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Paperzilla

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dralkh
paperzilla

Chat with your agent about projects, recommendations, and canonical papers in Paperzilla. Use when users ask for recent project recommendations, canonical paper details, markdown-based summaries, recommendation feedback, feed export, or Atom feed URLs.

Overview

Publisherdralkh
Repositoryseerai
Skill namepaperzilla
Stars
96
Forks
5
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • 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 dralkh on GitHub. Read the source before you install it.

Installation

Install the Paperzilla 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/dralkh/seerai/tree/main/skills/paperzilla
  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/dralkh/seerai.git /tmp/seerai
mkdir -p .claude/skills
cp -r /tmp/seerai/skills/paperzilla .claude/skills/paperzilla
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paperzilla 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 Paperzilla 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 Paperzilla 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.

Paperzilla

Use this skill when you want to chat with your agent about projects, recommendations, and canonical papers in Paperzilla.

What you can ask

  • "Give me the latest recommendations from project X."
  • "Open recommendation Y and explain why it matters."
  • "Fetch canonical paper Z as markdown and summarize it."
  • "Tell me how this paper is relevant to my research."
  • "Show me the feed for project X."
  • "Leave feedback on a recommendation."
  • "Export this paper, recommendation, or feed as JSON."

This is the core Paperzilla skill. It gives your agent direct access to Paperzilla data, but it does not impose a workflow or external delivery integration.

Access method

Most current profiles in this repo use the pz CLI.

If the current profile ships extra agent-specific instructions, follow those as well.

Install

macOS

bash
brew install paperzilla-ai/tap/pz

Windows (Scoop)

bash
scoop bucket add paperzilla-ai https://github.com/paperzilla-ai/scoop-bucket
scoop install pz

Linux

Use the official Linux install guide:

Build from source (Go 1.23+)

See the CLI repository for source builds:

Update

Check whether your CLI is up to date and get install-specific upgrade steps:

bash
pz update

If detection is ambiguous, override it explicitly:

bash
pz update --install-method homebrew
pz update --install-method scoop
pz update --install-method release
pz update --install-method source

Supported values are auto, homebrew, scoop, release, and source.

Authentication

bash
pz login

CLI reference

If the current profile uses pz, these are the core commands.

List projects

bash
pz project list

Show one project

bash
pz project <project-id>

Browse project feed

bash
pz feed <project-id>

Useful flags:

  • --must-read
  • --since YYYY-MM-DD
  • --limit N
  • --json
  • --atom

Examples:

bash
pz feed <project-id> --must-read --since 2026-03-01 --limit 5
pz feed <project-id> --json
pz feed <project-id> --atom

Feed output can include existing recommendation feedback markers:

  • [↑] upvote
  • [↓] downvote
  • [★] star

Read a canonical paper

bash
pz paper <paper-id>
pz paper <paper-id> --json
pz paper <paper-id> --markdown
pz paper <paper-id> --project <project-id>

Open a recommendation from one of your projects

bash
pz rec <project-paper-id>
pz rec <project-paper-id> --json
pz rec <project-paper-id> --markdown

Leave recommendation feedback

bash
pz feedback <project-paper-id> upvote
pz feedback <project-paper-id> star
pz feedback <project-paper-id> downvote --reason not_relevant
pz feedback clear <project-paper-id>

Output and automation

  • Prefer --json for machine parsing.
  • pz paper --markdown only returns markdown when it is already prepared.
  • pz rec --markdown can queue markdown generation and prints a friendly retry message while it is still being prepared.
  • --atom returns a personal feed URL for feed readers.

Configuration

bash
export PZ_API_URL="https://paperzilla.ai"

References

Frequently asked questions

What does the Paperzilla AI skill do?

Chat with your agent about projects, recommendations, and canonical papers in Paperzilla. Use when users ask for recent project recommendations, canonical paper details, markdown-based summaries, recommendation feedback, feed export, or Atom feed URLs.

Why use Paperzilla on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dralkh/seerai/tree/main/skills/paperzilla. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Paperzilla?

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 Paperzilla?

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

Is the Paperzilla AI skill free?

Yes. It is published on GitHub by dralkh under the MIT license. 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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