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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 built a personal daily tracking system called Artifact. It gives me a way to log my days and turn the data into decisions over time. To use it, I copy everything attached—or screenshot the included “green orange red” example—and paste it into my AI assistant, preferably Claude. The assistant briefly interviews me and then builds a personalized daily tracking system.

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 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 asked Claude to help me build a weekly and monthly habit tracker as an artifact. I prompted it to conduct a Q&A with me so it could understand everything I wanted to include. I also asked Claude to add an insights tab and provide tips for improving my own compliance. Step-by-step: 1. I asked Claude to build a weekly and monthly habit tracker as an artifact. 2. I prompted Claude to conduct a Q&A with me about the features and details I wanted included. 3. I asked Claude to add an insights tab. 4. I asked Claude to provide tips for improving my compliance with the habits.

I open Pulse and click Pull AM Brief or Pull PM Brief, search for a topic, or paste in an article link. That triggers a call to Claude, which scans for the five most relevant marketing, AI, and CPG stories that day. I choose one story and expand it into a full seven-slide carousel. Claude writes all the copy according to our brand’s specific editorial rules. I can then generate AI imagery for each slide. The final slide gets a custom scene generated with our actual logo built into it, so it feels native to the story instead of looking like a generic closing card. Once the carousel is built, I run it through a scoring system that evaluates factors such as how surprising the opening is and how likely people are to share it. If a slide scores low, the system automatically rewrites it until it passes. Finally, it generates the caption, a call to action, and a LinkedIn post, and I download everything ready to publish. Step-by-step: 1. I open Pulse and select Pull AM Brief, Pull PM Brief, a topic search, or an article link. 2. Claude scans for the five most relevant marketing, AI, and CPG stories of the day. 3. I select one story and expand it into a seven-slide carousel. 4. Claude writes the carousel copy according to our brand’s editorial rules. 5. I generate AI imagery for each slide, including a custom final-slide scene with our actual logo built into it. 6. I run the carousel through the scoring system, which evaluates the opening’s surprise and the likelihood that people will share it. 7. The system rewrites any low-scoring slide until it passes. 8. I generate the caption, a call to action, and a LinkedIn post, then download everything ready to publish.

I continue to use Claude artifacts for all kinds of visualizations. Recently, when purchasing acoustic panels for my ceiling, I wasn't sure how many to buy or the right orientation to install them in. I had Claude create a mock-up of my room and then lay them out in different orientations and different patterns to optimize the right number, the right order, and the right way to install them. I made sure to purchase the right amount, reduced waste and cuts, and was able to better estimate costs when comparing different options. I can see this being super valuable any time I'm doing any kind of home improvement project that includes estimating materials, whether it's tile, carpeting, or any kind of paneling across an area. Step-by-step: 1. I provided the room layout, ceiling context, and the dimensions of the acoustic panels I was considering. 2. I asked Claude Artifacts to create a visual mock-up of the room. 3. I had it lay out the panels in several orientations and patterns. 4. I compared the options based on panel count, order, cuts, waste, and visual balance. 5. I used the preferred layout to estimate materials and compare the cost before purchasing.

I continue to find that Claude Artifacts (with its front-end design) is delightfully useful — and use it several times a day to learn something new, or catch up on a news story, and turn anything into a custom webpage right inside chat. Attach surveys, a long article, a spreadsheet, etc., and tell Claude to turn it into an interactive page displaying key insights. The design is impressive, and it takes just minutes. Next time, try this to quickly share findings with your team. Step-by-step: 1. I attached the source material I wanted to understand or share, such as a survey, long article, or spreadsheet. 2. I asked Claude Artifacts to turn the material into a custom interactive webpage. 3. I specified the key insights the page should surface instead of asking only for a visual redesign. 4. I reviewed the hierarchy and interactions and refined anything that was unclear. 5. I shared the finished page with the team as a faster way to explore the findings.

I asked Claude to build me an artifact that works as my own personal book tracker. It includes separate sections for books I want to read, books I’m currently reading, and books I’ve read. I also asked Claude to collect specific data points about each book and create an insights tab that can gather information about my likes and dislikes over time, then make future recommendations. I personally dislike Goodreads’ UI and don’t use its social media features, so I wanted a personalized alternative housed within Claude alongside all my other workflows—a one-stop shop. Step-by-step: 1. I asked Claude to build an artifact for tracking my books. 2. I organized the tracker into books I want to read, books I’m currently reading, and books I’ve read. 3. I specified the data points I wanted to collect for each book. 4. I asked Claude to add an insights tab to gather data about my likes and dislikes. 5. I set it up to use those insights for future recommendations and keep the entire workflow within Claude.

I fed Claude my sweat-test data, and it formulated a DIY high-carb cycling mix tuned to my sweat chemistry. It also scaled the batch to match my available supplies and generated print-ready labels and batch sheets. Step-by-step: 1. I provided Claude with my sweat-test data. 2. I used Claude to formulate a DIY high-carb cycling mix tuned to my sweat chemistry. 3. I had Claude scale the batch to match my available supplies. 4. I had Claude generate print-ready labels and batch sheets.