Skills Data Science Paperzilla: Research Insights and Data Access

Paperzilla: Research Insights and Data Access

v20260416
paperzilla
Paperzilla is a comprehensive skill for interacting with curated academic research data. Users can query the latest project recommendations, fetch canonical papers with detailed summaries (markdown), track project feeds, and provide feedback. It allows deep analysis and export of research insights, making it essential for researchers and students needing structured academic information.
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Overview

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

brew install paperzilla-ai/tap/pz

Windows (Scoop)

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:

pz update

If detection is ambiguous, override it explicitly:

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

pz login

CLI reference

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

List projects

pz project list

Show one project

pz project <project-id>

Browse project feed

pz feed <project-id>

Useful flags:

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

Examples:

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

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

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

Leave recommendation feedback

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

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

References

Info
Category Data Science
Name paperzilla
Version v20260416
Size 3.64KB
Updated At 2026-04-17
Language