digital-wellbeing

name

digital-wellbeing

description

Digital-wellbeing lens on Apple Screen Time — how much you live on screens and your phone, so you don't end up chronically online. Use for "screen time", "phone usage", "am I on my phone too much", "digital detox", "screen balance", "too much time online", "phone addiction", "device time", "presence", "touch grass". Lifestyle-area analysis of volume and balance; for the QUALITY of what you consume use the mind `info-diet` skill instead.

Digital wellbeing (lifestyle lens)

Measures how much of your life happens on a screen — total device time, phone dominance, and whether you're living chronically online — using unified Apple Screen Time (Mac + iPhone). The question is volume and balance, NOT content quality (that's the mind info-diet skill). Same underlying data, different question.

Get the data

Run the bundled extractor (self-contained — Python stdlib + vendored ccl_segb; needs Full Disk Access), or read the latest vault snapshot if fresh:

python3 "${CLAUDE_PLUGIN_ROOT}/skills/digital-wellbeing/screentime_extract.py" --days 7 \
        --snapshot-dir "<vault>/400 Resources/screentime"

• Source: ~/Library/Biome/streams/restricted/App.InFocus/ — local/ = this Mac, remote/<uuid>/ = each iCloud-synced device (Screen Time → Share Across Devices ON).
• (idle/system) = lock/StandBy/Universal Control — count it as "device on, not used", and keep it separate from real usage. Only source of true iPhone time.

The lens: volume & balance

• Total device time/day (Mac + iPhone summed) and the trend — is it climbing?
• Phone dominance: iPhone hours ÷ total. A high phone share is the chronic-online signal, since the phone follows you everywhere.
• Biggest time-sinks by raw hours (the apps eating your day), phone first.
• Idle/StandBy share: high (idle/system) on iPhone often = phone constantly in hand/on the dock — a presence red flag even when not "using" an app.
• Compare online vs. offline: waking hours minus device time ≈ life off the screen.

Report

• Lead with a blunt number: "You spent ~Xh/day on screens, Yh of it on your phone."
• Name the top phone sink and quantify it. Flag if total or phone share is trending up.
• Recommend for lifestyle/presence, not the brain: phone-free windows (meals, first hour, bedroom), leaving the phone in another room, a daily cap. Ground in 300 Areas/4 Lifestyle protocols. Voice guide applies (concrete, take a position, end with a rec).
• If the user asks about what they consume (feeds vs reading), hand off to the mind info-diet skill.

Provenance

ccl_segb/ is CCL Forensics' MIT-licensed SEGB parser (github.com/cclgroupltd/ccl-segb). reference_schema.proto documents the App.InFocus protobuf. The extractor hand-rolls a 3-field protobuf decoder — no runtime dependencies beyond the Python stdlib.