A command-line interface for Paperzilla that enables seamless discovery and filtering of high-signal academic papers for researchers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pz
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install pz using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Paperzilla CLI (pz)?
The Paperzilla CLI (pz) is a specialized utility designed for researchers and developers who need to interact with the Paperzilla platform directly from their terminal. As a key addition to the collection of Openclaw Skills, this tool bridges the gap between high-level AI paper discovery and local development environments. It allows users to stay updated with their research feeds and manage academic projects without leaving their command-line workspace.
By integrating this skill, users gain the ability to list research projects, fetch curated feeds, and apply granular filters to identify essential scientific literature. It is an ideal solution for professionals looking to automate their literature review process or integrate academic data into broader AI-driven workflows.
Paperzilla CLI (pz) Use Cases
- Tracking specific academic research feeds directly from the command line.
- Filtering high-signal papers to identify must-read content for literature reviews.
- Exporting paper metadata to JSON for use in custom scripts or AI agents.
- Generating Atom/RSS feed URLs for integration with third-party feed readers like NetNewsWire or Feedly.
- Automating the retrieval of new papers published after a specific date within a research project.
How Paperzilla CLI (pz) Works
- The user authenticates their local environment with their Paperzilla account using the login command.
- The CLI communicates with the Paperzilla API to fetch project metadata and user-specific research feeds.
- Users execute commands to list projects or browse specific feeds using unique project IDs.
- Optional flags are applied to filter results based on importance, publication date, or result limits.
- The tool outputs data in human-readable text, JSON for machine parsing, or Atom XML for external feed readers.
Paperzilla CLI (pz) Setup
To get started with this entry in the Openclaw Skills library, install the CLI for your operating system:
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:
curl -sL https://github.com/paperzilla-ai/pz/releases/latest/download/pz_linux_amd64.tar.gz | tar xz
sudo mv pz /usr/local/bin/
After installation, authenticate your account:
pz login
Paperzilla CLI (pz) Data Schema & Taxonomy
The Paperzilla CLI organizes data around projects and paper metadata. When using the --json flag, the output typically follows a structured format that includes:
| Attribute | Description |
|---|---|
| Project ID | Unique identifier for a curated research feed. |
| Title | The name of the academic paper. |
| Status | Tags such as must-read or unread for organization. |
| Date | The publication or index date (YYYY-MM-DD). |
| Metadata | Author information and paper abstracts where available. |
Configurations are stored locally, and the API endpoint can be customized via the PZ_API_URL environment variable.
Paperzilla CLI (pz) Advanced Features
- JSON Output: Pipe structured academic data into other Openclaw Skills or LLMs for automated summarization.
- Atom Feed Generation: Create secure, tokenized URLs for integration with any standard RSS reader like Vienna RSS.
- Granular Date Filtering: Use the
--sinceflag to programmatically fetch papers published after a specific milestone. - Custom API Endpoints: Support for custom Paperzilla instances through environment variable overrides.
- Efficiency Flags: Limit result counts and isolate high-priority papers using the
--must-readand--limitflags.
SKILL.md
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