Feed Catchup logo

Feed Catchup

Organization
readwiseio
feed-catchup

Catch up on your RSS feed — highlights up top, full browse below

Overview

Publisherreadwiseio
Repositoryreadwise-skills
Skill namefeed-catchup
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 Feed Catchup 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/feed-catchup
  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/feed-catchup .claude/skills/feed-catchup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feed Catchup 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 Feed Catchup 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 Feed Catchup 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 helping the user catch up on their Readwise Reader RSS feed. Follow this process carefully.

Readwise Access

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

Setup

IMPORTANT — do this in a single parallel turn before anything else: Call ToolSearch with query "readwise list documents" AND read reader_persona.md at the same time. Both must happen in the same message as parallel tool calls. The ToolSearch loads the deferred readwise MCP tools so you can call them directly. Never use a Task/subagent to fetch feed data — the overhead makes startup brutally slow.

  1. Check for persona file. (Done in parallel above.) Use it throughout the session to personalize commentary and picks. If no persona file exists, note briefly that feed catchup will be less personalized and suggest running build-persona first — but proceed without waiting. If you show this message, add · · · after it.

  2. Fetch feed documents. Call mcp__readwise__reader_list_documents with location="feed", limit=20, and response_fields=["title", "author", "category", "word_count", "reading_time", "summary", "url", "site_name", "published_date", "saved_at", "first_opened_at"]. Documents come back most-recently-saved first. Filter to items where first_opened_at is null (unseen). If you have fewer than 20 unseen items and a nextPageCursor is returned, paginate until you have 20 unseen items OR the cursor runs out. Hold all unseen items in memory. (Note: the list API does not support server-side seen filtering — client-side first_opened_at check is required.)

  3. If truly nothing left: Only declare the feed fully caught up if you paginated through multiple pages and found zero unseen items. In that case, say so briefly and end.

  4. Pick the top 5. From the collected unseen items, select the 5 most worth reading based on the persona (if available) or general signal quality. Prioritize: high-density insight, direct relevance to their current interests, first-person operator takes, and novelty.

Opening Format

Render the overview exactly like this:

📡 Reader Feed

{1-2 sentences explaining what you looked at and what stood out — e.g. "Scanned the last 20 unseen items. AI and software architecture dominate, with a few standouts worth pulling."}

Today's picks (spanning {human-readable time range, e.g. "the last 8 hours" or "Feb 24–26"}):

#TitleSourceTimeWhy
1Titlesite_namereading_timeOne-line reason this made the cut
2............

{1-2 sentences of commentary on the picks as a set — what the pattern is, or why these five in particular.}

· · ·

Want to act on any of these, or browse everything?

  • Later N / Inbox N / Shortlist N / Archive N — move a pick
  • Show N — get a deeper summary
  • Read N — open in Reader
  • Browse all — go through all unseen items in batches of 20

Browse Loop

If the user says "browse all" (or similar), enter the batch-by-batch loop. Present unseen items 20 at a time:

The Table

Before the table, add a single line with the time range covered by the batch, e.g. "Feb 26, 3:00–11:00 PM" or "last 4 hours" — derived from the saved_at values of the items in that batch.

#TitleSourceTimeSummary
1Titlesite_namereading_timeBrief summary from metadata — one line, truncated if needed
2............

After the table, give a brief commentary (1-2 sentences) on the batch — what stands out relative to their interests.

Options

  • Mark all seen — mark the batch as seen and load the next 10
  • Later N — move to Later (you can also move to Inbox/Shortlist/Archive)
  • Show N — get a deeper summary (or the full content if short)
  • Read N — open in Reader

(You can act on multiple items at once, e.g. "later 2, 5, 8")

Handling Responses

  • "Mark all seen" / "next" / "seen" — Call mcp__readwise__reader_bulk_edit_document_metadata with documents set to [{document_id: <id>, seen: true}, ...] for every document in the current batch. This is a single call, not one per document. Do not move or archive them. Then display the next batch of 20.
  • "Later N" — Move that document to later location. Confirm briefly, then continue.
  • "Later N, N, N" — Move multiple documents to later. Confirm briefly.
  • "Inbox N" / "Shortlist N" / "Archive N" — Move to the specified location (new, shortlist, or archive). Confirm briefly.
  • "Show N" — Fetch full content using mcp__readwise__reader_get_document_details. If the document is 3 mins or under, show the full content verbatim — no summary. If over 3 mins, give a richer summary with why-read/why-skip reasoning. Then re-present the options.
  • "Read N" — Provide the Reader link (https://read.readwise.io/read/{id}) so they can open it directly.
  • "Stop" / "done" — End the session with a brief summary of what was processed (how many seen, how many pulled).

Transitions

When loading the next batch, use · · · as a visual separator before the next table.

Frequently asked questions

What does the Feed Catchup AI skill do?

Catch up on your RSS feed — highlights up top, full browse below

Why use Feed Catchup on TypingMind?

Because you install it once and use it with any model. Feed Catchup 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 Feed Catchup in TypingMind?

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

Which AI models can use Feed Catchup?

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 Feed Catchup?

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

Is the Feed Catchup 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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