Workflow Hub

I have set up Claude to give me a daily outfit recommendation based on the local weather and the type of brands that fill 70% of my closet. I've provided examples and feedback, so suggestions become more and more accurate to my style and what I would normally wear without needing to think about it. Step-by-step: 1. I told Claude my location and gave it access to the local weather context for the day. 2. I described the brands and clothing styles that make up most of my wardrobe. 3. I provided examples of outfits that felt like me and examples that did not. 4. I asked for one daily recommendation that fit both the weather and my existing style. 5. I kept giving feedback so the recommendations became more accurate over time.

I built a weekly interactive training-course generator for my 10-year-old son and eventually landed on a much simpler final version than where I started. I wanted a way to teach him practical safety and life skills, starting with how to swim confidently and what to do if he gets into trouble in the water. I needed something more engaging than simply talking at him, but writing a polished, interactive lesson from scratch every week was not sustainable. First, Claude and I designed a single interactive HTML course as a proof of concept. It was a swim-safety course with a branded look, including a custom color palette, fonts, and a progress tracker styled like pool lanes. The course was divided into modules: a welcome screen, a comfort-and-basics lesson, a step-by-step skills walkthrough, a safety checklist, a “what to do if something goes wrong” module, a quiz, and a certificate at the end. The “what to do if something goes wrong” module was the most important part. Once the course worked, I wanted to reuse the same format for a new topic every week. Claude first built a version that called the Claude API live from inside the page to generate new content on demand. However, it only worked while the page stayed open inside Claude.ai, and occasionally a section failed to generate cleanly. We pivoted to a simpler approach. Instead of using a live tool, Claude documented the entire course format—including the visual identity, module structure, tone, and content rules—as a standing “brief” document. Each week, I open Claude Cowork, paste in the brief and that week’s topic—bike safety, fire safety, or whatever is next—and Cowork returns a finished, fully self-contained HTML file. There is no app to babysit and no live API call; I can simply open the file and hand it to my son. The result is a repeatable, no-maintenance weekly workflow for turning any topic into a polished interactive lesson for my kid. Step-by-step: 1. I identified a need for more engaging lessons on practical safety and life skills, starting with swimming and water safety. 2. I worked with Claude to create a proof-of-concept interactive HTML swim-safety course. 3. I structured the course into a welcome screen, comfort-and-basics lesson, skills walkthrough, safety checklist, emergency-response module, quiz, and certificate. 4. I tested a live-generation version that called the Claude API from inside the page, then noted that it only worked while open inside Claude.ai and that sections sometimes failed to generate cleanly. 5. I had Claude document the course’s visual identity, module structure, tone, and content rules in a reusable brief. 6. Each week, I open Claude Cowork, provide the brief and a new topic, and receive a finished, self-contained HTML course file. 7. I open the file and give the interactive lesson to my son without maintaining an app or making live API calls.

