synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill openalex-databasegit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/openalex-database)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/openalex-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/openalex-database/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/synthetic-sciences/openscience/openalex-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/openalex-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00076 | $0.03052 |
| Opus 5.5 | $0.00030 | $0.01221 |
| Sonnet 5.5 | $0.00015 | $0.00610 |
| Haiku 4.5 | $0.00008 | $0.00305 |
Grade A, and why
openalex-database scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
citing_response = requests.get( Copies of this mod
7 near-identical copies found in the catalogue:
- openalex-database — 95% identical, 11 lines differ
- openalex-database — 95% identical, 7 lines differ
- openalex-database — 95% identical, 8 lines differ
- openalex-database — 86% identical, 10 lines differ
- openalex-database — 86% identical, 10 lines differ
- openalex-database — 86% identical, 10 lines differ
- openalex-database — 86% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenAlex Database
Overview
OpenAlex is a comprehensive open catalog of 240M+ scholarly works, authors, institutions, topics, sources, publishers, and funders. This skill provides tools and workflows for querying the OpenAlex API to search literature, analyze research output, track citations, and conduct bibliometric studies.
Quick Start
Basic Setup
Always initialize the client with an email address to access the polite pool (10x rate limit boost):
from scripts.openalex_client import OpenAlexClient
client = OpenAlexClient(email="[email protected]")
Installation Requirements
Install required package using uv:
uv pip install requests
No API key required - OpenAlex is completely open.
Core Capabilities
1. Search for Papers
Use for: Finding papers by title, abstract, or topic
# Simple search
results = client.search_works(
search="machine learning",
per_page=100
)
# Search with filters
results = client.search_works(
search="CRISPR gene editing",
filter_params={
"publication_year": ">2020",
"is_oa": "true"
},
sort="cited_by_count:desc"
)
2. Find Works by Author
Use for: Getting all publications by a specific researcher
Use the two-step pattern (entity name → ID → works):
from scripts.query_helpers import find_author_works
works = find_author_works(
author_name="Jennifer Doudna",
client=client,
limit=100
)
Manual two-step approach:
# Step 1: Get author ID
author_response = client._make_request(
'/authors',
params={'search': 'Jennifer Doudna', 'per-page': 1}
)
author_id = author_response['results'][0]['id'].split('/')[-1]
# Step 2: Get works
works = client.search_works(
filter_params={"authorships.author.id": author_id}
)
3. Find Works from Institution
Use for: Analyzing research output from universities or organizations
from scripts.query_helpers import find_institution_works
works = find_institution_works(
institution_name="Stanford University",
client=client,
limit=200
)
What ships with it
4 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.
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.
- 1mo ago Changed · +6 lines 9e0b27ad7db6
- 1mo ago First seen · 494 lines · 76 tokens per session scan A 68173c87e3ca
openalex-database is a skill published in the GitHub repository synthetic-sciences/openscience (4,121 stars, last pushed 2d ago), licensed Apache-2.0. It adds 76 tokens to every session and 3,052 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
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