MCP

Intermediate tutorial: have your AI agent install REA, an open-source reverse-engineering tool. Point it at an app and a feature, and it takes the feature apart without the source code, explaining the workflow with evidence. Once you understand the principle, it writes its own version from scratch in your project and adds one improvement. Includes 3 copy-paste prompts.

Intermediate tutorial: Hand REM, an open-source competitor research tool, to your AI agent. Run a free first check, read the ads, social, search page and website redesign reports, turn one finding into next week's experiment, then re-run and compare a week later. Includes 3 copy-paste prompts.

Reinstalling is easy. What's hard to get back on a new computer is your MCP servers, Skills, rules files, env vars and project dependencies. Ask your AI agent to inventory them, set up a private backup repo, list your keys, and write a restore script. Copy-paste prompt included.

The open source reverse engineering tool REA lets an AI agent take apart an app without its source code. Point it at a niche Android app and have it map only the user roles, flows, core data, and pricing points. Then build your own version from that brief. Includes a ready prompt and a note on install steps and infringement risk.

A written customer profile and a prospect list. Companies are filtered first, then people. Every email has a source. You spot-check the list, then send to a small batch.

Use when the user wants a prospect list, a target company list, or wants to know who to contact at a certain type of company. The agent queries the people and company data sources in Firecrawl Alexandria, finds companies first, then people, and fetches work emails only for the shortlisted people. The output is a ranked table or CSV with a source on every row, and it can run weekly to show only new prospects. It never guesses emails, looks up personal contact info or sends outreach, and it quotes the cost before spending credits. Use case: Have your agent build a verified, sourced B2B prospect list from your customer profile.

Give it a website and get back a teardown of how the company acquires customers. Every judgment is tied to evidence, and anything that could not be checked is listed separately. Then turn it into your own 30-day plan.

Use when you want to know how a company gets customers, what ads it is running, how several competitors' channels compare, or you want a promotion plan modeled on a rival's playbook. Your AI agent runs the open-source tool REM to collect public evidence on ad libraries, tracking tags, SEO, social media, founders, pricing and past selling points, then writes a report where every claim cites an evidence number and anything it could not find is listed separately. Public sources only. It does not invent spend and does not copy the rival's creatives. Use case: Have your AI agent map out a rival's customer acquisition playbook, with every judgment backed by evidence.

Google's A2UI 1.0 lets an agent say what belongs on screen while your app paints it with its own native parts: forms, charts and confirmation cards. Jira and Alation already use it, and it runs in Claude and Gemini through MCP Apps. Includes a prompt that walks your AI agent through the quickstart.

Beginner tutorial: install the open-source Muse4AI app on a Windows PC, log in to your own Meta Muse account, and hand the connection command to Claude Code. After that, one sentence in chat makes images and short videos. Covers checking credit use first and splitting shots to build longer videos. Includes 3 prompts you can copy and paste.

The open-source Poki project has released a preview iPhone app. An AI agent reads the screen, taps, and types on the phone locally. It takes commands from the app, iMessage, Telegram, or a terminal, and it can plug into Claude Code and Codex as an MCP server. Payments, deletions, and messages need your approval in the Dynamic Island first.

Use when the user wants you to work directly in their ElevenLabs account: generating voiceovers, sound effects, music, images and video, or creating, editing and debugging voice and chat agents, or adding ElevenLabs to a project. There are two official ways in. The hosted MCP (https://api.elevenlabs.io/v1/mcp, signs in with OAuth, no API key) fits chatting in Claude, ChatGPT or Cursor. The open-source CLI @elevenlabs/cli (MIT) with the official ElevenLabs Skills fits the terminal, scripts, and managing agent configs as code. Confirm credit use before generating, and confirm and dry-run before any delete or push. Use case: Connect your ElevenLabs account so the agent can make voiceovers, music and sound effects in chat or the terminal, and create and debug voice support agents.

Meta has released Astryx, a React design system it used internally for eight years and that powers over 13,000 apps, on GitHub under the MIT license, with themes, templates, and a hosted MCP and command-line tool for AI coding assistants. It is currently in Beta and requires React 19.

Use when a user is building a React UI and the project uses Astryx (@astryxdesign/core), or when they name Astryx or ask about its components, themes, or upgrades. Check component docs and templates first via the hosted MCP (search, get) or @astryxdesign/cli before writing code; get consent before running commands that write files; never run the unscoped npx astryx. Use case: Have the agent write UIs following Meta's Astryx component conventions instead of guessing from memory

The agent completes tasks on your real phone, key steps such as payments wait for you to click allow on your computer, sensitive apps are invisible, and every step comes with an explanation and a screen recording.

