Workflow Hub

I built a quarterly “AI Audit” of my Claude history, adapted from The Rundown’s AI Audit prompt and turned into a repeating loop. Each quarter, Claude reads every conversation I’ve had and reports patterns I can’t see myself: where my stated priorities differ from where my attention actually goes, which decisions I keep reopening, and connections across work, money, travel, and personal life. It then rewrites its own instructions for working with me based on what it found. I request my Claude data export, and the run finds the export email in Gmail, downloads it through Claude in Chrome, and unzips it on my machine through the desktop app. I never touch a zip file. The conversations are condensed and split by month, and Sonnet subagents pull structured notes from each month. An Opus subagent combines the monthly notes into a quarterly report under strict evidence rules: every finding cites at least three conversations across at least two months, states the strongest counter-evidence, and labels each claim as OBSERVED or INFERRED. When the evidence is thin, it says “insufficient evidence” instead of padding. Claude writes and maintains a Google Doc with two tabs. Reports contains one report per quarter with footnoted citations. Progress compares the quarters and includes two sections I added to the original prompt: Stop / Start / Continue / Change, which generates concrete behavior changes for the next quarter, and a Cross-domain pass, which identifies patterns spanning work, money, body, and personal life that I would not connect on my own. I push back on findings I disagree with. Those corrections go into the doc and a versioned prompt, now v1.1, so the next run does not repeat the mistake. Claude then takes the latest audit and its stored memory about me and proposes rewritten project instructions. The first rewrite removed stale facts and added rules to check memory, docs, and connected apps before saying it does not have something; move long-running threads into a maintained doc because one tracking chat had grown to 434 messages; and name the number that would settle an open decision because the audit showed I close decisions once a number exists and keep circling the ones without one. On the first non-holiday weekday after each quarter closes, a scheduled task emails me to request the export while I am at my desk. Scheduled checks later that day pick up the export and run the whole pipeline from a written runbook. I backfilled Q1–Q3 2026 to start, and Q3 alone covered 151 conversations and 2,922 messages. Step-by-step: 1. I started from The Rundown’s AI Audit prompt. 2. I requested my Claude data export and automated the process of finding the export email in Gmail, downloading it through Claude in Chrome, and unzipping it through the desktop app on my machine. 3. I condensed the conversations, split them by month, and used Sonnet subagents to extract structured notes from each month. 4. I used an Opus subagent to synthesize the monthly notes into a quarterly report. Each finding must cite at least three conversations across at least two months, include the strongest counter-evidence, and be labeled OBSERVED or INFERRED. Thin evidence is reported as “insufficient evidence.” 5. I had Claude write the results to a Google Doc with Reports and Progress tabs, including footnoted quarterly reports, cross-quarter comparisons, Stop / Start / Continue / Change recommendations, and a Cross-domain pass. 6. I challenged findings I disagreed with and stored those corrections in the doc and in the versioned prompt, now v1.1. 7. I had Claude use the latest audit and its stored memory about me to rewrite its project instructions, including rules about checking connected sources, maintaining long-running threads in a doc, and naming the number that would settle an open decision. 8. I scheduled the export request for the first non-holiday weekday after each quarter closes, then used scheduled checks and a written runbook to run the pipeline. 9. I backfilled Q1–Q3 2026, with Q3 covering 151 conversations and 2,922 messages. 10. To build your own version, add the evidence rules and OBSERVED/INFERRED labels, split the work into inexpensive monthly extraction and one strong synthesis, write the output to a living doc instead of a chat, and make the final step “rewrite your instructions for me.” That turns the audit from a report into a feedback loop.

