### Paperzilla
Works with
name: "paperzilla"
description: "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, reco..."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpaperzillaExecute the skills CLI command in your project's root directory to begin installation:
Fetches paperzilla from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate paperzilla. Access via /paperzilla in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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| name | paperzilla |
| description | 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. |
| license | MIT |
| metadata | version: "1.0" skill-author: "Paperzilla Inc" |
Use this skill when you want to chat with your agent about projects, recommendations, and canonical papers in Paperzilla.
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.
Most current profiles in this repo use the pz CLI.
If the current profile ships extra agent-specific instructions, follow those as well.
brew install paperzilla-ai/tap/pz
scoop bucket add paperzilla-ai https://github.com/paperzilla-ai/scoop-bucket
scoop install pz
Use the official Linux install guide:
See the CLI repository for source builds:
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.
pz login
If the current profile uses pz, these are the core commands.
pz project list
pz project <project-id>
pz feed <project-id>
Useful flags:
--must-read--since YYYY-MM-DD--limit N--json--atomExamples:
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[★] starpz paper <paper-id>
pz paper <paper-id> --json
pz paper <paper-id> --markdown
pz paper <paper-id> --project <project-id>
pz rec <project-paper-id>
pz rec <project-paper-id> --json
pz rec <project-paper-id> --markdown
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>
--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.export PZ_API_URL="https://paperzilla.ai"
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
google-deepmind/science-skills
google-deepmind/science-skills
paperzilla fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: paperzilla is the kind of skill you can hand to a new teammate without a long onboarding doc.
paperzilla is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added paperzilla from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in paperzilla — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for paperzilla matched our evaluation — installs cleanly and behaves as described in the markdown.
paperzilla is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend paperzilla for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: paperzilla is the kind of skill you can hand to a new teammate without a long onboarding doc.
paperzilla reduced setup friction for our internal harness; good balance of opinion and flexibility.
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