Use when the user asks you to do things on their own iPhone or Android phone (look up information, reply to messages, place orders, organize photos, move files). Operates through the Pinky desktop app and phone_* MCP tools, with the user approving key steps such as payments; never enters passwords or verification codes, and never bypasses any block. Use case: Let an Agent operate your phone while you watch and approve

Developer deep dive: how the three agent protocols divide the work. MCP lets models connect to tools and data, A2A lets different agents collaborate with each other, and NLWeb lets websites be accessed in natural language.

A beginner tutorial: install the desktop app, set the working directory, permission mode and Browser Subagent before you start, then learn common commands like /plan, /boost and /browser, connect Notion and Obsidian, and use the Flash model to save quota.

Someone has put together 10 reverse-engineering Skills and integration solutions for Agents: comprehensive analysis, scenario routing, Android interfaces, product analysis, lightweight command line, CTF, IDA assistance, JS signatures, and an all-in-one lab. We have added all of them to our site's Skill pages, with selection advice, license differences, and authorization reminders.

Google AI Ultra includes monthly Colab compute that, at A100 rates, runs for over 200 hours, which is a good deal for running open-source models. Compares the official Colab CLI and the remote Colab MCP: the CLI suits complex deployments, while MCP lets you turn GPUs on and off from within a chat at any time. Includes a quota table, hands-on steps, cost-saving principles and FAQs.

Use when a user wants an agent to start a GPU or TPU on Google Colab, run code, deploy or fine-tune open-source models, fetch results, and release the machine. Combines the official google-colab-cli (Apache-2.0) and the Colab remote MCP into one workflow: the CLI handles file transfer, SSH, Drive, and one-off jobs, while MCP handles lightweight start/stop from a chat client. Enforces releasing runtimes after use, asking before expensive GPUs, and never paying on the user's behalf. Use case: Let an agent start a GPU on Colab for you, run open-source models, fetch the results, and shut down automatically. Use the CLI from a local terminal, or the remote MCP from a chat client.

Use when a user wants an agent to generate images, video, audio, 3D assets and game art, or to model, rig, build levels and create effects in Blender, Maya, ZBrush, Unreal, Unity or Godot. Built on the open-source scenario-labs/skills (MIT): run dry_run to estimate cost before generating, and confirm before spending; expert tools only need the matching software installed locally. Use case: Have an agent generate images, video, audio and 3D assets, estimate cost with a dry run before generating, and drive software such as Blender, Maya, ZBrush, Unreal and Unity.

Intermediate tutorial: copy the prompt from this site's BlenderMCP Skill page and give it to your Agent, which installs everything for you; after a restart, open Blender and model, change materials, and arrange scenes one sentence at a time, then save and run a pre-delivery check. Suited to 3D prototypes, asset sketches, and scene layouts.

Use this when a user wants AI to work in Blender through natural language for 3D modeling, scene building, or object editing, or asks how to connect Blender to MCP. BlenderMCP exposes Blender as an interactive environment via the Model Context Protocol, so clients like Claude can talk to Blender and the result is an editable scene. Before using it in a real production pipeline, still check the Blender version, plugin permissions, file paths, and output quality. Use case: Use natural language to have AI model, build scenes, and edit 3D objects in Blender, producing editable scenes.

Intermediate tutorial: No more opening multiple terminals and manually shuttling messages between them. One command, npx tmux-bridge-mcp setup, lets Claude Code, Codex, Gemini CLI, and Kimi Code read each other's terminals, send each other tasks, and hand work off in relay, all through MCP. Includes concrete steps for split panes, naming, and collaboration.

A general-purpose skill for Codex and local creative workflows that turns a product brief, reviewed narration, digital human or real-person on-camera speech, real product footage, and motion graphics into a deliverable video with review gates. It is not tied to any brand or script; product facts, copy, media, and vendor parameters live in the project directory. Multi-file repository, installed via git clone. Use case: Combines a product brief, narration, and digital human on-camera speech into a deliverable product video with review gates.

Use when a user wants an AI assistant to control editing, color grading, media organization and rendering in DaVinci Resolve Studio using natural language, or asks how to connect DaVinci Resolve to MCP. Connects through the MCP server built into Studio 21.1; always back up first, work on a copy, and ask the user before deleting, overwriting, changing project settings, or batch rendering. Use case: Let an AI assistant directly control editing, color grading and rendering in DaVinci Resolve Studio using natural language.