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 nmr-compound-inferencegit 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/nmr-compound-inference)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/nmr-compound-inference"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/nmr-compound-inference/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/nmr-compound-inference"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/nmr-compound-inference.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 | $0.00080 | $0.00907 |
| Opus 5.5 | $0.00032 | $0.00363 |
| Sonnet 5.5 | $0.00016 | $0.00181 |
| Haiku 4.5 | $0.00008 | $0.00091 |
Grade A, and why
nmr-compound-inference scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 17d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NMR Compound Inference
Use this skill to analyze a local 1D NMR dataset, generate a figure and peak list, search standard reference libraries, and infer the most likely compound or a short ranked candidate list.
Best fit
1Hand13C1D spectra- Bruker processed
pdata, Bruker rawfid, NMRPipe 1D files, or two-columnppm,intensitytext exports - Samples with one dominant component
Required inputs
- local spectrum path
- preferably nucleus, solvent, and field strength
- any sample context that narrows candidate chemistry
Dependencies
Install the dependencies into the active Python environment:
python3 -m pip install nmrglue numpy matplotlib
This skill depends on the upstream nmrglue package and does not bundle the nmrglue source code.
Workflow
- Run the helper script to generate the evidence package:
python3 scripts/nmr_candidate_report.py \
--input "<path-to-spectrum>" \
--output-dir "./outputs/$(date +%F)" \
--nucleus "1H"
Optional metadata for better matching:
python3 scripts/nmr_candidate_report.py \
--input "<path-to-spectrum>" \
--output-dir "./outputs/$(date +%F)" \
--nucleus "1H" \
--solvent "D2O" \
--field-mhz 600
-
Review the generated outputs:
spectrum.png— Plot of the processed spectrumpeak_table.csv— Extracted peak positions and intensitiesanalysis_summary.json— Structured summary for downstream processinganalysis_report.md— Human-readable report with search recommendations
-
Read
references/standard-libraries.mdand search the relevant library sites:- BMRB Metabolomics: https://bmrb.io/metabolomics/
- HMDB 1D NMR: https://hmdb.ca/spectra/nmr_one_d
- SDBS: https://sdbs.db.aist.go.jp/SearchResult.aspx
- nmrshiftdb2: https://nmrshiftdb.nmr.uni-koeln.de/nmrshiftdbhtml/t1.html
-
Return a report with:
- problem type
- what data was analyzed
- plot path
- major peaks
- library links searched
- top 1-3 candidate compounds
- confidence, mismatch notes, and what would disambiguate the call
What ships with it
2 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.
- 17d ago First seen · 104 lines · 80 tokens per session scan A e141bfa42857
nmr-compound-inference is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 80 tokens to every session and 907 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-30.
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