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 agentmods add skills/synthetic-sciences/openscience/imaging-data-commonsnpx skills add synthetic-sciences/openscience --skill imaging-data-commonsgit 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/imaging-data-commons)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/imaging-data-commons"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/imaging-data-commons.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.10242 |
| Opus 5 | $0.00031 | $0.05121 |
| Sonnet 5 | $0.00012 | $0.02048 |
| Haiku 4.5 | $0.00006 | $0.01024 |
Grade A, and why
imaging-data-commons 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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- imaging-data-commons — 92% identical, 37 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Imaging Data Commons
Overview
Use the idc-index Python package to query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). No authentication required for data access.
Primary tool: idc-index (GitHub)
Check current data scale for the latest version:
from idc_index import IDCClient
client = IDCClient()
# get IDC data version
print(client.get_idc_version())
# Get collection count and total series
stats = client.sql_query("""
SELECT
COUNT(DISTINCT collection_id) as collections,
COUNT(DISTINCT analysis_result_id) as analysis_results,
COUNT(DISTINCT PatientID) as patients,
COUNT(DISTINCT StudyInstanceUID) as studies,
COUNT(DISTINCT SeriesInstanceUID) as series,
SUM(instanceCount) as instances,
SUM(series_size_MB)/1000000 as size_TB
FROM index
""")
print(stats)
Core workflow:
- Query metadata →
client.sql_query() - Download DICOM files →
client.download_from_selection() - Visualize in browser →
client.get_viewer_URL(seriesInstanceUID=...)
When to Use This Skill
- Finding publicly available radiology (CT, MR, PET) or pathology (slide microscopy) images
- Selecting image subsets by cancer type, modality, anatomical site, or other metadata
- Downloading DICOM data from IDC
- Checking data licenses before use in research or commercial applications
- Visualizing medical images in a browser without local DICOM viewer software
IDC Data Model
IDC adds two grouping levels above the standard DICOM hierarchy (Patient → Study → Series → Instance):
- collection_id: Groups patients by disease, modality, or research focus (e.g.,
tcga_luad,nlst). A patient belongs to exactly one collection. - analysis_result_id: Identifies derived objects (segmentations, annotations, radiomics features) across one or more original collections.
Use collection_id to find original imaging data, may include annotations deposited along with the images; use analysis_result_id to find AI-generated or expert annotations.
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.
- yesterday First seen · 1,184 lines · 62 tokens per session scan A 09c0563cd268
imaging-data-commons is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 10,242 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
meta-paper-write
Use this meta-skill instead of answering directly when the current user asks to draft or produce a new academic/research paper or LaTeX manuscript. It uses multi-skill orchestration for manuscript workflows that need source search, citation planning, experiment or figure/table placeholders, drafting, length checks…
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-section-author
Write one publication-style research-paper section as a bounded, citation-grounded LaTeX fragment from a writing plan, outline, citation plan, and optional figure/table context.
meta-arxiv-daily-digest-deck
Fetch the day's top arXiv submissions in a chosen category, write a structured per-paper digest, render the digest as a PPTX deck (one slide per paper), and persist the digest to long-term memory. Use for a daily 'arxiv morning briefing' — manual fire or cron-scheduled.
paper-quality-gate
Deterministic pre-compile gate for meta-paper-write. Enforces length/citation verdicts and rejects unsupported empirical-result claims when no user evidence was supplied.
paper-latex-sanitizer
Deterministically normalize safe LaTeX punctuation and replace unsupported forecast magnitudes with explicit placeholders before meta-paper-write publication gates run.