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Nmrglue

OrganizationPopular
K-Dense-AI
nmrglue

Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions. Use for raw 1D NMR processing, ppm-axis verification, apodization, Fourier transformation, manual phasing, baseline correction, or reproducible spectral integration.

Overview

PublisherK-Dense-AI
Repositoryscientific-agent-skills
Skill namenmrglue
Stars
48.4K
Forks
4.4K
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by K-Dense-AI on GitHub. Read the source before you install it.

Installation

Install the Nmrglue AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/nmrglue
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p .claude/skills
cp -r /tmp/scientific-agent-skills/skills/nmrglue .claude/skills/nmrglue
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nmrglue in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nmrglue on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nmrglue is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

nmrglue: calibrated 1D FID processing

When to use

Use for a uniformly sampled complex 1D FID whose acquisition parameters and complex frequency convention are available. The helper produces a descending ppm spectrum, positive peak candidates, signed region integrals, and a reproducible processing report. It does not identify compounds or assign resonances.

The executable accepts a NumPy .npz containing exactly one complex fid array or a canonical 1D complex time-domain NMRPipe file. NMRPipe reading is tested with a synthetic write/read round trip, known-spectrum recovery, and a small upstream NMRPipe-generated binary fixture. Experimental Bruker, Varian, and JEOL imports are not verified by this suite. For those formats, first inspect the relevant nmrglue reader and acquisition metadata. Opening a converted file does not validate the original acquisition decoding. Read references/acquisition-and-validation.md for conversion boundaries, axis calibration, and quantitative limits.

Workflow

  1. Preserve the raw FID. Establish spectral width in Hz, positive observation frequency in MHz, carrier in ppm, observed nucleus, and the sign convention from the acquisition or a known reference. Determine whether digital-filter/group-delay removal has already occurred. Do not infer these from array length or typical instrument defaults.
  2. Copy assets/processing.json and replace its synthetic example values with the measured parameters and explicit processing choices. Its sign -i means a resonance at offset f = (ppm - carrier_ppm) * observation_mhz has time dependence exp(-2*pi*i*f*t). Select +i only for the opposite convention; the helper conjugates it before processing. Validate with a known reference peak.
  3. Choose nonnegative exponential line broadening (Hz), an even zero-filled size at least as large as the acquired FID, first-point scaling, and phase angles. Zero filling improves interpolation, not acquired spectral resolution. First-point scaling 0.5 is suitable for the supplied causal synthetic example; acquisition and prior preprocessing may require another value.
  4. Run the helper, inspect the real and imaginary spectra, and revise manual phase if needed. phase0_deg + phase1_deg * index / zero_fill_points is applied after FT; index zero is the high-ppm edge. There is no implicit pivot or automatic phase estimate.
  5. Only fit a linear baseline when explicitly supplied ppm regions are signal-free. Set baseline to linear and add baseline_regions_ppm containing at least two regions. Inspect residuals and broad peaks; fitting through signals biases integrals.
  6. Compare peak positions with references, inspect peak candidates for artifacts, and integrate specified regions. Report overlapped peaks as overlapped. Preserve negative areas as diagnostic evidence of phase/baseline problems instead of taking absolute values.

Execute

Tested with Python 3.12, nmrglue 0.12, NumPy 2.5.3, and SciPy 1.18.1:

bash
uv run --no-project --python 3.12 --with nmrglue==0.12 --with numpy==2.5.3 --with scipy==1.18.1 \
  python skills/nmrglue/scripts/process_1d.py fid.npz processing.json nmr-result

Paths assume the collection root. Adjust them when installed elsewhere. The output directory must be new, so repeated processing keeps previous results reviewable.

For an existing 1D NMRPipe FID, add --input-format nmrpipe and supply its path in place of fid.npz. The helper requires the canonical FDF2 direct dimension, complex quadrature, a time-domain flag, and agreement between header and JSON spectral width, observation frequency, and carrier. JSON settings remain explicit; a mismatch fails instead of silently recalibrating. FDF2TDSIZE must equal the stored complex-point count, and FDF2CENTER / FDF2ORIG must describe a canonical centered axis. Previously zero-filled, truncated, or recentered files need a separate acquisition-aware workflow. The nucleus/complex sign and previous digital-filter corrections still need acquisition evidence. A time-domain flag alone does not establish an unprocessed FID.

This executable synthetic example matches the supplied settings, generates resonances at 3 and 7 ppm in a 1:2 amplitude ratio, and does not represent an experimental sample:

python
import numpy as np

t = np.arange(8192) / 4000.0
fid = sum(a * np.exp(-np.pi * 2.0 * t)
          * np.exp(-2j * np.pi * (ppm - 5.0) * 400.0 * t)
          for ppm, a in [(3.0, 1.0), (7.0, 2.0)])
np.savez("fid.npz", fid=fid)

Run it with assets/processing.json as the settings argument. The repository suite executes this signal and the CLI, checks both peak locations within 0.001 ppm, checks integral ratio and analytic area, and checks phase and baseline recovery. The NMRPipe round-trip test writes this FID using ng.pipe.create_dic/ng.pipe.write, reads it through the CLI, and verifies the recovered peaks and integral ratio. Processed frequency-domain files and conflicting calibration metadata are rejected.

The 2,176-byte upstream fixture checks complex sample order and header calibration using a file generated by NMRPipe's simTimeND / SET tools. Those native tools were not run in this review; this is fixture compatibility, not a live NMRPipe processing comparison.

Deliverables and interpretation

  • spectrum.csv: descending ppm, real signal after baseline correction, phased imaginary signal, and the fitted real baseline. Plot NMR with the high-ppm end on the left.
  • report.json: input/settings SHA-256, package versions, all settings, acquired duration, zero-filled digital spacing, positive peak candidates, and signed region areas.

Integrals use endpoint interpolation and trapezoidal integration along increasing ppm; area units are arbitrary signal times ppm, independent of display direction. Regions outside the sampled ppm axis fail rather than being silently clipped. Peak prominence is a fraction of the largest positive real intensity; it is not a noise-derived detection limit. Strong solvent signals can obscure weak candidates at the default threshold.

For quantitative NMR, additionally establish relaxation delay, pulse angle, saturation, receiver behavior, internal/external reference amount, and integration uncertainty. The helper does not calculate concentrations or correct unequal relaxation. Preserve these limits with the result rather than converting arbitrary areas to molecule counts.

Upstream contracts

The hosted latest documentation identified itself as 0.9-dev when checked; the actual 0.12 package APIs and numerical behavior were tested. Its proc_base.fft uses the negative-exponent NumPy FFT followed by fftshift; NMRPipe's FT convention corresponds to fft_positive, so do not substitute it without revisiting the FID sign and phase. See the v0.12 processing source. Multidimensional processing, nonuniform sampling, automated assignment, and experimental vendor imports remain outside this helper's validated scope.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nmrglue AI skill do?

Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions. Use for raw 1D NMR processing, ppm-axis verification, apodization, Fourier transformation, manual phasing, baseline correction, or reproducible spectral integration.

Why use Nmrglue on TypingMind?

Because you install it once and use it with any model. Nmrglue is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nmrglue in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/nmrglue. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nmrglue?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nmrglue?

As many as you like. As long as a model supports skills, you can use Nmrglue with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nmrglue AI skill free?

Yes. It is published on GitHub by K-Dense-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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