Every Friday at 10 a.m. ET, a scheduled Claude agent refreshes a living "Hevy Workout Log" document with that week's strength-training data, body measurements, and a progress photo. The automation runs fully unattended, with no human in the loop after the owner sets it up. The agent pulls information from three independent systems: the Hevy fitness-tracking API, a Google Drive photo folder, and the living document. It reconciles those sources against a date range, determines what's new versus already recorded, and writes structured, formatted updates in the correct places. This requires more judgment than a simple script or webhook. The scheduled task starts a fresh agent session every Friday at 10 a.m. ET with the full task specification and API key included in the prompt. The Hevy API is the source of truth for workouts from `GET /v1/workouts` and body measurements from `GET /v1/body_measurements`; both endpoints are paginated. Google Drive holds the "Progress Pics" folder, which the agent searches by file timestamp. Claude Docs, through a Claude Artifact, is the living document: it has one tab for each weekday and a Weekly Summary tab, and the agent edits it in place with structured operations rather than full rewrites. To embed a photo, the agent downloads the Drive file locally, uploads it to the Claude Artifact asset store, converts it to a document-native blob, and references it as a Markdown image. There is no custom backend, database, or glue code. The agent calls each system's API directly and keeps the orchestration logic in its reasoning for that run. Step-by-step: 1. Read the document's current state and find the most recent week already recorded in the Weekly Summary tab. 2. Call the Hevy API and determine which Monday–Friday week has just ended, making sure today's date is past that week's Friday. 3. Check whether that week's row is already in the Weekly Summary table, fully populated, and contains the latest matching photo. If everything is already recorded, there may be nothing to update, but the agent still checks for a newer photo that may have appeared in Drive after the data was written. 4. Pull per-set details for every workout that week, including the exercise, weight, and reps. Compute total daily lifted volume by summing weight × reps for each set on each day. 5. Search the Google Drive photos folder for images timestamped within that Monday–Sunday window. Select the single most recent image, replacing any older photo already embedded for that week. 6. Pull body measurements from the same week and calculate weekly averages for weight and body fat, along with the week's waist reading. Convert units as needed from kilograms to pounds and centimeters to inches. 7. Update the Weekly Summary tab with one row per week, including daily volumes, weekly averages, and the progress photo. 8. Update each weekday tab with a dated section containing the workout name, a per-exercise set-by-set table, and a Total row for summed volume and reps. If no workout was logged, add a note instead. 9. Send the human a plain-language summary every time, describing what changed or confirming that nothing changed, because nobody is watching the scheduled run live. The workflow is idempotent without a database. There is no job-tracking table; on each run, the agent reads the live document, compares dates, and checks whether cells are filled before deciding what to write. Progress photos are not tagged by week, so the agent matches them only by whether their Drive timestamp falls within the Monday–Sunday window. It explicitly selects the most recent match, allowing a better photo taken later in the week to replace an earlier embedded photo. The workflow also handles the multi-hop process required to embed an image in a live document: Drive file to local download, artifact asset upload, document-native blob conversion, and Markdown image reference. If the Hevy API key is revoked, the task stops cleanly and tells the owner to generate a new key and provide it, rather than failing silently or looping. Because the workflow runs on a schedule without anyone watching, its specification requires a notification every time, including when nothing changed. This keeps the automation auditable even when it performs no updates.

I built an AI fundraising campaign assistant with Make.com. The workflow reads emails sent from the crowdfunding platform used for the campaign, extracts the relevant information, and records it in a spreadsheet. It also uses spreadsheets from previous years’ campaigns to maintain a list of people who have not donated to this campaign yet but have donated in the past. I can ask the agent questions by email, such as who has already donated or who has not donated this year but donated previously. It can also answer questions with specific parameters, such as who has not donated but previously gave more than $1,000, or how much a specific person donated. I built the workflow in Make.com using its AI tools. I connected it to a Gmail account, a Google Drive account containing the spreadsheets, and a ChatGPT account that processes the information as the AI agent. Step-by-step: 1. I connected Make.com to the Gmail account that receives emails from the crowdfunding platform. 2. I configured the workflow to extract relevant information from those emails and record it in a spreadsheet. 3. I connected Google Drive so the workflow could use spreadsheets from previous years’ campaigns. 4. I used the historical spreadsheets to identify people who have not donated to the current campaign but have donated in the past. 5. I connected ChatGPT as the AI agent that processes the campaign and donor information. 6. I configured the agent to respond to questions sent by email, including who has already donated, who has not donated this year but donated previously, who previously gave more than $1,000, and how much a specific person donated.