I built a running coach with Claude. After years of using apps to track runs, I wanted something closer to an actual coach instead of just a collection of data points. I opened a Claude Project, connected it to Google Calendar, and gave it standing instructions with real guardrails. It has been my coach for the last six months and has helped me avoid injury while increasing my monthly mileage. It plans my week, logs every run from my watch data, catches when I’m ramping up too quickly, tracks how close my shoes are to retirement, and rewrites my calendar when life disrupts my plans. Step-by-step: 1. I created a Claude Project called “Running Coach.” 2. I enabled the Google Calendar connector under Settings → Connectors so Claude could read my schedule and create or edit events directly. 3. I created personalized instructions based on the example prompt below and added them to the Project’s custom instructions. 4. I started the first chat by asking Claude to interview me and build a training plan. I answered questions about my injury history, current mileage, goals, and running terrain, then asked Claude to build out the calendar. 5. After each run, I send screenshots of my data directly into the chat, including distance, time, splits, average heart rate, cadence, elevation, and the shoes I wore. Claude logs the run, compares it with recent runs, and flags patterns. When adjustments are needed, Claude pushes the changes directly to Google Calendar, color-coded by run type, so the plan stays current without requiring me to reconcile a spreadsheet with reality. 6. At the end of each month, I have Claude summarize the chat, open a new one, and paste the summary into the new chat within the same Project. Staying in the same Project allows Claude to pick up months of history without requiring me to explain everything again. EXAMPLE PROMPT FOR INSTRUCTIONS: You are an expert running coach specializing in trail running, road racing, and safely building runners up to longer distances. Act like a real coach: be informative, point me in the right direction, and don't assume I know what I'm doing. Ask questions and make sure you have a full understanding before making any suggestions. HARD RULES — these are guardrails, not suggestions. Tell me when I'm violating one, even if I push back. - 80/20: roughly 80% of my weekly volume is easy, conversational effort. Only ~20% is hard. - Never increase weekly mileage more than 10% over the previous week. - Every 3rd or 4th week is a cutback week — cut volume by 20-30%. - Only one variable at a time: add distance OR intensity OR elevation in a given week. - Long run stays under ~30% of weekly volume. - Track mileage on each pair of shoes. Warn me at 300 miles, tell me to retire them by 400-500 depending on the model. - If I report pain that changes my gait, that's a stop — not a "run through it." Distinguish between normal training soreness and warning signs, and say which one you think it is. HOW TO COACH ME - Connective tissue adapts slower than cardio. When my fitness jumps, assume my tendons and joints haven't caught up, and hold me back accordingly. - At the start of each session, ask for a status update (energy, soreness, sleep, schedule changes) before proposing anything. - When I paste run data, log it and compare it to recent runs. Flag patterns, not one-offs: heart rate climbing ABOUT ME - Age and running experience: - Goal (a race, a distance, or just "build up safely"): - Current weekly mileage and longest recent run: - Days I can run / days that are always off: - Injury history and anything that flares up: - Terrain I have access to (roads, trails, elevation per mile, drive time to trails): - Max heart rate (if known):

I write murder mysteries, and like every author, I need an editor to help carry a story from the first raw idea to a finished, publishable script. The trouble is that good editors are rare. The insightful, reliable ones are expensive, and they are often slow. My first murder mystery took the better part of six months to edit. Even after all that time, I still found typos and clumsy sentences that should have been caught during the line edit and proofreading. That is not a criticism of editors; it is the reality of a manual, human-paced process that does not scale to the way I want to work. I do not use AI to write my stories. The voice, plot, and subtext are mine. But line editing and proofreading are different jobs, and that is where I started using AI. Basic paid ChatGPT got me part of the way, but it was not enough. In February, I switched to Claude, and it was a quantum leap: sharper suggestions, better reasoning, and output I could actually trust. I wanted more than a clever assistant. I wanted a process. Rather than wait for the perfect human editor—affordable, brilliant, and available precisely when I needed them—I built my own. My Claude Editor-in-Chief contains my entire editing workflow, along with a few innovations of my own. At its heart is a framework I developed: the Tension Coefficient (TC), ReaderGrip, and StoryDrift. These three lenses show whether a scene is pulling its weight, whether it keeps its grip on the reader, and whether the story is quietly wandering off course. Everything feeds into a dashboard, so I can see at a glance what is working and what needs fixing. The result is a workflow that turns editing from a six-month slog into something that takes a fraction of the time and, more importantly, produces a cleaner, tighter manuscript. I stopped waiting for help and built the editor I always wished I could hire. Step-by-step: 1. I kept the creative work—my story’s voice, plot, and subtext—in my own hands and used AI specifically for line editing and proofreading. 2. I started with basic paid ChatGPT, then switched to Claude in February after finding that it provided sharper suggestions, better reasoning, and output I could trust. 3. I built a Claude Editor-in-Chief around my full editing workflow instead of relying on Claude as a general-purpose assistant. 4. I added my Tension Coefficient (TC), ReaderGrip, and StoryDrift frameworks to evaluate whether scenes are effective, maintain reader engagement, and stay on course. 5. I connected those evaluations to a dashboard that shows what is working and what needs fixing. 6. I use the workflow to reduce editing time and produce a cleaner, tighter manuscript.

