AI & ML

Embed Copilot's agentic workflows in any application using Python, TypeScript, Go, or .NET.

Debug LLM applications using the Phoenix CLI: fetch traces, analyze errors, structure trace review with open and axial coding, inspect datasets, review experiments, and query the GraphQL API.

Create concise tldr summaries for GitHub Copilot customization files, MCP server documentation, or Copilot documentation from URLs or queries.

Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.

Refines task prompts through iterative questioning about scope, deliverables, and constraints, then copies the final markdown to the clipboard.

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

Create, update, and manage GitHub issues using MCP tools and the GitHub CLI.

Generates comprehensive GitHub project plans with Epic > Feature > Story/Enabler > Test hierarchy, dependencies, priorities, and automated issue tracking.

Generate images using AI from OpenAI or Google Gemini, with support for textures, icons, sprites, and visual assets.

Queries Microsoft 365 Copilot data (emails, meetings, documents, Teams, people) using the WorkIQ CLI/MCP server for live context, summaries, and recommendations.

Creates and runs LLM-as-judge evaluators on Arize, including managing tasks, column mappings, and continuous monitoring.

Controls and inspects a live Chrome browser for automation, debugging, and performance profiling using the Chrome DevTools MCP server.

Generates a comprehensive AGENTS.md file at the repository root to provide AI coding agents with project context, setup commands, development workflows, testing instructions, and code style guidelines.

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

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.

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

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

Build and optimize Power BI semantic models with star schema design, DAX measures, relationships, and row-level security following Microsoft best practices.

Runs a complete quality engineering audit on any codebase, deriving behavioral requirements, generating spec-traced tests, performing multi-pass code review, and producing a consolidated bug report with verified patches.

Integrate Xquik into apps, scripts, data pipelines, or AI agents for X API tasks like tweet search, user lookup, follower export, media actions, and webhook verification.

Registers prompt-based AI agents in Azure AI Foundry via REST API from a local JSON manifest, making them immediately available for invocation.

Build a persistent, compounding knowledge base that combines a wiki, knowledge graph, and raw source storage for structured retrieval with provenance.

Generates a high-level technical architecture specification for an epic from a Product Requirements Document, including system diagrams, component lists, and estimates.

Generate standardized commit messages following the Conventional Commits specification using a structured XML format.

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.

Automates repeated rounds of Copilot code review on GitHub pull requests: triggers review, triages threads, dispatches parallel fix agents, commits, replies, and loops until convergence.

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

Refines and polishes prompt files by applying proven best practices for structure, wording, and clarity while preserving original intent and formatting.

Guides users through an interactive tour of the context-matic MCP server, explaining its purpose and demonstrating all available APIs with live tool calls.

Write, review, refactor, and optimize Slang shader code for graphics, compute, tessellation, and ray tracing pipelines with cross-compilation support.

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

Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.

Converts plain text or generic text-based documentation files to properly formatted markdown, using explicit instructions, documented options, or a reference file as a template.

Analyzes current git changes and creates an appropriate branch following the Git Flow branching model.

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