Start with the property, not a generic landscaping template. Upload photos, measurements, site constraints, current problems, future plans, budget limits, and the look you want. Then use AI to build a complete picture of how the property functions today and how it should evolve over time. Next, work area by area. For overcrowded garden beds, map every tree and shrub, then assess mature size, spacing, health, irrigation coverage, sightlines, and maintenance demands. From there, use AI to determine what should stay, what should move, what should be removed, and how each bed should be reshaped and edged. Convert each recommendation into an execution plan that includes: Step-by-step: 1. The target design 2. A step-by-step build sequence 3. Required materials and tools 4. Budget and priority level 5. Seasonal timing 6. A year-by-year maintenance plan Finally, connect every individual project into one master roadmap. Plan the garden beds, trees, lawn, irrigation, drainage, driveway, shelterbelts, recreation areas, and future buildings together so one improvement does not create a problem somewhere else. The result is a living property operating system. Add a new photo, issue, or idea, and the plan updates with the next best action.

I built a workflow that prepares late invoice reminder emails for me. Our industry has tight margins, and our back end is rudimentary with limited capabilities, so sending these reminders was annoying, repetitive manual labor. I had Claude Cowork walk me through creating an agent that logs in every Monday night, checks our late AR, and compares the customer names against a Google Sheet list I created. It uses an email template I provided, adds the contact name from the Sheet, and includes each late invoice’s identifying number, amount, and due date. The agent puts the emails in my Drafts folder and emails me a report showing which customers were included, how much each one owes, and the totals. All I have to do is review the drafts, confirm everything looks good, and hit Send. This has saved me about an hour every Monday night of tedious, repetitive manual labor. Step-by-step: 1. I created a Google Sheet with the customer names and contact names. 2. I had Claude Cowork walk me through creating an agent that logs in every Monday night. 3. The agent checks our late AR and compares the customer names against the Google Sheet. 4. It uses my email template and adds the contact name, invoice identifying number, amount, and due date. 5. It places the completed emails in my Drafts folder. 6. It emails me a report listing the included customers, the amount for each, and the totals. 7. I review the drafts, confirm that everything looks good, and send them.

San Diego County is enormous, and its startup scene is growing rapidly. I wanted a better way to see where companies are located, especially because many of them host networking events and there was no central map or repository. I told Claude what I wanted: an alphabetical directory with an industry selector and a way for people to add their own companies. I also asked it to keep the project as simple as possible, use as few tokens as possible, and wait for my approval before taking any action. Claude helped me design the map, create the Google Form for submissions, set up the Google Sheets workflow for hosting and approving companies, and use Netlify to host the main file. It also guided me through linking the map to a page on my own website: https://sdaimap.michelabood.com/. Claude warned me about the legal issues involved in scraping a list wholesale from other sites, so the map is designed to be populated by people submitting their own companies. I just published it, and 10 companies have already been listed, with more on the way. I have also received great comments on LinkedIn. Now I can see at a glance how far I need to go to find a specific company. I also had Claude create a step-by-step guide, which I am happy to share if people want it. Step-by-step: 1. I described the map I wanted to Claude, including an alphabetical directory, an industry selector, and a way for people to add their own companies. 2. I asked Claude to keep the project as simple as possible, use as few tokens as possible, and wait for my approval before taking any action. 3. I used Claude to design the map and create the Google Form for company submissions. 4. I used Google Sheets to host and approve the submitted companies. 5. I followed Claude's guidance to host the main file on Netlify. 6. I linked the map to a page on my own website: https://sdaimap.michelabood.com/. 7. I avoided scraping a wholesale list from other sites because of the legal issues Claude identified. 8. I published the map and began collecting company listings from people directly.

