# paperzilla

> paperzilla — pors-paperzilla. Use this tool when you need to efficiently search and discover relevant academic papers, as it solves the problem of information overload by filtering and browsing high-signal research papers, taking in keywords and topics as input and outputting curated lists of papers, ideal for researchers and students seeking reliable sources.

Canonical page: https://skillsregistry.net/skills/pors-paperzilla  
JSON: https://api.skillsregistry.net/v1/skills/pors-paperzilla

## Description

Use the Paperzilla CLI (pz) to search, filter, and browse high-signal academic papers.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-05-22

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** search
- **Updated:** 2026-09-13

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/pors-paperzilla)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "pors-paperzilla"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/pors-paperzilla` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/pors-paperzilla/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt