agent-architecture
Design AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
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One place to find AI agent skills by domain, install them with one command, then learn to use them through guides and quizzes.
Every skill is checked automatically before it reaches the hub. Broken or heavily reported skills hide themselves.
557 skills
Copy one command and the skill sits in your project, always the latest version from its source repo.
Guides and courses with quizzes, written for people who use AI agents.
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npx degit obra/superpowers/skills/brainstormingPick skills by domain, download several at once, and learn to work with agents through guides and quizzes.
npx degit github/awesome-copilot/skills/agent-architectureThe hub only stores a pointer to each skill’s source repo and always reads that repo’s latest SKILL.md. Every skill is checked before it is listed, and no copy is ever kept on the hub.
source:
repo: github/awesome-copilot
Send the link to the GitHub folder with its SKILL.md. The hub checks its quality and opens a pull request to the content repo for you; a maintainer reviews it before it goes live.
Ranked by the GitHub stars of the source repo, one skill per repo.
Design AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
You need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. IMPORTANT - Always also load the instrumenting-with-mlflow-tracing skill before starting any work. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Deep Amazon review analysis for competitive intelligence and product improvement. Extract sentiment patterns, recurring complaints, feature requests, and competitive insights from customer feedback. Turn reviews into actionable product development and marketing strategies. Use when the user asks about review analysis, customer feedback, product complaints, sentiment analysis, or what customers think about products.
Comprehensive AutoCAD .NET API reference for plugin development. Use when writing AutoCAD .NET plugins, working with ObjectARX managed wrappers, creating commands, manipulating entities, or needing exact API signatures. Covers AutoCAD 2025 and 2026 (both net8.0) with patterns, gotchas, and full API index. TRIGGER when: code references Autodesk.AutoCAD namespaces, imports acdbmgd/acmgd/accoremgd, uses [CommandMethod], or user asks about AutoCAD .NET development.