My mother recently moved into a retirement suite, which meant downsizing her possessions. We ended up with dozens of physical items she no longer had space for, and we wanted her children and grandchildren to have an opportunity to sign up for the family items they loved before anything was donated. I needed a way to catalog everything quickly, share the information remotely, and verify that we weren't accidentally giving away items of significant value. I tested a physical-to-digital workflow using Gemini. Instead of putting the items in a pile or typing out endless lists, I drew a numbered grid on a large whiteboard, placed one item in each square, and took a few photos. Gemini identified the objects by grid position, categorized them, estimated their market values, and built a ready-to-use spreadsheet with a signup column. The physical staging took about 30 to 45 minutes, depending on how many items were on that particular board. The entire digital workload—including cataloging, valuing, formatting, and creating the spreadsheet—took under 10 minutes. The process turned an overwhelming chore into something manageable and produced an end product that was easy for everyone to use. Step-by-step: 1. I drew a simple grid on a whiteboard, numbered each box, and placed one item in each square before taking photos. 2. I uploaded the photos to Gemini and prompted it to identify every item by its board name and grid number. 3. I asked Gemini to classify each item into logical groups, such as Glassware, Ceramics, and Woodcraft, and provide quick, realistic resale-market valuations based on visual condition. 4. I asked Gemini to generate a downloadable Excel file formatted as a signup catalog, with columns for Item ID, Grid Number, Description, Category, Estimated Value, and a blank "Claimed / Signed Up By" column for family members to enter their names. 5. I uploaded the file to Google Drive, converted it to Google Sheets, and added a note at the top explaining how tie-breakers would work if two family members wanted the same item.

Many organizations have critical process knowledge that exists only in employees’ heads. When someone has a question, they have to track down the right expert, ask questions others may have already asked, and hope they remember every detail. This doesn’t scale and creates knowledge silos. I built a workflow that turns conversations with subject matter experts into structured, searchable organizational knowledge. Instead of asking employees to write documentation, an AI interviewer guides them through the process they know best, converts the conversation into well-structured documentation, and publishes it to an AI-powered knowledge base that anyone can query. Step-by-step: 1. Ask an employee to choose a business process they know well. 2. Have an AI interviewer ask follow-up questions to capture the complete workflow, decisions, exceptions, and best practices through a natural conversation. 3. Save the interview transcript. 4. Use an LLM to convert the transcript into structured documentation with clear sections, steps, decision points, and FAQs. 5. Store the documentation in a searchable knowledge repository and index it in a vector database. 6. Let employees ask questions through an AI assistant that retrieves the most relevant documentation and answers in natural language. 7. Repeat the process over time as processes evolve or additional experts contribute new knowledge. Instead of repeatedly interrupting the same subject matter experts, employees can get consistent answers from an AI assistant backed by documented organizational knowledge. The organization captures valuable expertise before it’s lost, reduces knowledge silos, improves onboarding, and creates documentation simply by having conversations.

I’m creating a master plan in Google Docs that documents my file structure and naming conventions for different file types, including insurance documents, receipts, contracts, and others. I’ll give Claude access to my file server through a file-sharing link and have it create a Markdown file with guidelines for how I want my files organized. To define those guidelines, I can have Claude interview me about my business and what I want the system to do. I’ll then copy and paste a file path into Claude Code and ask it to organize the files according to the master plan. Eventually, I plan to create a folder on all my employees’ desktops that Claude can scan regularly, naming and filing everything placed there. That way, I won’t have to worry about files being misfiled or named incorrectly. Step-by-step: 1. I’ll create a master plan in Google Docs that lists the file structure and naming conventions for each file type, such as insurance documents, receipts, and contracts. 2. I’ll give Claude access to my file server through a file-sharing link. 3. I’ll have Claude create a Markdown file with guidelines for the organization system, using an interview about my business and requirements to define what it should do. 4. I’ll copy and paste a file path into Claude Code and ask it to organize the files according to the master plan. 5. Eventually, I’ll create a folder on each employee’s desktop for regular scanning, so files placed there can be named and filed automatically.