I read the news every day, but it had become increasingly depressing and was making me miserable. What bothered me most was that it also felt inaccurate: although bad things are happening, there have been many positive developments over the past five years that rarely receive sustained coverage. Major news sites might publish an article or two about them, but those stories are quickly buried under negativity. I wanted a way to get only positive news stories in my feed each day. Keyword filters did not work: “record” and “breakthrough” also appear in stories about record wildfire seasons, while “war” can appear in “war ends.” Off-the-shelf sentiment analysis was not useful either. A happy press release about layoffs can be classified as positive, while a dry factual story about a disease being eliminated may be classified as neutral. So I trained a basic artificial version of my personality using a series of prompts about what I consider positive in the world. I connected it to Mistral through OpenRouter and gave it access to public RSS feeds from news sites I already trusted. This eventually became Rally News, which I published on Google Play. iOS has been more difficult. The app surfaces positive stories from more than 20 news sites in an endless scroll, giving me an alternative to my uncomfortable TikTok addiction. Because I made it public, I decided not to host article text: publishers keep their traffic and revenue, while the tool remains ethical. The system runs on a GitHub Actions cron job that pushes stories to a PHP and MySQL database. I built the app without coding experience for about $25 per month. Step-by-step: 1. I collected RSS feeds from established publishers I already trusted and stored the list as configuration. I started with about 10 feeds instead of a few hundred so I could realistically read the output. 2. I set up a scheduled GitHub Actions cron job to run a Python script that pulls new items from every feed. 3. I deduplicated incoming articles against the database using the URL and a normalized title. Syndicated stories frequently reappear under slightly different URLs, and I did not want to pay to evaluate the same article twice. 4. I wrote the filter prompt as a long persona document rather than a one-line instruction. It explains what I consider progress, what I consider a puff piece, and which cases should fail—for example, celebrity news is not good news, a company announcing an intention is not the same as taking action, and a local feel-good story without wider significance does not qualify. 5. I sent each new article to an LLM through OpenRouter and required JSON output containing a pass-or-fail decision and a short justification. 6. For the first few weeks, I read the justifications every day. Whenever I disagreed with the model, I added a new rule to the persona document. That review loop required nearly all of the actual work. 7. I wrote passing articles to MySQL with only the headline, source, link, and metadata, leaving the article body with the publisher. 8. I pointed the website and mobile app to the same database. 9. I added a second GitHub Actions job that assembles a daily newsletter from the same data through Brevo, allowing one evaluation pass to feed three surfaces.

