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/learningmatter-mit/atomisticskills/chem-nmr-analysisnpx skills add learningmatter-mit/AtomisticSkills --skill chem-nmr-analysisgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/chem-nmr-analysis)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-nmr-analysis"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-nmr-analysis.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 | $0.00036 | $0.02248 |
| Opus 5 | $0.00018 | $0.01124 |
| Sonnet 5 | $0.00007 | $0.00450 |
| Haiku 4.5 | $0.00004 | $0.00225 |
Grade A, and why
chem-nmr-analysis 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 5d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NMR Mixture Analysis
When to Use This Skill
The agent should use this skill's scripts when:
- A workflow (e.g.,
reaction-to-nmr-quantification.mdornmr-reaction-kinetics.md) calls for deconvolution, product prediction, kinetics analysis, or spectral plotting. - The user already has reference spectra and a mixture spectrum and wants to quantify component proportions directly.
- The user has multiple time-point spectra and wants to track reaction progress via NMR.
For end-to-end workflows that chain this skill with other skills, see: .agents/workflows/reaction-to-nmr-quantification.md and .agents/workflows/nmr-reaction-kinetics.md.
When NOT to Use This Skill
- 13C NMR, 2D NMR (COSY, HSQC, etc.), or solid-state NMR -- this skill handles 1H solution-state NMR only.
- Structure elucidation of unknown compounds -- this skill requires knowing (or predicting) what compounds are in the mixture. It does not identify unknowns from scratch.
- Pure compound characterization -- if the user has a single pure compound and just wants to assign peaks, this skill is not appropriate. The agent should interpret the spectrum directly.
- Mass spectrometry data -- despite the Wasserstein algorithm's origins in mass spec, this skill operates on NMR chemical shift axes only.
- Digitizing spectrum images -- the agent should use the
general-plot-digitizerskill for that step. - Predicting NMR spectra from SMILES -- the agent should use the
chem-nmr-predictskill for that step. - Resolving compound names to SMILES -- the agent should use the
drug-db-pubchemskill for that step.
Scripts Reference
| Script | Purpose | Key Inputs | Key Outputs |
|---|---|---|---|
predict_products.py |
Predict reaction products via ReactionT5 (HuggingFace API) | --reactant_smiles, --reagent_smiles |
JSON with predicted product SMILES |
deconvolve.py |
Wasserstein deconvolution of mixture against references | mixture file + reference files + --protons |
proportions, Wasserstein distance, plot |
kinetics.py |
Time-series deconvolution across multiple time points | --refs, --timepoints, --times |
kinetics.csv + kinetics_plot.png |
plot.py |
Overlay or stack NMR spectra for visual comparison | spectrum files + --labels |
plot image |
spectra.py |
I/O utilities (imported by other scripts, not called directly) | -- | -- |
What ships with it
17 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.
- examples/deconvolution/borneol.csv 21 KB
- examples/deconvolution/crude.csv 9.2 KB
- examples/deconvolution/isoborneol.csv 23 KB
- examples/kinetics/t000min.csv 22 KB
- examples/kinetics/t005min.csv 22 KB
- examples/kinetics/t010min.csv 22 KB
- examples/kinetics/t020min.csv 22 KB
- examples/kinetics/t030min.csv 22 KB
- examples/kinetics/t045min.csv 22 KB
- examples/kinetics/t060min.csv 22 KB
- examples/kinetics/t090min.csv 22 KB
- reference/named_reactions.json 9.6 KB
- scripts/deconvolve.py 12 KB runs code
- scripts/kinetics.py 6.9 KB runs code
- scripts/plot.py 8.5 KB runs code
- scripts/predict_products.py 4.7 KB runs code
- scripts/spectra.py 3.2 KB runs code
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.
- 5d ago First seen · 182 lines · 36 tokens per session scan A b0f14dfd464f
chem-nmr-analysis is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 2,248 once invoked, about $0.0002 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 2026-08-30.
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