The workflow starts when a project grows beyond one conversation and approved decisions, file versions, or next steps become difficult to track. I begin by creating one active authority document. It is not a transcript of every discussion; it contains only the rules, definitions, methods, and structural decisions that currently control the project. Next, I label project files by status: active, working, superseded, or archived. I also state clearly which file is authoritative instead of expecting the AI to infer it from the filenames. As decisions are approved, I record them in the authority document or a change log. I include what was decided, why it was decided, what it affects, and whether it needs later review. This keeps the chat from becoming the only project record. For large deliverables, I divide the work into named stages. A spreadsheet project might move through architecture, data migration, calculations, validation, and publication. Each stage produces a separate file and has a clear completion check. When a conversation gets too long, I create a handoff prompt for the next chat. It includes: - the project goal; - the active authority files; - completed work; - approved decisions; - unresolved issues; - the next specific deliverable; - anything that should not be redesigned. I start the new conversation with that prompt and only the files needed for the next stage. Finally, I validate the output outside the chat. For Excel files, that means opening them in desktop Excel, checking formulas and errors, saving them, closing them, and reopening them. An AI-generated file is not finished until it passes that check. The core pattern is to record the authority, label the versions, log decisions, work in stages, create a clear handoff, and validate the result. Step-by-step: 1. I create one active authority document containing the project’s current rules, definitions, methods, and structural decisions. 2. I label each project file as active, working, superseded, or archived, and identify the authoritative file. 3. I record approved decisions in the authority document or a change log, including the decision, rationale, effects, and any need for later review. 4. I divide large deliverables into named stages, with a separate file and clear completion check for each stage. 5. I create a handoff prompt when a conversation becomes too long, covering the project goal, authority files, completed work, approved decisions, unresolved issues, next deliverable, and anything that should not be redesigned. 6. I begin the next conversation with the handoff prompt and only the files needed for that stage. 7. I validate the final output outside the chat—for Excel files, by opening them in desktop Excel, checking formulas and errors, saving, closing, and reopening them.

I use Claude Desktop on my Mac, connected to Canva and Metricool, to build and schedule social media posts. I used ChatGPT to help me set everything up because it has more history and access to my company knowledge documents. ChatGPT created instruction files for Claude that provide our company context and guidelines. I pointed Claude to our website, which contains many articles, and connected our Canva and Metricool accounts. We discuss suitable topics to post about. Claude reviews the website for relevant articles or landing pages to link to, then suggests a series of Facebook and Instagram posts. It checks the designs in our Canva account, adjusts them as needed, or asks me for photos and other artwork. Claude then builds the posts in Metricool and saves them as drafts for me to review, following our working rules. I review the drafts and move them to pending. Claude checks that they will publish automatically on the correct date and at the correct time, and it creates links with meaningful UTM parameters. After publishing, I can ask Claude to analyze the results, identify what is working and resonating with my market, and help us test different approaches, content, and topics. Metricool captures the data, and Claude uses it to inform the next round of posts. This saves me hours every week, gives me time to work on other things while Claude handles the process, and produces results that are improving each week. Step-by-step: 1. I used ChatGPT to help configure Claude Desktop and create instruction files containing our company context and guidelines. 2. I connected Claude to our website, Canva account, and Metricool account. 3. I discuss potential topics with Claude, and it finds relevant website articles or landing pages to link to. 4. Claude suggests a series of posts for Facebook and Instagram. 5. Claude reviews our Canva designs, adjusts them when necessary, or asks me for photos and other artwork. 6. Claude builds the posts in Metricool and saves them as drafts for me to review according to our working rules. 7. I review the drafts and move them to pending. 8. Claude checks the publishing date and time and creates links with meaningful UTM parameters. 9. After publication, I ask Claude to analyze the results and identify what is resonating with my market. 10. I use the Metricool data and Claude’s analysis to test different approaches, content, and topics in the next round of posts.