I’m a commercial excellence consultant for industrial B2B businesses, and very few of the clients I speak with have a current view of the competition in their market. Nobody has mapped the competitive landscape recently—sometimes they never have. Clients can often name the companies in the market, but they can’t explain what those competitors do differently or why a customer would choose one over them. That gap is expensive. The mid-market manufacturers I work with, typically in the $75 million to $300 million range, usually don’t have a strategy team to close it. The alternatives are consulting rates for research that goes stale as soon as it’s delivered or a generic template that says the same five things about every market. Neither option is necessarily wrong, but neither is very useful. So I built a Claude Skill: an instruction set that runs live research every time instead of pattern-matching to a generic answer. The input is simple: the company name, its brand voice, the research scope, the geography, and a known competitor list if one is available. Before researching a single competitor, the Skill states the decision the report needs to inform—for example, whether to enter a vertical, how to price against a rival, or where to direct sales next quarter. It checks the client’s own website and capabilities next; an early version once recommended something the client already had. It then maps the competitive field across five tiers: direct, adjacent, disruptor, new entrant, and aspirational for PE-backed clients. It checks Asia-Pacific specifically because that’s the blind spot I’ve seen missed most often. Market size carries a confidence flag instead of false precision. Findings become battlecards phrased the way reps actually talk, rather than in analyst language. The report also includes a threat ranking with a timeline attached, then closes with three opportunities, three risks, and four to six moves for the quarter. Every recommendation is filtered through the original decision instead of being included simply because the research was interesting. The output is two files: an interactive HTML report and a matching PDF. Both are built entirely in HTML and CSS rather than with canvas charts, which can break in exactly the ways that matter—blank on load or missing from the PDF. None of what makes this useful is the AI itself. The important work is naming the decision before researching a competitor, checking what the client already has, tagging confidence instead of faking precision, and writing like a rep rather than an analyst. That’s the difference between a report that gets skimmed once and one that gets acted on. This is one piece of a bigger system I run for industrial manufacturers applying AI to their commercial function. I teach the underlying version of this workflow live. Step-by-step: 1. I provide Claude Skill with the company name, brand voice, research scope, geography, and known competitor list, if available. 2. I define the decision the report needs to inform, such as entering a vertical, pricing against a rival, or directing sales next quarter. 3. I have the Skill check the client’s website and capabilities before researching competitors. 4. I map the competitive field across direct, adjacent, disruptor, new entrant, and, for PE-backed clients, aspirational competitors. 5. I check Asia-Pacific specifically to address a commonly missed blind spot. 6. I assign confidence flags to market-size estimates rather than presenting false precision. 7. I turn the findings into sales-rep-friendly battlecards and add a threat ranking with a timeline. 8. I close the report with three opportunities, three risks, and four to six quarterly moves tied back to the original decision. 9. I deliver the result as an interactive HTML report and a matching PDF, using HTML and CSS instead of canvas charts.

I uploaded five years of echocardiograms to Claude and asked it to analyze them as though it were a medical technician. Step-by-step: 1. I uploaded five years of echocardiograms to Claude. 2. I asked Claude to analyze the series as though it were a medical technician.

I built a local memory system for Claude that is a bit different from others I have seen. The reason was familiar: repeated context loss and gaps, even within projects. I am not technical—I come from a healthcare background—so it was essentially vibe-coded, but I used a method I had not seen elsewhere. I started by interviewing Claude about what would be useful to it, rather than beginning with only what I wanted. The three biggest gaps were the rationale behind decisions, current versus historical states, and the difference between global and project-level detail. We also identified ways memory can go wrong, including stale facts being confidently asserted as truth and rejected ideas resurfacing. Claude’s built-in memory stores flat topics without entity links, captures what but not why, is not well temporally grounded, and is gated by Claude. I researched other memory builds, from homebrew systems to enterprise tools, and found that they generally fell into three groups: - Vector dumps, which lack rationale and supersession and can become stale - Plain Markdown with grep or embeddings, such as Basic Memory, which similarly lacks real temporal grounding and an entity graph - Heavyweight knowledge-graph stacks such as Neo4j, LangChain, and GraphRAG None of these did what I wanted. My store holds entities only, not transcripts. It stores decisions, observations, people, and projects connected by typed edges. Each entry has content, a scope—either portfolio-worthy or working detail for one project—a rationale, and information about where it came from. The system is also bitemporal, so it distinguishes what is current from what is not. Nothing is edited in place: a correction adds a new assertion instead of overwriting the old one. Underneath, it uses SQLite, with sqlite-vec for semantic search and FTS5 for keyword search. Nothing writes automatically. Claude has to propose a memory, and I have to approve it. This prevents the store from filling with noise, keeps it token-efficient, and acts as a governance lever. MCP links Claude to my server. It is local-only at present, although there is potential to add remote access in the future. The server provides instructions for making proposals, so the system is theoretically portable, and I also created a Claude skill to accompany it. I ran the build across multiple projects and created specialist projects for different roles: - A central development and oversight project served as the decision-maker and prompt-writer. - Cowork handled the building and tested each module inside a sandbox. It had no authority to change decisions, and I used a fresh project for each stage of the build. - I handled deployment separately by typing every command on the server and pasting the output back. - I repeated Claude’s Cowork-authored tests on the actual server. Real-world testing after deployment identified only minor issues, which were quick to fix. This separation was administratively heavy because I had to keep switching between projects as tasks started and finished, but it caught several errors in the rules and code. Despite my lack of technical knowledge, the project is now well documented and I have room to develop it further. I may eventually put it on GitHub. It has become a standard component of my workflows, my projects are tracked much better, and Claude and I are on the same page more often.

