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.
Best use case
paperzilla is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using paperzilla should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/paperzilla/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How paperzilla Compares
| Feature / Agent | paperzilla | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this 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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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: - https://docs.paperzilla.ai/guides/cli-getting-started ### Build from source (Go 1.23+) See the CLI repository for source builds: - https://github.com/paperzilla-ai/pz ## 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 - Docs: https://docs.paperzilla.ai/guides/cli - Quickstart: https://docs.paperzilla.ai/guides/cli-getting-started - Repo: https://github.com/paperzilla-ai/pz
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