I built a job application workflow that goes beyond finding vacancies and generating generic CVs. First, GPT helps me scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. The more useful part starts when a vacancy looks genuinely promising. I connected GPT to Canva and created several “base CVs” for the sectors and roles I was interested in. Instead of using one universal template, I designed each CV to fit the visual and cultural tone of a particular type of work. For example, a front-desk role, a back-office administrative position, and an assistant role in a creative company may involve overlapping skills, but they communicate very different expectations. Each base CV therefore uses different layout choices, colour use, visual tone, and photo selection. For every version, I review several possible photos and choose the one that best matches the role and the impression I want to convey. When I apply for a specific vacancy, GPT helps turn the relevant base CV into a more personalized version. Instead of rewriting my entire work history, we emphasize the experience, tasks, strengths, and values that are genuinely most relevant to that role. The same applies to motivation letters. Rather than generating a generic corporate letter, GPT uses a tone of voice shaped through months of conversation with me. The goal is for the application to sound recognizably like me while still matching the language, priorities, and culture of the vacancy. Before building this workflow, I did everything manually. For a vacancy that felt worth applying to, I typically spent around two hours refining the CV and motivation letter alone, not including the time spent searching for the vacancy. Even a small typo in the final application email could undermine hours of careful work. Now, once I decide a vacancy is a good fit, the full personalization process takes around twenty minutes on average. That includes selecting the right base CV, adapting the emphasis, refining the letter, checking the tone, and preparing the final application. The workflow works across several layers: vacancy discovery, role and sector matching, base CV selection, adapting experience and values, adjusting visual tone, personalizing the letter, final review, and application. What I like about this approach is that personalization is not limited to inserting keywords from a vacancy. It includes content, visual identity, emphasis, tone, and context. The result is a small family of CVs rather than one document being stretched awkwardly across every possible job. Each version keeps the same underlying career history while presenting the parts that matter most for a particular type of role. The biggest improvement is not only speed but consistency. The workflow reduces repetitive manual rewriting while keeping each application specific, personal, and carefully matched to the role. It turns roughly two hours of manual polishing per strong vacancy into about twenty minutes of collaborative refinement, with fewer opportunities for small final-stage errors to spoil an otherwise strong application. Step-by-step: 1. I use GPT to scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. 2. When a vacancy looks promising, I identify the relevant sector, role, and type of impression I want to convey. 3. I use Canva and GPT to create and maintain several base CVs, each with its own layout, colour use, visual tone, and photo selection. 4. For each base CV, I review several possible photos and choose the one that best fits the role and the impression I want to convey. 5. I select the base CV that best matches the vacancy. 6. I adapt the CV by emphasizing the experience, tasks, strengths, and values that are genuinely most relevant instead of rewriting my entire work history. 7. I use GPT to personalize the motivation letter in a tone shaped through months of conversation with me, while matching the vacancy’s language, priorities, and culture. 8. I check the tone, review the application for small errors such as typos, and prepare the final application. 9. I complete the personalization process in around twenty minutes on average instead of spending roughly two hours on manual polishing for a strong vacancy.

I built Timelanes so you can type any topic into one text box, such as “The Space Race” or “my grandfather's war years,” and generate a visually engaging, sourced, shareable timeline in seconds. Step-by-step: 1. Type a topic into the text box. 2. Let AI generate the timeline with dated events, short descriptions, and source links. 3. Review citation coverage for each event to see what's verified at a glance. 4. Edit and reorder events, add images and milestones, and choose a theme. 5. Publish the timeline with one click to create a shareable page and embeds that auto-render in Substack and Notion. 6. Export the timeline as a PDF, Markdown file, or CSV. Bonus: Compare mode places two or more timelines on one shared axis so overlaps stand out.