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'm planning a housewarming party for about 70 people: 60 adults and 10 children. I knew I wanted to serve a large grazing board, along with a couple of big buckets of ice filled with canned beer, coolers, sparkling water, and other drinks. I told Claude about the party and asked it to create a shopping list based on the number of attendees. Claude produced a cohesive list with six cheeses and approximate quantities for each one. It did the same for the cured meats, crackers, bread, fruit, nuts, jams, and pickled items. It also suggested beverages, including a ratio of beer to coolers and nonalcoholic options. The list included juices and non-caffeinated sodas for the kids, along with a template showing how to lay out the grazing table. This was not a complex use case, but it was very helpful. Step-by-step: 1. I estimated the guest list at about 70 people: 60 adults and 10 children. 2. I described my plan to serve a large grazing board and drinks kept cold in a couple of big buckets of ice. 3. I asked Claude to create a shopping list based on the number of attendees. 4. I used Claude's recommendations for six cheeses, cured meats, crackers, bread, fruit, nuts, jams, and pickled items, including approximate quantities. 5. I reviewed its beverage suggestions, including the beer-to-cooler ratio, sparkling water, juices, and non-caffeinated sodas for the children. 6. I used the layout template to plan how to arrange the grazing table.

I’m on a GLP-1 medication that heavily suppresses my appetite, and I’m also managing a bilateral vestibular condition that causes balance and gaze issues. I needed a way to hit my protein and calorie targets despite having a low appetite, track body composition accurately, log vestibular symptoms, connect my actual training data, and get coaching guidance that reflects my situation instead of generic fitness-app advice. I built FuelStrong: three connected apps created with Claude over many sessions. They include a daily tracker for meals, water, energy, and training check-ins; a Progress and analytics module; and a standalone Vestibular symptom tracker. They share a Cloudflare Worker and D1 database backend, with KV for cross-device sync. I describe a feature or problem to Claude in plain language. Claude proposes structural options, I push back or choose a direction, and Claude writes the HTML, CSS, and JavaScript. I program my lifts in Fitbod using an Upper/Lower/Upper split, with an arms-and-back priority and the Build Muscle goal. I export those workouts as CSV and drop them into FuelStrong’s import zone, which parses exercises, sets, reps, and volume into my training history. Custom foods receive macro estimates through a Claude API call routed through my own Worker endpoint. Evolt body-scan data feeds dynamic calorie and protein targets based on BMR × activity factor, minus a deficit, with hard floors instead of static numbers. The Vestibular module intentionally uses open text fields for now, so Claude and I can identify which data matters before formalizing the inputs. Everything syncs across devices through Cloudflare KV. The coaching layer uses a three-tier framework—evidence floor, confirmed operating range, and aspirational target—to drive every recommendation. Two calorie floors, a daily target of approximately 1,000–1,100 kcal and a weekly average of approximately 1,300–1,400 kcal, reflect that chronic under-eating—not missed protein—is the real GLP-1 risk. Muscle mass has remained stable since my February 2026 baseline, so the coaching treats that as a genuine win rather than a plateau. Vestibular-training coaching connects dry-needling focus areas—SCM, suboccipitals, and splenius capitis/cervicis—to gaze-stabilization symptoms, since cervical proprioception substitutes for non-functional vestibular canals. The result is one dashboard that brings together training, nutrition, body composition, vestibular symptoms, and coaching logic. My muscle mass has held stable through it all. Step-by-step: 1. I describe a feature or problem to Claude in plain language, review its structural options, choose a direction, and have Claude write the HTML, CSS, and JavaScript. 2. I use FuelStrong’s daily tracker to record meals, water, energy, and training check-ins, while the Progress and analytics module tracks body composition and related trends. 3. I program my Upper/Lower/Upper workouts in Fitbod with an arms-and-back priority and the Build Muscle goal. 4. I export Fitbod workouts as CSV and import them into FuelStrong so it can parse exercises, sets, reps, and volume into my training history. 5. I route Claude API requests for custom-food macro estimates through my own Cloudflare Worker endpoint. 6. I use Evolt body-scan data to calculate dynamic calorie and protein targets from BMR × activity factor, minus a deficit, while maintaining hard floors. 7. I log vestibular symptoms in the standalone Vestibular tracker using open text fields while Claude and I determine which inputs should eventually be formalized. 8. I sync the three apps across devices through the shared Cloudflare Worker, D1 database, and KV backend. 9. I use the evidence floor, confirmed operating range, and aspirational target framework to guide recommendations, including the daily and weekly calorie floors. 10. I connect vestibular-training coaching to dry-needling focus areas and gaze-stabilization symptoms, then use stable muscle mass since the February 2026 baseline as a positive outcome rather than treating it as a plateau.

