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// Skill profile

reMarkable Tablet Integration (rmapi)

name: remarkable

by coolmanns · published 2026-03-22

图像生成API集成加密货币
Total installs
0
Stars
★ 0
Last updated
2026-03
// Install command
$ claw add gh:coolmanns/coolmanns-remarkable-tablet
View on GitHub
// Full documentation

---

name: remarkable

description: Fetch handwritten notes, sketches, and drawings from a reMarkable tablet via Cloud API (rmapi). Process content by refining artwork with AI image generation, extracting handwritten text to memory/journal, or using sketches as input for other workflows. Use when working with reMarkable tablet content, syncing handwritten notes, processing sketches, or integrating tablet drawings into projects.

---

# reMarkable Tablet Integration (rmapi)

Fetch handwritten notes, sketches, and drawings from a reMarkable tablet via Cloud API. Process them — refine artwork with AI image generation, extract text to memory/journal, or use as input for other workflows.

Typical Use Cases

1. **Journal entries** — User writes thoughts on reMarkable → fetch → OCR/interpret → append to `memory/YYYY-MM-DD.md` or a dedicated journal file

2. **Sketch refinement** — User draws a rough graphic → fetch → enhance with nano-banana-pro (AI image editing) → return polished version

3. **Brainstorming/notes** — User jots down ideas, lists, diagrams → fetch → extract structure → add to project docs or memory

4. **Illustrations** — User creates hand-drawn art → fetch → optionally stylize → use in blog posts, social media, etc.

Processing Pipeline

reMarkable tablet → Cloud sync → rmapi fetch → PDF/PNG
                                                  ↓
                                    ┌─────────────┴─────────────┐
                                    │                           │
                              Text content?               Visual/sketch?
                                    │                           │
                              OCR / interpret            nano-banana-pro
                                    │                     (AI enhance)
                                    │                           │
                              Add to memory/            Return refined
                              journal/project            image to user

Setup

  • **Tool:** rmapi (ddvk fork) v0.0.32
  • **Binary:** `~/bin/rmapi`
  • **Config:** `~/.rmapi` (device token after auth)
  • **Sync folder:** `~/clawd/remarkable-sync/`
  • Authentication (ONE-TIME)

    1. User goes to https://my.remarkable.com/connect/desktop

    2. Logs in, gets 8-character code

    3. Run `rmapi` and enter the code

    4. Token saved to `~/.rmapi` — future runs are automatic

    Commands

    # List files/folders
    rmapi ls
    rmapi ls --json
    
    # Navigate
    rmapi cd "folder name"
    
    # Find by tag / starred / regex
    rmapi find --tag="share-with-gandalf" /
    rmapi find --starred /
    rmapi find / ".*sketch.*"
    
    # Download (PDF)
    rmapi get "filename"
    
    # Download with annotations rendered (best for sketches)
    rmapi geta "filename"
    
    # Bulk download folder
    rmapi mget -o ~/clawd/remarkable-sync/ "/Shared with Gandalf"

    Sharing Workflows

    Option A: Dedicated Folder

    User creates "Shared with Gandalf" folder on reMarkable → moves items there → agent fetches with `rmapi mget`

    Option B: Tag-Based

    User tags documents with `share-with-gandalf` → agent discovers with `rmapi find --tag`

    Option C: Starred Items

    User stars items → agent fetches with `rmapi find --starred`

    Fetch Script

    # Fetch from shared folder
    ~/clawd/scripts/remarkable-fetch.sh
    
    # Fetch starred items
    ~/clawd/scripts/remarkable-fetch.sh --starred
    
    # Fetch by tag
    ~/clawd/scripts/remarkable-fetch.sh --tag="share-with-gandalf"

    Notes

  • Tablet must cloud-sync before files are available
  • `geta` renders annotations into PDF (preferred for handwritten content)
  • Use `convert` (ImageMagick) to go from PDF → PNG for image processing
  • For text extraction, interpret the handwriting visually (vision model) rather than traditional OCR
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