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Automate a Weekly Workout Log with Claude and Hevy

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.
Bridget Overson
  • Claude Cowork
  • Google Drive
3

Log your day by voice and wake up to a fully organized health tracker—completely free

I track my recovery daily—mood, energy, focus, activities, medications, and reflections—but typing everything out never lasted more than a few days. So I built a voice-first pipeline: I talk, and it automatically becomes a clean, organized Google Sheet. Step-by-step: 1. I built a free iPhone Shortcut that records my voice when I say, “Hey Siri, Log,” and sends it to the Gemini endpoint. 2. Gemini transcribes the audio and extracts structured details, including wake time; mood, energy, and focus scores; activities with categories and durations; medications; and reflections. 3. Everything is automatically organized in the appropriate place in a Google Sheet: a daily summary, activity log, metrics log, medications log, and clean, human-readable digest for each day. 4. I can say “yesterday” or “on Monday” to log something I forgot, and it files the entry under the correct date. 5. I connected the completed sheet to a separate AI chat scoped specifically to reflection, so I can ask questions such as how my exercise correlates with my mood or how my energy trends over the month. The best part is that the entire workflow runs on free tiers—Google Sheets, Apps Script, iPhone Shortcuts, and Gemini’s free API tier—so there’s zero cost to build or run it. The result is a daily tracker that takes seconds to update from anywhere, requires no typing, and turns my entries into a dataset I can actually learn from—all without writing a traditional app.
Alex Smirnov
  • Gemini
11

Replace an AI File-Transfer Workflow with a Python Desktop App

Management pushed for an AI workflow to handle a massive daily headache: staff were manually searching for, copy-pasting, and moving hundreds of files listed in Excel. I was assigned to train the team to use the AI workflow. As soon as training started, though, it became clear that this was the wrong tool for the job. Forcing non-technical staff through a lengthy process of opening browsers, writing prompts, uploading spreadsheets, and dealing with token friction created more work than the manual process. It also introduced token costs, speed bottlenecks, and hallucination risks involving local file paths. The data was already structured. It did not need semantic intelligence; it needed deterministic speed. So AI got fired from running the task. Instead, I used AI as the developer. In less time than it would have taken to train one person, I had Gemini code a standalone Python desktop app and compile it into a simple executable. Now, with zero training required, staff drag and drop their Excel list into the app, choose a destination folder, and click Run. The app executes the transfers for hundreds of files, verifies every arrival on disk, and logs missing files in seconds. The result: $0 in API tokens, two hours recovered each day, zero path errors, and 100% team adoption. A two-second drag-and-drop will always beat a multi-step prompt-engineering exercise. As a side effect, the experience also supported AI adoption. Staff are now seeing more ways AI can help rather than hinder their work. Step-by-step: 1. I evaluated the proposed AI workflow for manually searching, copy-pasting, and moving hundreds of files from Excel lists. 2. I identified that training non-technical staff to open browsers, write prompts, upload spreadsheets, and manage token friction added more work, costs, bottlenecks, and local-file-path hallucination risks. 3. I used Gemini as the developer to create a standalone Python desktop app and compile it into a simple executable. 4. I had staff drag and drop their Excel list into the app, select a destination folder, and click Run. 5. The app transferred hundreds of files, verified each arrival on disk, and logged missing files in seconds. 6. I measured the results: $0 in API tokens, two hours recovered daily, zero path errors, and 100% team adoption.
Jace Cunniffe
  • Gemini
5

Create a five-day sermon follow-up text series from a previous Sunday’s sermon

I created a Gemini Gem with custom instructions. When I paste a link to the previous Sunday’s sermon, the Gem “watches” the video and creates five messages—one for each weekday. Each message uses a specific engagement method, such as a link to watch the sermon for anyone who missed it, a graphic featuring a verse from the sermon, or a reflection question tied to the sermon’s theme. After I approve the copy, the Gem creates a properly formatted Google Sheet in my Google Drive, ready to be dragged and dropped into our text provider. Within five minutes, I have content deployed that extends the impact of the 45-minute sermon throughout the week. Step-by-step: 1. I paste a link to the previous Sunday’s sermon into my Gemini Gem. 2. The Gem watches the video and creates five weekday messages. 3. I review and approve the copy for each message. 4. The Gem creates a correctly formatted Google Sheet in my Google Drive. 5. I drag and drop the content into our text provider for deployment.
Anon Echidna
  • Gemini
  • Google Drive
0