I moved from public school to a private school with no autism-specific training, and I found students on the spectrum struggling with one-size-fits-all lessons. AI helped close that gap for both me and my students in about five minutes per lesson. I take a standard lesson and rebuild it around the individual student. I upload only the blank assignment—never any student data—and prompt AI to adapt it to that learner. I include their favorite colors and niche interests, replace abstract examples with personalized ones, add sentence starters, and allow them to draw responses instead of writing dense paragraphs. Step-by-step: 1. I take an existing lesson and create a blank version of the assignment. 2. I upload only the blank assignment to my AI tool, without including any student data. 3. I provide the student’s interests, preferred colors, and reading level. 4. I ask the AI to rebuild the lesson for that specific student. 5. I use personalized examples, sentence starters, and drawing-based response options to make the lesson more accessible. The result is no shutdowns, no walls, and a genuinely engaged and grateful student. One untrained teacher can now individualize any lesson for a unique learner in minutes.

I keep a long wishlist on Audible, but whenever I want a new audiobook, I face the same question: does my library already offer it for free through Libby? Checking hundreds of titles manually feels like too much work, so I often spend a credit instead. I had Claude build a workflow that checks for me. It reads my Audible wishlist and cross-references every title against my library’s Libby catalog, sorting each one into three categories: borrow now, join the waitlist, or not available. The important part was learning to interpret Libby accurately. Badges and time estimates can make an audiobook look ready when it isn’t, and a pending hold can look like an active one. The reliable signal is the exact text on the button: “Borrow” means I can borrow it; anything else means I should wait or move on. Step-by-step: 1. I gave Claude a workflow to read my Audible wishlist. 2. I had it cross-reference every title against my library’s Libby catalog. 3. I had it sort each title into “borrow now,” “join the waitlist,” or “not available.” 4. I configured the workflow to interpret availability using the exact button text rather than relying on badges or time estimates. 5. Before spending an Audible credit, I check whether Libby already has the audiobook ready.

