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.

Every Friday, I inspect random parts of my company the way a factory owner walks the floor: looking for errors, slow processes, and handoffs that shouldn’t exist. The inspection takes 30–60 minutes, but the preparation used to take longer—choosing what to check, finding the right links, and remembering what I flagged the previous week. I turned the entire preparation process into one scheduled Claude task. It runs before I wake up on Fridays, builds the full inspection report in Notion, verifies whether last week’s findings were resolved by checking Slack for evidence, and sends me the link by DM. I arrive to a finished briefing and focus only on judgment. You need Claude with scheduled tasks, plus Notion (or your company’s notes page) and Slack connected. If you use different tools, the prompt can be adapted; the structure is what matters. Step-by-step: 1. Create a page or database in Notion where the weekly reports will live. 2. In Claude, create a new scheduled task, set it to run early Friday morning in your time zone, and paste the prompt below. 3. Replace every bracketed section with your own details. 4. Customize the checklist and wildcard pool for your business. Mine is built for a media company; yours might inspect deployments, client deliverables, or a sales pipeline. 5. Run the task once immediately to identify configuration issues before relying on it on Friday. I use this prompt: ```text You are preparing [your name]’s weekly Digital Factory Inspection report. Context: [your name] runs [your company — one line on what it is and team size]. Every Friday they “walk the factory floor” digitally: inspecting parts of the company looking for errors, slow processes, unnecessary handoffs, and automation opportunities. Your job is to prepare the inspection report in advance so their 30-60 minutes is pure judgment, not gathering. You prepare; they inspect and decide. All reports live in [link to your Notion inspections database or page]. Do not create pages anywhere else. Steps: Read the most recent report. Collect: (a) any findings not clearly marked resolved, and (b) which wildcard areas were used in the last two reports. Create this week’s report as a new entry there, dated today, matching the existing format. Build the content in this order: A. “Follow-ups from last week”: every unresolved finding, each with its owner and a status check — search Slack public channels for evidence it was addressed and mark it “looks resolved”, “no activity found”, or “unclear”. If none, write “No open follow-ups.” B. The standard checklist — include ALL of these every week. Under each: random samples with direct links, 3-5 recent data points you can actually reach, and 2-3 sharp inspection questions: What is slow here? Which handoffs could disappear? What should be automated? What would make this 10 percent better this month? Where something sits behind a login you don’t have, do not attempt access — link it and list what to look for. Randomize sample picks genuinely, and never reuse a specific sample from the previous two reports. [Your standard areas — mine: 3 random published pieces of content, 3 random newsletters, 3 random social posts, ad performance, growth dashboards and funnels, product dev work, live events/workshops, community activity, the team’s weekly reports] C. “Wildcards this week”: pick exactly 2 at random from this pool, never repeating either of the last two weeks’ picks: [Your wildcard pool — mine: random customer support conversations, the public sign-up flow walked as a new user, one specific finance item to verify, one email automation sequence, ads on a secondary channel, one random SOP compared against how the team actually works now, one recent client/sponsor deliverable] D. “Notes \u0026 findings”: empty except the line “[Your name] fills this in during inspection — tag each finding with an owner and Open/Resolved.” E. “Wildcards used”: list the 2 wildcard names, for future rotation. Send [your name] a Slack DM (member ID [find yours in your Slack profile menu]) with the report link, the count of open follow-ups, and the 2 wildcards. Nothing else. Hard rules: this report contains candid internal assessments — the DM goes ONLY to [your name], never to any channel, and you never message anyone else. Read-only everywhere except the one report you create and the single DM. Do not reply to emails, do not post, do not modify existing pages. If Notion is unreachable, send the full report as the DM. If Slack is unreachable, still create the report. If both fail, do nothing. ``` The follow-up section is the whole point. I keep the workflow read-only: the agent gathers and drafts, but it never messages my team or touches live systems. Any feedback after the inspection comes from me, in my own words. Randomness is a feature, not laziness.

I’ve been using a simple AI workflow to capture and act on my best ideas before they get lost between meetings. It’s been so useful that I wanted to share it for others to borrow. Here’s what I do: Step-by-step: 1. While walking between meetings, I open Wispr Flow on my phone and dictate every idea and takeaway into an Apple Note, completely unstructured. The goal is to get everything out of my head before the next meeting overwrites it. 2. The notes sync automatically to my Mac, where a scheduled Claude task runs at the end of the day through the Claude Desktop app and simple connectors. It reads all the voice notes I’ve added. 3. Claude structures the notes in Notion, where I do most of my work, turning them into to-do tasks with key ideas, action items, and suggested deep-work blocks in my calendar for the bigger items. I no longer stress about forgetting important meeting takeaways because, by morning, I have actionable next steps waiting for me. I never have to touch my keyboard.

