Workflow Hub

I built this because modern camera manuals are packed with useful information but hard to use when you need an answer quickly. A professional camera can have hundreds of pages covering autofocus, custom buttons, metering, flash, bracketing, and shooting modes. Most of us don't memorize all of it, and during a shoot, when the light is changing or the client is waiting, I didn't want to search through a 300-to-500-page PDF. So, I turned my camera manual into an AI Field Assistant. It works with almost any camera that has a PDF manual, for both professionals and hobbyists. You need a Claude or ChatGPT account, the mobile app, a Project, your camera manual PDF, and a small Markdown instruction file that tells the AI how to use the manual. The setup is: 1. Create a new Project. 2. Add your camera manual PDF and the instruction Markdown file. I also added optional Markdown reference files for things like lenses, field notes, and troubleshooting, but the core setup works without them. Once the Project is set up, use the mobile app and ask short practical questions like: "flash not firing" “moving outdoors” “flash not firing” “low light checklist” “bracketing off” “which lens should I use?” The Project already knows which camera you use and treats the manual as the source of truth, so you don't have to repeat the model or write long prompts. The best use is before a shoot. Ask it to create quick notes for situations like indoor, outdoor, low-light, action, portrait, flash, or sunset, then keep those notes on your phone for the field. The goal isn't to let AI operate the camera. It's to help you understand your camera faster, prepare before the pressure starts, and avoid missing a shot because the answer was buried in the manual. If anyone wants to try this without building the instruction files from scratch, I already have the Markdown files I use for the assistant, including the camera instructions and optional lens/reference files. I’m happy to share a folder link with anyone who wants to copy the setup and adapt it to their own camera. The only thing you would still need to provide is your own camera manual PDF and any camera/lens-specific information you want the assistant to know.

I built this webapp for a friend who is building a house. It lets you see the finished version of every room in the planned home before anything is built. I described the idea to Claude Cowork, had a short back-and-forth and answered its questions, and it built the app in four stages. 1. Upload one or more floor plans. You can add as many as you need. 2. Select a room from the floor plan. The app shows it in several 3D views that you can rotate. 3. Ask for the room the way you want it when completed. For example: "Show me with a dark colored wood floor with blue walls" 4. Make changes and ask again, such as light colored wood floors. 5. Ask the app to add furniture so you can see how the room will look when finished. 6. Save multiple designs for each room, and export any image as a PNG. The whole build took less than an hour from start to finish. I can share the finished prompt if anyone wants to build something similar.

My Windows File History backup stopped working, and after a lot of troubleshooting I still couldn't get it running reliably. I wanted to keep using one external USB drive, so I asked ChatGPT to help me design a replacement. The result is a two-part automated system: FreeFileSync handles my everyday files, and a PowerShell script handles my roughly 12 GB Outlook archive. It's for anyone comfortable with Windows who has no programming background. 1. ChatGPT walked me through setting up FreeFileSync to mirror my important documents and folders to the external drive, using several source and destination pairs. 2. We created backup batch files for those pairs. 3. I configured RealTimeSync to watch for changes and trigger the backups automatically. 4. We added exclusion filters so the Outlook PST file stays out of continuous sync, since Outlook is normally running and the file needs special handling. 5. ChatGPT wrote a PowerShell script to back up the Outlook archive separately. 6. Windows Task Scheduler runs the script three times a day to check whether a backup is due, using a minimum 14-day interval. 7. The script detects changes so it skips unnecessary backups. 8. When a backup is due, it asks permission to close Outlook, copies the archive, and reopens Outlook automatically. 9. It verifies the copied archive with a SHA-256 checksum. 10. It keeps two rolling versions, Current and Previous, and logs errors. It also has safeguards against failed copies and disconnected drives. 11. ChatGPT helped me test the setup in isolation, verifying three backup rotation scenarios without touching my real Outlook data. What it produces: a configured, automated backup system on one external drive. Initial tests have passed, and the first full production Outlook backup is still pending.