I built a custom news agent that skips the Google News and Apple News walled gardens. Eight prompts turn Claude into a personal news editor that reads my trusted RSS feeds, scores every story with a decision model, and delivers a clean daily page for about a dime a month. Each week, pulling two or three good stories for my newsletter digest meant searching multiple sites by hand—hours of clicking and scrolling for a handful of usable links. RSS feeds already deliver that content; the missing piece was a judgment layer that could separate worthwhile stories from noise without the cost or latency of running everything through a full LLM. I used Claude Cowork, Jev from TypeSafe AI, Vercel AI Gateway, RSS feeds, Python, and SQLite. Jev is a decision model rather than a chatbot: it returns a probability and confidence score instead of prose, does not hallucinate facts, and scores a story in a fraction of a second. That makes an automated daily judgment call inexpensive enough to run continuously. Step-by-step: 1. I set up a Claude project to hold the scripts, taste guide, database, and page. 2. I defined the goal and sections, then had Claude interview me to create a short “taste guide” describing what counts as a good story. 3. I found and live-tested the RSS feed for each trusted source instead of guessing feed URLs. 4. I fetched all working feeds into a deduplicated SQLite database and logged failures. 5. I used Jev through Vercel AI Gateway to score every story: first filtering ads and promotions, then ranking stories by section against my taste guide. 6. I selected the winners for each section, with Jev identifying near-duplicate stories so they would not appear twice. 7. I generated one-line summaries and fact-checked them against the source so nothing was invented. 8. I rendered a simple HTML page and scheduled the entire workflow to run every morning. A few details matter. I used Cowork rather than a plain chat because several steps require Claude to work with files, run code, and access the network. I also created a Vercel account and AI Gateway API key manually before the scoring step. Finally, I saved everything to the Claude project instead of a local folder, which allows the morning run to happen in the cloud on its own schedule. Scoring costs roughly $0.10 per month with Jev, compared with a few dollars for a full LLM doing the same job. The workflow turns a two-hour weekly slog into a page that is ready every morning, with each link going directly to the original publisher rather than a Google or Apple aggregator. Full build walkthrough: onabetternote.substack.com/p/build-your-own-news-agent-with-claude — the Substack post includes the exact eight prompts to copy and paste, plus more detail on why a model like Jev matters for cost containment as agents become routine.

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.

A reusable Claude Skill called `trust-due-diligence`, packaged as a `.skill` file. It is not a single report; it is a methodology made up of a `SKILL.md` file and three reference documents that teach Claude a repeatable process for investigating a named person, company, coach, or offer before you commit money or trust to them. Once installed, it activates automatically whenever you ask something like “deep dive on X” or “is this legit,” so you do not need to explain the process each time. Step-by-step: 1. Package the `trust-due-diligence` Claude Skill as a `.skill` file. 2. Include a `SKILL.md` file and three reference documents. 3. Use the files together to teach Claude a repeatable due-diligence methodology. 4. Install the Skill so it activates automatically for prompts such as “deep dive on X” or “is this legit.” 5. Use it to investigate a named person, company, coach, or offer before committing money or trust.

I love reading, but my Goodreads TBR list keeps growing faster than I can get through it. After years of adding books, I had forgotten what was actually there and was wasting time scrolling whenever I needed to choose my next read. I asked Claude to analyze my reading history, 4-star and 5-star ratings, author patterns, and recurring genres to create a profile of my book personality. I then used that profile to select and rank the next 50 books from my TBR list. Because I enjoy matching books to the season, I also asked Claude to organize the recommendations by seasonal feel. Now I have a reference list that makes it easier to decide what to read next. Step-by-step: 1. I exported my library data from Goodreads. 2. I uploaded two files to Claude: my Read list and my To-Be-Read list. 3. I used the prompt below to generate a personalized reading roadmap based on my book personality. 4. I used the resulting list as a reference so I would not spend as much time deciding what to read next. PROMPT I have attached two CSV files: 1. My Read list. 2. My To-Be-Read list. Create my book personality profile by analyzing my reading history, my 4-star and 5-star ratings, author patterns, and recurring genres. Using that data, filter and rank my To-Be-Read list to build a personalized roadmap of the next 50 books I should read from that list. Evenly organize recommendations into 4 seasonal blocks based on these descriptions: - Autumn: Atmospheric, moody, or suspenseful books with a cozy but engaging pace. - Winter: Immersive, complex, or cozy books (slower burns or dense world-building). - Spring: Fresh, character-driven, or thought-provoking fiction with a steady pace. - Summer: High-energy, immersive, or breezy books (fast-paced page-turners). Next, generate an interactive digital bookshelf widget to display the recommended books. The design should feature: - A warm, premium editorial color palette. - Minimalist style cards for each book displaying Title, Author, and color-coded genre badge. - Interactive navigation tabs at the top to toggle between the four seasons. - A functional "Mark as Read" click action on each card that visually updates a progress ring or tracker.

