Agent skill

pyzotero

Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.

Stars 16,890
Forks 1,841

Install this agent skill to your Project

npx add-skill https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pyzotero

Metadata

Additional technical details for this skill

skill author
K-Dense Inc.

SKILL.md

Pyzotero

Pyzotero is a Python wrapper for the Zotero API v3. Use it to programmatically manage Zotero libraries: read items and collections, create and update references, upload attachments, manage tags, and export citations.

Authentication Setup

Required credentials — get from https://www.zotero.org/settings/keys:

Store credentials in environment variables or a .env file:

ZOTERO_LIBRARY_ID=your_user_id
ZOTERO_API_KEY=your_api_key
ZOTERO_LIBRARY_TYPE=user  # or "group"

See references/authentication.md for full setup details.

Installation

bash
uv add pyzotero
# or with CLI support:
uv add "pyzotero[cli]"

Quick Start

python
from pyzotero import Zotero

zot = Zotero(library_id='123456', library_type='user', api_key='ABC1234XYZ')

# Retrieve top-level items (returns 100 by default)
items = zot.top(limit=10)
for item in items:
    print(item['data']['title'], item['data']['itemType'])

# Search by keyword
results = zot.items(q='machine learning', limit=20)

# Retrieve all items (use everything() for complete results)
all_items = zot.everything(zot.items())

Core Concepts

  • A Zotero instance is bound to a single library (user or group). All methods operate on that library.
  • Item data lives in item['data']. Access fields like item['data']['title'], item['data']['creators'].
  • Pyzotero returns 100 items by default (API default is 25). Use zot.everything(zot.items()) to get all items.
  • Write methods return True on success or raise a ZoteroError.

Reference Files

File Contents
references/authentication.md Credentials, library types, local mode
references/read-api.md Retrieving items, collections, tags, groups
references/search-params.md Filtering, sorting, search parameters
references/write-api.md Creating, updating, deleting items
references/collections.md Collection CRUD operations
references/tags.md Tag retrieval and management
references/files-attachments.md File retrieval and attachment uploads
references/exports.md BibTeX, CSL-JSON, bibliography export
references/pagination.md follow(), everything(), generators
references/full-text.md Full-text content indexing and retrieval
references/saved-searches.md Saved search management
references/cli.md Command-line interface usage
references/error-handling.md Errors and exception handling

Common Patterns

Fetch and modify an item

python
item = zot.item('ITEMKEY')
item['data']['title'] = 'New Title'
zot.update_item(item)

Create an item from a template

python
template = zot.item_template('journalArticle')
template['title'] = 'My Paper'
template['creators'][0] = {'creatorType': 'author', 'firstName': 'Jane', 'lastName': 'Doe'}
zot.create_items([template])

Export as BibTeX

python
zot.add_parameters(format='bibtex')
bibtex = zot.top(limit=50)
# bibtex is a bibtexparser BibDatabase object
print(bibtex.entries)

Local mode (read-only, no API key needed)

python
zot = Zotero(library_id='123456', library_type='user', local=True)
items = zot.items()

Expand your agent's capabilities with these related and highly-rated skills.

K-Dense-AI/claude-scientific-skills

pufferlib

High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.

16,890 1,841
Explore
K-Dense-AI/claude-scientific-skills

fluidsim

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

16,890 1,841
Explore
K-Dense-AI/claude-scientific-skills

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

16,890 1,841
Explore
K-Dense-AI/claude-scientific-skills

astropy

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

16,890 1,841
Explore
K-Dense-AI/claude-scientific-skills

pyhealth

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

16,890 1,841
Explore
K-Dense-AI/claude-scientific-skills

research-lookup

Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.

16,890 1,841
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results