I built a set of agents that run every day to find local businesses with no website, build a site for a selected business, and email the mockup to that lead. It's for anyone who sells web services to local businesses and wants to automate the prospecting. 1. Each day, my Gemini and ChatGPT agents check the major cities in the state I select, for the industry I select, and find the best businesses that don't have a website. They add those businesses to a spreadsheet. 2. A second agent reads the spreadsheet, selects the best prospect based on its need for web services and likelihood of buying, and starts building its website. 3. That agent checks the business's social media for images and its Google Business Profile for information. It also looks for a logo. If there isn't one, it creates a logo for them. 4. The agent finishes building the website, which becomes the mockup. 5. A third agent takes the mockup and sends an email with it to the lead. 6. A fourth agent is set up to reply once the lead responds, book them on a call, and put the call on the calendar. The result is a workflow that takes selected businesses from a prospect spreadsheet through a website mockup and follow-up, with replies leading to a booked call.

I use Claude, connected live to Airtable through MCP, as a one-person GTM team. It runs a daily pipeline war room, drafts outreach, and helps me create data-driven LinkedIn content. The workflow uses Claude, Airtable with a Prospects table and a LinkedIn Posts table, an outbound email platform for sequences, and LinkedIn. I review everything before sending or publishing, so a human stays in the loop on anything that goes out under my name. Step-by-step: 1. I start a morning war room session by opening Claude and letting it read my live Airtable pipeline. It gives me a scoreboard of what moved, what is stuck, and who needs a touch that day, along with a quick drill on my pitch. 2. Claude flags unworked contacts and suggests the next action for each. It drafts outreach using a written style rulebook: peer tone, hypothesis framing, short sentences, and no brochure language. I review everything and send it myself. 3. I log every LinkedIn post in Airtable with its full text, followed by its analytics: impressions, industry mix, seniority, comments, and notes on what worked. 4. Before writing a new post, Claude reviews the full post history, ranks the posts, and identifies which patterns drove reach and engagement from my target audience. 5. When I learn a new framework, such as Eugene Schwartz’s five stages of market sophistication, I give it to Claude along with my post history. Claude diagnoses where my content is going wrong and drafts a post designed to fix the pattern. 6. I publish at 7:30 AM, and a colleague seeds a comment during the first 30 minutes. Claude then logs the post back into Airtable, closing the loop for the next post. Key prompt: “Read every post in my LinkedIn Posts table. Rank them by impressions and ICP engagement. Tell me what the top performers have in common and what the weak ones share. Then apply [framework] and draft one post that fixes the pattern. Follow my style rules. Flag any claim or statistic you can’t verify.” What makes this work is that the analytics live in the same table as the content, so every post makes the next one smarter.

I wanted a repeatable way to create images with ChatGPT where we first agree on what the image should be, and only then generate it. So I described my preferred process to Plugin Creator and asked it to turn that into a private plugin called "beeldcreaties" (image creations). It is useful for anyone who wants more control over image generation, especially when small details like a crooked beak or an unusual expression matter. The workflow: 1. I say I want to make an image. 2. ChatGPT asks for a reference image and/or a written prompt. 3. I provide the input. 4. ChatGPT explains what it understood: subject, style, mood, important details, and anything that should stay unchanged. It asks for my approval before generating. 5. I approve the description or refine it. If I refine it, ChatGPT updates its understanding and checks again. 6. Once approved, it generates the image. 7. I give feedback. Any new version goes through the same understanding, approval, and generation loop. That approval step matters to me. A crooked beak, an unusual expression, or an imperfect detail might be exactly what gives a drawing its personality, so I want us to agree on what to preserve before creating a new version. To build your own, select Plugin Creator and try: "Create a private plugin called [name] for this image creation workflow: ask for a reference image and/or prompt, explain what you understood, and wait for my explicit approval before generating. If I refine the idea, update your description and ask again. After generation, accept feedback and repeat the approval process before making another version." The plugin uses ChatGPT's available image generation. What we created is a reusable set of instructions for how we collaborate. My favourite part is that I could describe the interaction I wanted in ordinary language and turn it into something I can call on again.