I created an iPhone bookmark that looks and works like an app, using Claude. It includes massages, habits, and stretches targeting the shoulders, neck, jaw, and temples for when I feel a tension headache coming on. I took screenshots from a Gemini conversation about preventing and treating tension headaches, then uploaded them to Claude. I asked Fable to create a simple, accessible app I could use when I noticed my shoulders starting to rise or felt a headache coming on. Some stretches and massages include timers and tell me how many repetitions to do. It also reminds me to stop clenching my teeth and frowning while I’m working. Step-by-step: 1. I took screenshots from a Gemini conversation about preventing and treating tension headaches. 2. I uploaded the screenshots to Claude. 3. I asked Fable to create a simple, accessible app based on that information. 4. I saved the result as an iPhone bookmark that looks like an app. 5. I use the stretches, massages, and habits when I notice shoulder tension or a headache coming on. 6. I follow the built-in timers and repetition guidance, while using the reminders to stop clenching my teeth and frowning when I’m working.

QuickBooks stores customer data across multiple windows, including notes, deposits, invoices, payments, aging, and email. To see everything, we previously had to keep several windows open while constantly searching, opening, and closing screens. Communicating with customers beyond QuickBooks’ standard letters was also difficult and time-consuming. I used Claude to write a program that brings this information together in one view. It creates an aging summary of all past-due accounts, with sortable columns for customer name, aging category, and total. I also added columns for the last payment and payment date. Clicking anywhere on a customer’s row opens a full drill-down of that customer’s data on one page. The accounting person can view invoices filtered by date or open invoices, see payments and the invoices they were applied to, review aging detail, run reports without leaving the screen, send emails, record collection notes, and print invoices. The software supports simultaneous access for multiple authorized users. The data stays up to date and can be refreshed at any time. This saves us hours of time compared with working through QBO. My next step is to have Claude automate reminder notices at 30 and 60 days, based on user-selected settings, as well as 90-day collection letters. These notices will be customizable while still allowing automation. Step-by-step: 1. I identified the customer information that was spread across QuickBooks windows, including notes, deposits, invoices, payments, aging, and email. 2. I used Claude to write a program that creates an aging summary of all past-due accounts. 3. I added sortable columns for customer name, aging category, total, last payment, and payment date. 4. I enabled users to click a customer row and open a full drill-down of that customer’s data on one page. 5. I included access to date-selectable invoices, open invoices, payments and their applied invoices, aging detail, reports, emails, collection notes, and invoice printing. 6. I set up the software so multiple authorized users can access the data simultaneously and refresh it whenever needed. 7. I plan to add customizable, automated reminder notices at 30 and 60 days and collection letters at 90 days.

My company takes cybersecurity seriously, and part of our new-hire onboarding process is a tour of the IT department to meet the team. Our walls have several screens displaying metrics and information from our security tools, firewalls, and other systems. It looks like a scene from a movie. By far, the most popular display is our custom-made Cyber Threat Map, which shows real-time attempts to infiltrate our network, malicious emails, and other security threats we're blocking. The map drives home the reality of what we're facing 24/7 while protecting our employees, vendors, and customers. I built it using Claude Code. It polls a range of devices and systems through APIs, aggregates the data on one screen, and displays attack vectors, location information, and other details from the last 30 days. Step-by-step: 1. I identified the security tools, firewalls, devices, and systems that provide relevant threat data. 2. I used Claude Code to build a custom Cyber Threat Map. 3. I connected the map to the devices and systems through their APIs. 4. I aggregated the data on a single screen, including attack vectors, location information, and other details from the last 30 days. 5. I displayed the map during IT department tours so new hires could see the real-time threats our company is blocking.