using the official Anthropic SDK or raw HTTP, covering model selection, API parameters, streaming, tool use, MCP, caching, and model migration.

Guides the creation of high-quality MCP servers that enable LLMs to interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.