I run nine AI agents on one VPS, each named after its job, with 65 scheduled tasks between them. Most of those tasks never call a model. This is for anyone who wants AI working in the background. 1. One agent per job, not one for everything. Each has its own directory and credentials file, so revoking a compromised one costs one service, not the estate. 2. Write the work as a schedule, not a prompt, using five field cron. "0 3 * * *" backs the fleet up, "0 6 * * *" runs the security scan, and "0 23 * * *" mines the day's session logs. Interval jobs cover the rest, every 15 minutes for the watchdog. 3. Pick the delivery target at creation time: a Discord channel, email, or a local file. Mine go to a channel per officer, so a failure is a message I see and a quiet job is one I stop noticing. 4. Attach a skill instead of pasting a paragraph. Each job loads a skill file with the procedure, pitfalls and exact commands, kept in version control instead of a prompt I edit by accident. 5. Use a plain script when the answer is deterministic. My watchdog is a shell script on a 15 minute interval that checks disk, memory and failed units and pipes the result to the channel. It has run 13,752 times with no tokens and no model. 6. Put a guard in front of the tools. A hook runs before every tool call and refuses secrets in a chat message, deletes without approval, and invites accepted on my behalf. 7. Count your jobs monthly. I have 85 defined and 65 enabled. A disabled job is a plan; an enabled job is a contract that should be able to say what it can reach. The boring half of running a small estate happens without me, the interesting half still gets my attention, and it runs on a VPS I already paid for. The skills are public at github.com/ciberjohn/Hermes-Skills. The agents were never the expensive part. My attention was.

When I create video content, I pull imagery from many different sources, so my editing folder often ends up with photos that may or may not work inside my editing program. For a handful of photos, converting them manually is fine. For larger projects with dozens of images, it eats into my time and focus. I wanted something simple, lightweight on compute power, that could batch convert photos. 1. I asked Claude to create an app that runs on my local machine and converts photos into formats I know will work in my video editor. 2. I asked for it to be able to batch convert photos. 3. Claude returned an app that worked exactly the way I needed. 4. I asked Claude to install it on my computer. It was installed and working within seconds. 5. I use the app to batch convert my photo folders before bringing them into the editor. There are plenty of traditional ways to do this, but with Claude I got a solution that fit my workflow perfectly, and large image batches no longer slow my editing down.

I use AI to turn my weekly Slack updates into a report. This workflow is for anyone who posts updates in Slack and needs a summary at the end of the week. I haven't written out the full walkthrough yet, so these are only the basic actions I currently use. I'll update the post with more detail once I've documented the process properly. Step-by-step: 1. I gather the relevant Slack threads from the week. 2. I use AI to turn those threads into a weekly report. My notes also mention a separate goal: copying my own voice with AI, using it to make music, and releasing that music. I'm still working toward that goal and will share a workflow for it after I've tested it. I haven't measured how much time this saves yet.

I'm Wayne, founder of WazWorld, an independent music and content brand. My starting point is usually 1980s new wave, alternative, and synth-driven music: melodic bass, analog-style synths, guitars, and arrangements that leave room for the music. I use those influences to develop original WazWorld songs, not to reproduce existing recordings. This workflow is for artists who want AI to speed up experimentation while the creative decisions stay with them. 1. I define the emotion, instrumentation, structure, and vocal approach first. 2. I use ChatGPT (I call it Kai) to analyze arrangements and test directions against that brief. 3. For lyrics, I rewrite and refine the phrasing or provide my own verse and chorus ideas rather than accepting generated lyrics. 4. I turn the direction into musical versions in Suno and compare the results. 5. I use Suno Studio to work further on sections, arrangements, and regenerated parts. The first generation is rarely the final recording. 6. For PERFECT DAMAGE, I directed an album-cover concept contrasting a beautiful coastal setting with architectural destruction, then developed coordinated single artwork and promotional formats with AI assistance. 7. My WazWorld Command Center coordinates specialized AI help, including Kai, a Manager Agent, and YouTube and Streaming agents. Some of its capabilities are still in development. 8. I evaluate each version, request targeted changes, and approve the music, visuals, and release materials myself. The result is faster experimentation and coordination, but my musical taste, experience, and decisions shape the finished work.