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 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.

I ran a prompt to read and listen to all key notes from companies in the medical device industry over the last year. I used it to identify where attention was being drawn and where companies were investing. This helped me see the trends companies were discussing, investing in, or directing their efforts toward. Step-by-step: 1. I ran a prompt over key notes from medical device companies covering the last year. 2. I used the prompt to identify where companies were directing attention and investment. 3. I reviewed the results to see which trends companies were discussing and prioritizing.

At some point, my newsletters stopped feeling like reading and started feeling like debt. The information was good; I just couldn’t keep up. I wanted a way to turn that backlog back into something useful. So I built The Daily Nexus, a private tool that reads newsletters from a Gmail label and creates two editions: a podcast I can listen to and a separately written, two-page newspaper I can scan. It runs on demand or on a schedule, and it can publish the audio to a private RSS feed for Apple Podcasts. The project also became a hands-on experiment in building with coding agents. Claude Code and Codex helped me implement features, troubleshoot failures, review the design, and tighten security. The stack includes Python, the Gmail API, Antigravity, Kokoro, FFmpeg, Firebase, Cloudflare Workers, and GitHub Actions. The carousel shows the rest of the flow. It started as a personal tool, but I’m sharing the template for anyone who wants to adapt the idea. Each deployment uses its own accounts and credentials, and the design aims to avoid additional API costs by using an existing AI subscription and available free tiers. GitHub Repo Template: https://lnkd.in/eYceS4KR Step-by-step: 1. I label the newsletters I want to process in Gmail. 2. I run The Daily Nexus on demand or on a schedule so it can read the newsletters from that Gmail label. 3. The tool creates a podcast edition and a separately written, two-page newspaper edition. 4. I listen to the podcast or scan the newspaper, depending on how I want to catch up. 5. When needed, the audio is published to a private RSS feed for Apple Podcasts. 6. I use Claude Code and Codex to implement features, troubleshoot failures, review the design, and tighten security. 7. Each deployment uses its own accounts and credentials, with Python, the Gmail API, Antigravity, Kokoro, FFmpeg, Firebase, Cloudflare Workers, and GitHub Actions supporting the workflow.

Take the AI out of AI-written text. We all know its characteristics: it is sometimes overwritten and repeats itself. /humanise

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.

My calendar is wall-to-wall meetings, so I built a Scheduled Claude Cowork task that automatically preps me for all of them (sort of like an AI Chief of Staff). Every morning, it scans my calendar, then pulls past emails, Slack, and my Granola notes for each call to build a context brief that takes 30 seconds to read before each meeting. It's simple and keeps me sharp for every meeting. Step-by-step: 1. I created a Scheduled Claude Cowork task that runs before my meeting day begins. 2. I had it scan my calendar and identify every meeting that needed preparation. 3. For each call, it pulled relevant past emails, Slack messages, and Granola notes. 4. I asked it to synthesize those sources into a short context brief with the information I would need in the room. 5. I read each brief in about 30 seconds immediately before the meeting.

I gave Claude Cowork a list of insurance providers and asked it to get quotes using my specific details, including my house address, number of bedrooms, and security features. I asked for multiple quotes covering extras, monthly versus yearly payments, and different excess levels. I then asked Claude Cowork to review the policy terms and online reviews to identify any potential gotchas. This helped me compare more than 20 quotes from major providers and choose the insurance that best suited my house. Step-by-step: 1. I gave Claude Cowork a list of insurance providers and my specific house details. 2. I requested quotes with different extras, payment frequencies, and excess levels. 3. I asked Claude Cowork to review the policy terms and online reviews for potential gotchas. 4. I compared more than 20 quotes and selected the insurance that best suited my house.

