Results for “git”

Merge the winning agent's branch into the base branch, archive losing branches via git tags, and clean up worktrees after an AgentHub competition session.

Run, debug, and interactively test C# MCP servers locally with IDE configuration, MCP Inspector, and GitHub Copilot Agent Mode integration.

Orchestrates multi-agent teams using the ClawTeam CLI, enabling task management, inter-agent messaging, git context integration, runtime profiles, and monitoring dashboards.

Bootstrap and run a multi-agent AI development team with parallel chats for planning, development, QA, and DevOps.

Generate a complete Go MCP server project with proper structure, dependencies, and implementation using the official github.com/modelcontextprotocol/go-sdk.

Enforces a machine-readable behavioral contract for multi-agent development, mandating specification-first workflows, git checkpoints, context lineage, and quality gates.

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

Prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.

Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions.

Build applications that programmatically interact with GitHub Copilot using the Copilot SDK, supporting session management, custom tools, streaming, hooks, MCP servers, and deployment across Node.js, Python, Go, and .NET.

Guides agents through the full lifecycle of NeMo-RL experiments: understanding recipes, launching reproducible runs, analyzing results, and preserving human oversight with git and TSV logs.

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI, Microsoft Agent Framework, GitHub Copilot SDK, ONNX Runtime, and OllamaSharp.

Build applications that programmatically interact with GitHub Copilot using an SDK that wraps the Copilot CLI via JSON-RPC, providing session management, custom tools, hooks, MCP server integration, and streaming across Node.js, Python, Go, and .NET.

Instrument LLM applications with OpenInference tracing for Phoenix AI observability, covering setup, custom spans, and production deployment.

Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot.

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance using the ax CLI.

Compares coding agents head-to-head on reproducible tasks, measuring pass rate, cost, time, and consistency.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

Automates filling in a Microsoft Forms form using Playwright MCP, with a review step before submission.

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs.

Compare coding agents head-to-head on reproducible tasks with pass rate, cost, time, and consistency metrics.

Generate a complete, production-ready MCP server project in Ruby using the official MCP Ruby SDK.

Add governance, safety, and trust controls to AI agent systems with policy enforcement, intent classification, and audit trails.

Adds Arize AX tracing to LLM applications using a two-phase agent-assisted flow that analyzes the codebase before implementing instrumentation.

Generate a complete, production-ready Model Context Protocol server project in Swift using the official MCP Swift SDK.

Generate a complete MCP server project in Python with tools, resources, and proper configuration using uv and the MCP SDK.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Optimize AI agents through automated evolution cycles using LLM-driven mutation of prompts, skills, and memory against measurable benchmarks.

Compiles research inputs—PDFs, code, logs, notes—into structured Agent-Native Research Artifacts with cognitive and physical layers.