What started with a single question became a collection of research projects, business plans, technical investigations, and even an original movie universe. I use ChatGPT 😳🫱, Gemini, Claude, Grok, and Perplexity to research, compare information, develop ideas, and solve problems. Managing them occasionally feels like running a staff meeting. 🤓 I also use AI for: - Personal organization: Managing appointments, schedules, reminders, and important dates. I can remember something from twenty years ago but forget an appointment at 1 p.m. 😭 - Writing: Preparing letters, emails, formal requests, reports, and business proposals while maintaining my own voice. - Technical research: Reviewing screenshots, documents, diagnostic reports, and online resources to investigate problems, analyze evidence, and identify possible solutions. - Private innovation: Developing an original technology concept through AI-assisted research and planning. Project details remain confidential. - Business development: Researching grants, funding, financial opportunities, and potential partnerships. I'm still looking for that million-dollar deposit button. 😂 - Research briefings: Following AI, cybersecurity, quantum computing, scientific discoveries, and world news through scheduled updates. - Creative production: Co-writing an original rogue-AI science-fiction comedy movie with ChatGPT, including characters, dialogue, scenes, and storylines. Occasionally, even the script editor goes rogue. 🎬 I provide questions, information, documents, or ideas. AI helps me research, analyze, organize, and develop the results, while I review the work and make the decisions. I started with one question. Now I'm apparently running an entire AI workforce. And since I'm paying their rent, EVERYBODY BETTER CLOCK IN! 😭🤣🤌 Step-by-step: 1. I provide AI tools with questions, information, documents, or ideas. 2. I use ChatGPT, Gemini, Claude, Grok, and Perplexity to research, compare information, develop ideas, and solve problems. 3. I apply the tools to organization, writing, technical investigations, private innovation, business development, research briefings, and creative production. 4. I review the research and developed materials, then make the final decisions.

I built a live camera bubble that floats over my slides during Facebook Live presentations. Because Facebook Live does not let me switch between my camera and a shared screen, I wanted my face to stay visible while I presented. This is useful for anyone who streams presentations to a Facebook Group and wants viewers to see them speaking. Claude Code built the working app within about five minutes. I made one tweak to the camera bubble’s shape, and it was then a finished, usable product. When I’m ready to go live in the Facebook Group, I click the camera bubble app on my local desktop. It uses my laptop camera, and I can drag the bubble around the screen and choose the circle size that suits whatever I’m sharing. Before I go live, I select "share entire screen" in the Facebook Live settings. Viewers then see my slides or screen with my floating camera bubble, so they can see me speaking as I present. The result is a live stream that shows my slides and my face together, and it took about five minutes to build. Step-by-step: 1. I gave Claude Code the challenge of building a live camera bubble for Facebook Live presentations. 2. Claude Code built the working app within about five minutes. 3. I made one tweak to the shape of the camera bubble, creating a finished, usable product. 4. When I’m ready to go live in the Facebook Group, I click the camera bubble app on my local desktop and use my laptop camera. 5. I drag the bubble around the screen and choose the circle size that suits what I’m sharing. 6. In the Facebook Live settings, I select "share entire screen." 7. I present my slides or screen while viewers see my floating camera bubble and my face together.