My mum’s 80th birthday is next week, and my dad, sister, and I wanted to create a photobook of her life. My dad scanned hundreds of photos from over the years and sent them to me to clean up. Because we were working to a tight deadline and I was away on holiday, I didn’t have time to open and edit each image individually in Photoshop. I asked ChatGPT to build a tool that accepts a folder of scanned images in various formats, including scans containing single or multiple photos, overlapping photos, and photos cut off by the scanner. The tool processed each scan, cropped and straightened the individual photos, and provided a UI where I could review the results and make manual adjustments to the cropping and orientation before saving the changes to new files. I then had the tool upload the images to Google Drive and create a spreadsheet with thumbnails of every image, along with a rating system. I shared the spreadsheet with my dad and sister so we could use it as a central place to rate the photos we wanted to include in the final book. This saved me hours of work and meant we could complete the photobook in time for my mum’s birthday. Step-by-step: 1. My dad scanned hundreds of photos and sent them to me as image files in various formats. 2. I asked ChatGPT to build a tool that could process scans containing single or multiple photos, overlapping photos, and photos cut off by the scanner. 3. I used the tool to crop and straighten each individual photo. 4. I reviewed the results in the tool’s UI and made manual adjustments to the cropping and orientation where needed. 5. I saved the adjusted photos as new files and had the tool upload them to Google Drive. 6. I had the tool create a spreadsheet containing thumbnails of all the images and a rating system for each photo. 7. I shared the spreadsheet with my dad and sister so we could rate the photos and choose which ones to include in the final photobook.

I need help creating a daily revision page covering Maths and English for my son as he prepares for the SEAG exam to get into Bangor Grammar School in Bangor, Northern Ireland.

I run operations for my husband’s residential architecture firm, and I have no coding background. After every site visit, dozens of photos landed in Google Drive named `IMG_8834.JPG`. Renaming and filing them took about an hour per visit, when it happened at all. When it didn’t, the photos sat unnamed and unfindable. For an architecture firm, that creates a professional liability gap because the photos document site conditions on a specific date, as well as lost portfolio material and a hole in the firm’s permanent project archive. I solved this by building a Claude skill: a saved set of instructions that runs the same way every time with one command. I trained it to examine each photo through the eye of a residential architect, describe what the image actually shows using our professional vocabulary, and rename the file in a consistent format. `IMG_8834.JPG` becomes `2026-07-17_03_side-elevation-porch-brick-piers.JPG`—searchable, legible, and filed. Because it’s a skill rather than a one-off chat, it’s a file I can hand to anyone in the office. Everyone runs the same process and gets identical output. Step-by-step: 1. I gathered the raw photos into one “unsorted” folder in Google Drive. 2. In Claude’s desktop app, I used Cowork mode, which handles actual files, and connected only that folder—not my whole Drive. This is the safety practice I’d urge anyone to follow: the skill can see and touch only what you connect, so connect the narrowest folder that does the job. 3. I created a skill that tells Claude to read each photo, identify what it shows using the industry’s vocabulary, and rename each file in a standard format. I wrote mine for residential architecture, but the approach also works for real estate, inspections, insurance, and other field work. My format is `date_sequence_description`. 4. I ran the skill with one command, and Claude worked through the folder photo by photo. 5. The renamed photos were filed into a dated site-visit folder, ready to reference by number in reports. 6. I handed the skill file to teammates so they could run it on their machines and get the same result. One caveat for anyone using this on business files: check your client confidentiality obligations and your AI vendor’s data policy before pointing any tool at project files. We did. I wrote up the full build, including how to decide when something should be a skill versus a regular Claude conversation, at The 2040 Studio: https://the2040studio.substack.com/p/every-img_8834jpg-is-snitching-on

