Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Lord1Egypt/scientific-agent-toolkit --skill pyzoterogit clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/pyzotero)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/pyzotero"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/pyzotero/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/pyzotero"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/pyzotero.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.01074 |
| Opus 5 | $0.00043 | $0.00537 |
| Sonnet 5 | $0.00017 | $0.00215 |
| Haiku 4.5 | $0.00009 | $0.00107 |
Grade A, and why
pyzotero scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to pyzotero — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- User ID: shown as "Your userID for use in API calls"
- API Key: create at https://www.zotero.org/settings/keys/new
- Library ID: for group libraries, the integer after
/groups/in the group URL
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
uv add pyzotero
# or with CLI support:
uv add "pyzotero[cli]"
Quick Start
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
Zoteroinstance is bound to a single library (user or group). All methods operate on that library. - Item data lives in
item['data']. Access fields likeitem['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
Trueon success or raise aZoteroError.
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 |
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/authentication.md 2.2 KB
- references/cli.md 2.2 KB
- references/collections.md 2.5 KB
- references/error-handling.md 2.6 KB
- references/exports.md 2.5 KB
- references/files-attachments.md 2.7 KB
- references/full-text.md 1.7 KB
- references/pagination.md 2.0 KB
- references/read-api.md 2.9 KB
- references/saved-searches.md 2.0 KB
- references/search-params.md 3.0 KB
- references/tags.md 2.0 KB
- references/write-api.md 3.2 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 112 lines · 85 tokens per session scan A 4d332f14a946
pyzotero is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (3 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 1,074 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to pyzotero, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
decoupler
Use for any task involving the decoupler library — inferring biological activity/enrichment scores from omics data (bulk, single-cell, spatial). Triggers on estimating transcription factor (TF) activity, pathway activity, or gene-set enrichment from an AnnData/DataFrame; running ulm, mlm, ora, gsea, gsva, aucell…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use 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…
alterlab-cobrapy
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…
alterlab-phylogenetics
Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 2 maximum-likelihood inference with model selection and bootstraps, FastTree for large datasets, then visualize with ETE3 or FigTree. Use when reconstructing trees from sequences (FASTA) for…
alterlab-qiime2-amplicon
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 amplicon distribution (2026.1; renamed to "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier classify-sklearn against a…