I had a ChatGPT personalisation setup that worked well until product changes made it less reliable. I kept re-explaining how I work and what good alignment feels like, so I rebuilt it as a plugin. I think of it as a backpack ChatGPT carries: a skill is the operating guide, reference files are the notes and maps, and connected tools are the gear. The plugin loads personal context and collaboration preferences, not a persona. How it runs: 1. In normal mode, the plugin quietly loads my current alignment and context bundle, so ordinary chats stay ordinary. 2. I trigger it with words I already use when talking to ChatGPT. Nicknames like "Goldfish," "Fish," "Blub," or "Blubsie" act as recognition signals, so I don't have to type setup commands. 3. Repair mode runs only when I explicitly ask for re-alignment. It reads the current bundle, identifies what went wrong, corrects course, and can propose a small update. 4. If the context fails to load, ChatGPT is set to say: "Error: Blub not loading. Fish is non-active." It is silly on purpose, but I immediately know the problem is missing context and not a personality shift. 5. Specialised skills stay separate, such as image generation, writing, research, or archiving. I combine them with the context layer when needed. It saves me from re-explaining my working style at the start of every session, and it makes missing context visible instead of silent.

I built an interactive web app to track a family member’s blood pressure four times a day. She is monitoring how her readings change throughout the day and how they respond when her doctor adjusts her medications. The app displays graphs and bar charts organized by date, time of day, and medication. This makes it easier to see how her blood pressure varies at different times and with different medications. She can upload readings by taking a picture of the blood pressure monitor screen or by entering the data into text fields. Selected data can be downloaded as a spreadsheet. The app works on her phone and laptop through a link hosted on Claude.ai. Step-by-step: 1. I built the app as a Claude artifact with a built-in database. 2. I hosted the web page on Claude.ai so it could be accessed from her phone and laptop through a link. 3. I connected the page to a database so readings persist and sync across devices. 4. I added photo input so she can take a picture of the monitor screen and have Claude read the numbers. She confirms the readings before they are saved. 5. I added table views, graphs, bar charts, and filters for weekly, monthly, or custom date ranges. 6. I added text fields for manually entering readings. 7. I added an export button for a CSV or printable report that she can take to the doctor.

I run product for Contentstack Canoe at Contentstack, and I use it on our own brand every week. Buyers ask AI what to buy before they ever reach a site, and most teams have no idea what those answers say. You add your domain, it drafts your brand profile and the questions your buyers actually ask, then asks each engine with web search on. The free plan covers ChatGPT and Gemini. Every Monday I open the report and look at three things: how often we're named, which competitors win the questions we lose, and which sources the engines cite. Then I take the top recommendation and fix just that one thing, usually a page AI crawlers can't read or a comparison page we never wrote. On the paid plan I also hook it into Claude through the MCP server, so I can ask "what changed this week and why?" in plain English. Next I want to add Microsoft Copilot and Google AI Mode, and see which AI agents actually read our pages.

Every new release, I love seeing what people reach for first. Are you throwing your hardest workflow at it, trying something you couldn’t do before, or waiting to see what others find? Drop what you’re excited about, even if you haven’t opened it yet. And if you build something cool, share it in the Workflow Hub so we can feature it!

I used an AI assistant to draft a volunteer management handbook for an arts organization. I wanted a complete starting document I could review and adapt, rather than building one from scratch. 1. I asked the AI to draft a volunteer handbook for the arts organization. 2. It pulled information from the organization's website. 3. It referenced relevant state laws. 4. It included the organization's current volunteer opportunities. 5. It reflected the organization's DEI policies. 6. It took the needs of the Board of Directors into account. 7. It wrote job descriptions and training opportunities for each volunteer role. The output was an 18-page volunteer management handbook with job descriptions and training for each role. The document is fabulous.