I use ChatGPT to turn a physical trading card collection into a structured digital inventory. I start by photographing the collection and uploading the images to Google Drive. I divide the cards into logical groups and store them in separate subfolders, creating a clear structure before identification begins. ChatGPT then works through the folders in manageable batches, using OCR and visual recognition to extract information from the cards. This avoids manually uploading and describing every card one by one. I use consistent processing rules to reduce mistakes: I limit the number of cards per batch, make sure every source image is accounted for, and use one central card per photo when identification could otherwise be ambiguous. For each card, ChatGPT helps identify and organize the information I want to keep: card name, set code, set or series, edition, rarity, language, quantity, approximate year or period, condition notes, confidence level, and the source image. The useful part is not just OCR or card recognition. It is the combination of a structured Google Drive archive, visual identification, consistency checks, and systematic data extraction across a large physical collection. When identification is uncertain, I record that uncertainty rather than silently guessing. Damaged or unusual cards can also receive specific notes, such as foil scratches, edge wear, folds, or other condition issues. Once all folders have been processed, I consolidate the separate batches into one central inventory so I can check duplicates, quantities, and inconsistencies more easily. I first used this workflow for a Yu-Gi-Oh! collection and plan to reuse the same process for a Harry Potter trading card collection. The result is a searchable, structured overview of a physical collection that can later be used for valuation, selling, insurance, cataloguing, or simply knowing what is actually in the boxes. Step-by-step: 1. I photograph the physical trading card collection. 2. I upload the images to Google Drive and divide them into logical groups stored in separate subfolders. 3. I process the folders with ChatGPT in manageable batches using OCR and visual recognition. 4. I limit the number of cards per batch, account for every source image, and use one central card per photo when needed to reduce ambiguity. 5. I extract each card’s name, set code, set or series, edition, rarity, language, quantity, approximate year or period, condition notes, confidence level, and source image. 6. I record uncertainty instead of silently guessing and add specific notes for damaged or unusual cards, such as foil scratches, edge wear, and folds. 7. I consolidate the processed batches into one central inventory to check duplicates, quantities, and inconsistencies. 8. I reuse the process for additional collections, including my planned Harry Potter trading card collection.

I use Claude Cowork and Meta MCP to map my existing ad creative data and find winning combinations. Once the data is mapped, I can usually identify one concept that wins more often than the others, has absorbed more spend, and still maintains an efficient CPA. That is the winning concept, and it is where I should focus 60% to 80% of my production time. Step-by-step: 1. I connect Claude Cowork to Meta MCP and Google Drive. 2. I place the brief for every ad I published in the past 365 days into one Google Drive folder. Each brief includes the script, format, offer, and persona it was written for. 3. I prompt Claude to work through the Drive folder and build a spreadsheet tagging every ad by persona, angle, offer, and format. 4. I prompt Claude to pull each ad’s spend and CPA through Meta MCP and add those metrics to the spreadsheet. 5. I ask Claude to turn the spreadsheet into a flowchart in which persona branches into angle, angle branches into offer, and offer branches into format. Each branch includes its spend and CPA. After the data is mapped, I use the flowchart to identify the winning concept and prioritize it in production.

I turned tedious Google Business Profile spammer tracking into a Claude Skill. It organizes the research and puts all findings into an XLS file that can be submitted through the Google Business Redressal form and the GBP forum for escalation by a Google Product Expert for Google review. Step-by-step: 1. I created a list of suspected spam profiles with each business name and Google Maps URL. 2. I asked Claude to examine each profile, determine its website, and identify any relationships between the domains. 3. I asked Claude to identify other businesses operating at the listed addresses. 4. I asked Claude to determine whether each profile is listed with the country’s primary Google data provider or providers. 5. I asked Claude to determine whether each profile is listed in a state or government registry. 6. I asked Claude to analyze commonalities across the profiles’ reviews, descriptions, and photos. 7. I asked Claude to identify patterns indicating whether the profiles are fake or real and whether they are related in some way. 8. I had Claude compile all findings into an XLS file for submission through the Google Business Redressal form and the GBP forum.