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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
4

Agentic Coding Workflow for Planning, Building, and Reviewing Software Projects

I’ve been experimenting with agentic coding for quite some time, and I’ve noticed that the tasks I let the agent handle keep getting bigger. For larger tasks and projects, I gradually moved to a standard workflow, which I published at https://github.com/evermeer/CodingAgentOrchestration in part 2 of the tutorial. I use this workflow professionally and in my side projects. My most recent side project is Quadrim, a Four in a row game played in a 4×4×4 cube. I made it open source and included all the prompts for both the code and the artwork: https://github.com/evermeer/Quadrim It took me about eight working days to publish the first version to the app stores. Step-by-step: 1. I start by describing the idea. 2. I brainstorm the idea with the agent. 3. I use `to-spec` to create a specification document for the idea. 4. I use `to-ticket` to create independent, executable tasks. 5. I review the specifications and tickets from the perspectives of a senior software engineer, architect, security officer, and product manager. 6. I implement one ticket at a time. 7. I code-review the generated implementation for each ticket. During the pre-implementation steps, I always add this instruction to my prompt: “Ask clarifying questions until any ambiguities are resolved.” Extra fine-tuning of the idea, specifications, or plan pays off during implementation. When I let the agent build code, I ask it to use TDD, perform a code review at the end, and resolve the major and critical findings.

Edwin Vermeer
  • Claude Code
3

Build and Ship an iOS App with Persistent AI Project Memory

I am a Mohs surgeon who built and shipped an iOS app without formal software engineering training. The surprising part was not only getting AI to write the code; it was getting AI to remember what it had already done. I built ErgoSherpa because up to 90% of surgeons in my field report musculoskeletal symptoms, while our training fails to address them. I wanted to help surgeons improve their health easily between cases. It is free on the Apple App Store and at ergosherpa.com. The bottleneck was maintaining coherence over time. Many sessions seemed to start from zero: I would re-explain the architecture, then watch a fix quietly undo something I had solved earlier. Three habits fixed that. First, I created persistent project documentation: a `MEMORY.md` index file plus separate topic files for architecture and business decisions. I update them at the end of every session so a new session can read the files first and pick up where the last one stopped. Second, I stopped handing one model the whole job. I use three models and match them to the task. Claude Fable audits only. I open a separate session, point it at the codebase, and require a prioritized checklist with the file path, the problem, and the fix in one sentence—without writing code. A model that did not write the code and has no memory of the project reviews it more honestly than the session that built it. I paste that checklist into a Claude Opus session, which handles the codebase repairs. Claude Sonnet handles routine work such as content updates, image processing, and scheduled maintenance, often from handoffs written by Opus or Fable. Nothing gets implemented on the auditor’s word alone: I have Opus flag any decision that needs human input. This workflow caught a deep-link handler that accepted authentication tokens from any URL and a database policy missing its write-side check. Third, I verify against production, not just the code. Some of the most frustrating parts of the project involved fixes that were correct in the file but wrong on the user’s screen because of a cached asset, a stale database row, or an iOS process that needed a force-quit. I no longer consider anything fixed until I have checked the live app. Step-by-step: 1. I keep a project memory directory with a `MEMORY.md` index and separate topic files for architecture and business decisions, updating them at the end of every session. 2. I open a separate Claude Code session running Claude Fable and prompt it to audit the codebase and return only a prioritized checklist: file path, line number, the problem in one sentence, the fix in one sentence, with no code or commentary. 3. I keep the auditor in its own session with no project history so it reviews the code independently instead of defending work it wrote. 4. I paste the checklist verbatim into a Claude Opus session and have it work from the top down, reading each file and making each change. 5. I tell Opus to flag anything it believes is a false positive instead of implementing it, keeping a human in the loop on key findings. 6. I use Claude Sonnet for routine work such as content updates, image processing, and scheduled maintenance, often from handoffs written by Opus or Fable. 7. I verify every change against the live production site rather than the local files because cached assets and stale database rows can make correct code behave incorrectly for users. 8. I append confirmed lessons and architectural decisions to the project memory so the next session starts from the project’s current state.

Alex Reid
  • Claude Code
5

Built a complete, free iOS cognitive training app (AllegraMente) solo with Claude Code - 11 exercise areas, 5 languages, no tracking

I built AllegraMente, a complete, free iOS cognitive training app, solo with Claude Code. It includes 11 exercise areas, 83 articles in five languages, and no tracking. My workflow starts with a new feature or content idea, a new exercise type, or an in-depth article about how memory works. Planning and execution stay separate, and the instruction document serves as the contract. Because everything is specified upfront, Claude Code almost never goes off track. Step-by-step: 1. I plan the feature with Claude in chat, define the data models and UX, and fact-check every scientific claim against primary sources before writing anything. 2. I package the result into one self-contained Markdown instruction document containing file paths, specifications, localization keys for all five languages (IT/EN/ES/FR/DE), edge cases, and acceptance criteria. 3. I create a Git restore point. 4. I give the document to Claude Code, which implements the feature end to end using SwiftUI, SwiftData, and MVVM. 5. A small script sends me a Telegram notification when the task is complete. I review the diff and test the app on a device. 6. I ship by creating an archive with `xcodebuild` and uploading it to the App Store with Transporter. This process is how I shipped 11 exercise areas, 83 articles in five languages, and a complete editorial system as a solo developer.

Roberto D'Angelo
  • Claude Code
  • Claude
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