Now Reading Page logo

Now Reading Page

Organization
readwiseio
now-reading-page

Generate a personal "Now Reading" webpage from your Reader library

Overview

Publisherreadwiseio
Repositoryreadwise-skills
Skill namenow-reading-page
Stars
324
Forks
13
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by readwiseio on GitHub. Read the source before you install it.

Installation

Install the Now Reading Page AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/readwiseio/readwise-skills/tree/master/skills/now-reading-page
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/readwiseio/readwise-skills.git /tmp/readwise-skills
mkdir -p .claude/skills
cp -r /tmp/readwise-skills/skills/now-reading-page .claude/skills/now-reading-page
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Now Reading Page in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Now Reading Page on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Now Reading Page is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

You are generating a beautiful standalone HTML page showing what the user is currently reading and has recently read. The output is a single HTML file they can open in a browser or host on their personal site.

Readwise Access

Check if Readwise MCP tools are available (e.g. mcp__readwise__reader_list_documents). If they are, use them throughout (and pass this context to the subagent). If not, use the equivalent readwise CLI commands instead (e.g. readwise list, readwise read <id>). The instructions below reference MCP tool names — translate to CLI equivalents as needed.

Process

Launch a Task subagent to fetch all the data and generate the HTML file. The subagent should:

1. Fetch Data

Run ALL of these in parallel:

  • Shortlist: mcp__readwise__reader_list_documents with location="shortlist", limit=50, response_fields=["title", "author", "category", "reading_progress", "first_opened_at", "last_opened_at", "image", "url", "site_name", "word_count", "saved_at"]
  • Later: mcp__readwise__reader_list_documents with location="later", limit=50, response_fields=["title", "author", "category", "reading_progress", "first_opened_at", "last_opened_at", "image", "url", "site_name", "word_count", "saved_at"]
  • Inbox: mcp__readwise__reader_list_documents with location="new", limit=50, response_fields=["title", "author", "category", "reading_progress", "first_opened_at", "last_opened_at", "image", "url", "site_name", "word_count", "saved_at"]
  • Archive page 1: mcp__readwise__reader_list_documents with location="archive", limit=50, response_fields=["title", "author", "category", "reading_progress", "last_opened_at", "image", "url", "site_name", "saved_at", "word_count"]

2. Paginate Archive Deeply

After the first archive fetch, use nextPageCursor to keep fetching more pages (limit=50 each). Fetch at least 6 more pages (~350 total docs) so the heatmap covers 6 months of reading activity. Keep paginating until the oldest last_opened_at is 6+ months ago OR pages are exhausted.

3. Categorize

From the fetched data, build two lists:

  • Currently Reading: Items from shortlist, later, or inbox where reading_progress is between 0.05 and 0.99 (started but not finished). Sort by last_opened_at descending.
  • Recently Read: Items from archive where reading_progress > 0.9 (actually finished). Sort by last_opened_at descending. Group by month. Show as many months as the data covers.

Also collect ALL last_opened_at dates from archive items with reading_progress > 0.9 for the heatmap.

There is no "Up Next" section. Only show things the user is reading or has read.

4. Generate HTML

Create a now-reading/ directory in the current working directory (if it doesn't exist) and write the HTML file to now-reading/index.html.

Design direction: Warm, sepia-toned, editorial. Think personal reading log, not media dashboard.

Fonts: Google Fonts — Newsreader (serif, for headings) + DM Sans (sans, for body). Include via <link> tag.

Color palette (CSS variables):

--bg: #f6f1eb          (warm parchment background)
--surface: #ede6dc     (card/heatmap empty cell background)
--surface-hover: #e4dbd0
--border: #d9d0c4
--text: #4a4239        (main body text)
--text-muted: #8a7e72
--text-dim: #b0a597
--heading: #2c251e
--accent: #a0724a      (warm brown — progress bars, active states)
--accent-dim: rgba(160, 114, 74, 0.12)

Layout: Max-width 760px, centered. Responsive.

Sections in order:

  1. Header: "What I'm reading" in Newsreader, light weight, large. Subtitle: "Powered by Readwise Reader" with accent-colored link.

  2. Currently Reading — section label in small caps. Gallery of cards using CSS grid (repeat(auto-fill, minmax(160px, 1fr))) so they fill the container. Each card:

    • aspect-ratio: 3/2, rounded corners, hover lift effect
    • Cover image if available (image field). Gradient placeholder if not (hash title → hue).
    • Dark gradient overlay at bottom with white title + author
    • Progress bar at card bottom: 5px tall track (dark semi-transparent) with accent-colored fill
    • Cards link to the Reader URL
  3. Reading Activity — GitHub-style heatmap filling full container width. Use CSS flex with flex: 1 on weeks and cells so it stretches. Warm amber color scale (rgba(160, 114, 74, 0.2/0.4/0.65/1.0)). Month labels above, day-of-week labels (Mon/Wed/Fri) on left. Show 6 months.

  4. Recently Read — Category filter pills (All, Articles, Books, Tweets, RSS, Email) with JS toggle. Then entries grouped by month (e.g., "FEBRUARY 2026" in small caps). Each entry as a row:

    • Date on left (tabular nums, muted)
    • Category emoji (📄 article, 📚 book, 🐦 tweet, 📰 rss, ✉️ email, 🎬 video, 🎙 podcast, 📑 pdf)
    • Title (linked, medium weight) + author/source (dim, smaller)

Styling notes:

  • All CSS in a <style> tag with CSS variables
  • Subtle page-load fade-up animation on sections
  • Divider lines between sections (1px solid var(--border))
  • Responsive: 2-column card grid on mobile

5. Return

Return the absolute path to the generated HTML file and a summary: how many currently reading, how many recently read, date range of activity data.

After Subagent Returns

  1. Tell the user the file was generated and where it is
  2. Offer to open it in their browser: open now-reading/index.html
  3. Ask if they want to adjust anything

Frequently asked questions

What does the Now Reading Page AI skill do?

Generate a personal "Now Reading" webpage from your Reader library

Why use Now Reading Page on TypingMind?

Because you install it once and use it with any model. Now Reading Page is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Now Reading Page in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/readwiseio/readwise-skills/tree/master/skills/now-reading-page. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Now Reading Page?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Now Reading Page?

As many as you like. As long as a model supports skills, you can use Now Reading Page with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Now Reading Page AI skill free?

It is published on GitHub by readwiseio. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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