My Downloads folder had been filled with random files for the last six months, and I'd been procrastinating on cleaning it up. So I gave Claude Cowork access to the folder, and asked it to "help me organize this folder." In a few minutes, it went through almost 100GB of files, arranged them by file type and folders, and removed duplicate files in just a few minutes, something that could have cost me hours of work. Step-by-step: 1. I gave Claude Cowork access to the Downloads folder that had accumulated files for roughly six months. 2. I asked it to inspect the folder and propose a sensible organization rather than specifying every destination myself. 3. I had it group the files into folders based on type and content. 4. I asked it to identify and remove duplicate files. 5. I let the workflow process the full folder, which was nearly 100GB, in one pass.

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'm a British Academy (BAFTA) voting member. Every autumn, the members' screener platform fills up with more than 100 eligible films—major contenders, obscure documentaries, and hidden gems all mixed together. I have a few weeks to decide what deserves my viewing time before I vote. The fun challenge is finding the sleeper nobody has heard of yet; the problem is the sheer volume of clutter I have to sift through. I built a workflow in Claude Cowork that turns the overwhelming list into an ongoing conversation with a curator that knows my taste. I started by inputting my film notes from previous years: what I watched each season, what I loved, what I abandoned, and why. Claude stores this in persistent memory alongside a growing profile of my taste, including my go-to critic, my low tolerance for slow cinema, the kinds of films that reward me, and my rule that a film gets minutes—not acts—to earn my time. Every session starts with this context already loaded. When a new batch of films drops, Claude opens the screener platform in its built-in browser, reads the full slate, and sends research agents to cover every title in parallel. They research the director, cast, runtime, Rotten Tomatoes scores, festival prizes, awards buzz, and whether my favourite critic has reviewed each film. Claude even pulls the transcripts of his YouTube reviews and summarises the verdict. In about 20 minutes, a 37-film batch is triaged into three categories: priority viewing, worth sampling, and skips. Slow starters are flagged so I know which films need a committed evening and which get 10 minutes to prove themselves. As I watch, I feed verdicts back in plain English, such as “gave it ten minutes, too abstract.” Claude updates both the tracker and its model of my taste, so each recommendation round becomes sharper. By voting time, I have a complete, searchable record of everything I watched and what I thought of it, built conversationally throughout the season. Step-by-step: 1. I input my film notes from previous years—everything I watched, loved, and abandoned, along with my reasons. Claude saves them to persistent memory, which every future session loads automatically, and builds a profile of my taste from them. 2. When new films appear on the members' platform, Claude opens it in its built-in browser and reads the full list of titles. 3. Claude launches parallel research agents to cover every title, including the cast, crew, runtime, Rotten Tomatoes scores, festival prizes, awards buzz, and my preferred critic's verdict from YouTube review transcripts. 4. Claude compiles a triage tracker that ranks every film by viewing priority, flags likely sleepers, and warns me about slow starters. 5. As I sample films, I report quick verdicts in plain English. Claude logs them and continuously refines its profile of my taste, so recommendations improve throughout the season. 6. At voting time, I have a complete record of what I watched, what I thought, and why, built conversationally across the season.

I quickly capture my thoughts in Apple Notes, which serves as my mobile inbox. A scheduled Claude Cowork skill reads those notes through the desktop connector on my MacBook and processes them into my calendar and Obsidian. I organize notes using either the “Forever Notes” method (https://www.myforevernotes.com/docs/journal) or a simple YYYYMMDD daily-note format. Time-log notes record a start time, end time, and description, then save that information to the calendar app. The calendar holds the raw data, which I can extract into a separate Excel sheet if necessary. Planned schedules are checked against existing calendar entries. If an entry is already present, it is highlighted; if it is missing, it is added. This saves me the time and effort of opening a link or editing multiple fields in the calendar app. Random thoughts are filed into my second brain in Obsidian. They are processed with templates, moved to specific folders, or intentionally tagged. Apple Notes, Obsidian, Apple Calendar, and Google Apps are free. The only subscription I need is a Claude account to use Claude Cowork. Step-by-step: 1. I capture thoughts and other information in Apple Notes on my mobile device. 2. A scheduled Claude Cowork skill reads the notes through the desktop connector on my MacBook. 3. I organize the notes using the “Forever Notes” method or a YYYYMMDD daily-note format. 4. I process time-log notes containing a start time, end time, and description, then save them to the calendar app. 5. I check planned schedules against existing calendar entries, highlighting entries that are already present and adding those that are missing. 6. I extract the calendar’s raw data into a separate Excel sheet if necessary. 7. I process random thoughts in Obsidian with templates, move them to specific folders, or intentionally tag them.

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.

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.