A working preview makes it tempting to keep asking for features. But the first screen tells you little about who controls the database, how the application is structured, or what the next prompt might change. I developed this workflow by putting my best-practices guides to work on a client project, then separating the reusable process from that project’s architecture. This workflow is for AI app-building platforms, often called vibe-coding platforms, where you describe an application and the platform generates and runs a working prototype. It focuses on keeping control of the code, repository, backend, and platform connections. It is not a general guide to coding agents or AI-assisted editors. Anyone who has watched a code generator slowly degrade a clean codebase knows why the starting conditions matter. Before asking for features, establish what you own, what the tool may change, and how you will check its work. Step-by-step: 1. Set ownership before the first build prompt. Decide what the application needs and who controls it. If it needs a new database or backend, provision it in an account you control before asking the builder to create the app. An existing API is another option, not a requirement; some prototypes need neither. Confirm the repository destination, integration permissions, and recovery access. Keep production behind a reviewed release process. 2. Give each tool readable rules. Establish one authoritative set of architecture, coding, and design standards. Identify which files each tool may change and which it may only read. Naming a rules file in a prompt does nothing if the tool cannot access it. Assign responsibility for data, authentication, application logic, deployment, and recovery. One person can hold several roles, but a UI task does not grant permission to redesign the backend. 3. Build the framework before the features. Use the opening prompt to establish the framework, runtime, routes, layout, and state ownership. Ask for empty page shells first. Check that navigation uses real routes and that shared elements have a defined home. Keep design values in one designated place. Any temporary authentication placeholder must be clearly labeled and isolated from protected data. Real users require real authentication and authorization. 4. Inspect and preserve the foundation. Read the generated files and inspect the running scaffold. Check for unrequested services, dependencies, and structural changes. A green build alone does not tell you whether the tool followed your instructions. Once accepted, protect the foundation files from routine UI prompts. Record the accepted commit and preserve a recovery copy beyond the integration’s ability to modify or delete. 5. Set up a prompt factory. A prompt factory is an assistant configured to turn a plain-language request into a bounded implementation prompt. Give it the project rules, file map, technical references, and current capabilities. It checks whether the requested behavior is supported before producing instructions. If something is missing, it prepares a prerequisite request for the responsible owner instead of inventing data or behavior to make the screen look finished. Each prompt names the tool, permitted files, protected files, applicable standards, relevant data and interfaces, exact change, what must be preserved, and observable checks. The factory prepares instructions; it does not approve architecture or release changes. 6. Build one governed feature. Where data is involved, establish correct data behavior first. Follow with a separate placement and styling task. Inspect the changes and test the affected behavior. Apply the One Fix Rule: one repair prompt, one attempt. If it fails, the generator is done with that file. Move the repair to manual maintenance rather than feeding another round of guesses into the codebase. After acceptance, save the next recovery point and update the working rules. Preserve the reason behind each rule so a later cleanup does not remove the safeguard. The point is to make the next feature less likely to undo the work you already accepted. Your database, where needed. Your code. Your standards. You control the tether. Part 2 will cover cutover: leaving the development platform with a controlled release, verification, and a recovery plan. Download The AI Builder Playbook on GitHub: https://github.com/wildeyeai/ai-builder-playbook

I'm an immigration attorney, and I built this workflow so our team can fill in the template emails we use at each stage of a case without copying a template or hunting for client details. We call up a template by number or case stage, answer a few questions, and Gemini drafts the email in Gmail, ready to send to the client. Step-by-step: 1. I uploaded all of our template emails into a Gemini notebook project inside our Enterprise Gemini account. 2. I connected Gemini to Gmail. 3. I instructed the Gemini project to ask for the details that need to be filled into the templates. 4. The user calls up a template by number or its corresponding case stage. 5. Gemini asks for missing details, such as the client’s name or other information, and tells the user what needs to be attached. 6. Once the user provides all the necessary details, Gemini drafts the email and usually finds the recipient’s email address on its own. 7. Gemini prepares the draft in Gmail for the user to send to the client. The result is a finished client email draft generated from a template number and the missing details, so we don't have to fill in the template by hand.