Jeden Skill in Manus ausführen
mit einem Klick

Jeden Skill in Manus mit einem Klick ausführen

apt-analyze

Sterne7

Forks5

Aktualisiert17. März 2026 um 18:24

Run a full atom probe analysis pipeline on a data file. Loads data, creates mass spectrum, and guides through ion identification, ranging, and composition calculation. Use for end-to-end APT analysis.

Installation

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

In Manus ausführen

Quelle

peterfelfer

peterfelfer/Atom-Probe-Toolbox

GitHub-Repository öffnen Creator-Repositorys ansehen

Download

In Manus ausführen

Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

SoftwareentwicklerInformatik- und Mathematikberufe·SOC 15-1252

SKILL.md

readonly

name	apt-analyze
description	Run a full atom probe analysis pipeline on a data file. Loads data, creates mass spectrum, and guides through ion identification, ranging, and composition calculation. Use for end-to-end APT analysis.
argument-hint	<file.pos\|file.epos>

Run a complete atom probe analysis pipeline. Use the MATLAB MCP server.

The file to analyze is: $ARGUMENTS

If no file is provided, ask the user for the file path.

Full Pipeline

Step 1: Setup (if not already done)

setupToolbox;
load('isotopeTable_naturalAbundances.mat');
load('colorScheme_default.mat');
cfg = APTConfig.getInstance();

Step 2: Ask about the instrument

Before proceeding, ask the user which instrument acquired this data:

LEAP 4000X HR (detEff = 0.37)
LEAP 5000 XR (detEff = 0.52)
LEAP 5000 XS (detEff = 0.80)
EIKOS (detEff = 0.50)

Apply the preset:

cfg.applyInstrumentPreset('LEAP5000XR');  % adjust based on answer

Step 3: Load data

pos = posLoad('$ARGUMENTS');
fprintf('Loaded %d atoms\n', height(pos));

Step 4: Mass spectrum

spec = massSpecPlot(pos, 0.01, 'normalised');

Step 5: Ask about expected elements

Ask the user what elements they expect in their sample (e.g., "Fe, C, Mn" for steel). Then add ion markers:

ionAdd(spec, 'Fe', 2, isotopeTable, colorScheme);
ionAdd(spec, 'C', 1, isotopeTable, colorScheme);
% ... for each element at likely charge states

Step 6: Define ranges

Tell the user to check the spectrum in MATLAB and confirm the ion identification looks correct. Then:

rangeAddAll(spec, colorScheme, 0.04, true);

Step 7: Extract ranges and allocate

rangeTable = rangesExtractFromMassSpec(spec);
pos = posAllocateRange(pos, rangeTable, 'decomposed');

Step 8: Composition

detEff = cfg.reconstruction.detectionEfficiency;
conc = posCalculateConcentrationSimple(pos, detEff, {'unranged'}, '', 'mode', 'atomic');
unc = concentrationUncertainty(conc.counts);
disp(conc);

Step 9: Visualization

scatterPlotPosWidget(pos, colorScheme);

Step 10: Report

Present a summary:

Dataset: file name, atom count
Instrument: name and detection efficiency used
Composition table with uncertainties
Suggest next steps: ROI analysis, proxigram, cluster analysis

Mehr aus diesem Repository

gleiches Repository

apt-cluster-voronoi

peterfelfer/Atom-Probe-Toolbox

Voronoi-cell cluster determination (Felfer et al. 2015) — detect and extract clusters via Voronoi-cell volumes, Delaunay triangulation, and a Kolmogorov-Smirnov test against a randomised reference. Use when the user wants the Voronoi/clusterDetermination method specifically (this is NOT DBSCAN — for density-based DBSCAN use /apt-cluster instead).

2026-06-247

apt-colorscheme

peterfelfer/Atom-Probe-Toolbox

Create a new ion color scheme or add ions/colors to the existing one. Produces/updates the `colorScheme` workspace variable used by ranging and 3D visualization everywhere else. Use when the user wants custom ion colors, a new color scheme, or to add a missing ion color.

2026-06-247

apt-concentration-kernel

peterfelfer/Atom-Probe-Toolbox

Per-vertex concentration mapping onto a mesh/interface — compute atom-to-mesh distances, clip a slab around the interface, voxelise by nearest vertex, and apply a concentration kernel to colour each vertex. Use when the user wants a concentration map painted onto an isosurface or modelled grain boundary mesh.

2026-06-247

apt-data-quality

peterfelfer/Atom-Probe-Toolbox

Run quality-control checks on an atom probe dataset before analysis — basic stats, voltage curve, detector hit map (FDM), mass spectrum sanity, coordinate ranges, multi-hit fraction, and automated quality metrics. Use when the user wants to assess data quality or sanity-check a reconstruction.

2026-06-247

apt-fdm

peterfelfer/Atom-Probe-Toolbox

Create a field desorption map (detector hit-density / concentration image) and optionally a movie of detector activity over the course of the experiment. Use when the user wants an FDM, desorption map, detector image, or to watch features evolve during the measurement.

2026-06-247

apt-interfacial-excess

peterfelfer/Atom-Probe-Toolbox

Quantify interfacial excess (Gibbsian excess of solute, in at/nm^2) across an interface — either a single value via the Krakauer-Seidman integral method, or a 2D excess map over the surface. Use when the user wants the amount of an element segregated to an interface or grain boundary.

2026-06-247

name	apt-analyze
description	Run a full atom probe analysis pipeline on a data file. Loads data, creates mass spectrum, and guides through ion identification, ranging, and composition calculation. Use for end-to-end APT analysis.
argument-hint	<file.pos\|file.epos>

Run a complete atom probe analysis pipeline. Use the MATLAB MCP server.

The file to analyze is: $ARGUMENTS

If no file is provided, ask the user for the file path.

Full Pipeline

Step 1: Setup (if not already done)

setupToolbox;
load('isotopeTable_naturalAbundances.mat');
load('colorScheme_default.mat');
cfg = APTConfig.getInstance();

Step 2: Ask about the instrument

Before proceeding, ask the user which instrument acquired this data:

LEAP 4000X HR (detEff = 0.37)
LEAP 5000 XR (detEff = 0.52)
LEAP 5000 XS (detEff = 0.80)
EIKOS (detEff = 0.50)

Apply the preset:

cfg.applyInstrumentPreset('LEAP5000XR');  % adjust based on answer

Step 3: Load data

pos = posLoad('$ARGUMENTS');
fprintf('Loaded %d atoms\n', height(pos));

Step 4: Mass spectrum

spec = massSpecPlot(pos, 0.01, 'normalised');

Step 5: Ask about expected elements

Ask the user what elements they expect in their sample (e.g., "Fe, C, Mn" for steel). Then add ion markers:

ionAdd(spec, 'Fe', 2, isotopeTable, colorScheme);
ionAdd(spec, 'C', 1, isotopeTable, colorScheme);
% ... for each element at likely charge states

Step 6: Define ranges

Tell the user to check the spectrum in MATLAB and confirm the ion identification looks correct. Then:

rangeAddAll(spec, colorScheme, 0.04, true);

Step 7: Extract ranges and allocate

rangeTable = rangesExtractFromMassSpec(spec);
pos = posAllocateRange(pos, rangeTable, 'decomposed');

Step 8: Composition

detEff = cfg.reconstruction.detectionEfficiency;
conc = posCalculateConcentrationSimple(pos, detEff, {'unranged'}, '', 'mode', 'atomic');
unc = concentrationUncertainty(conc.counts);
disp(conc);

Step 9: Visualization

scatterPlotPosWidget(pos, colorScheme);

Step 10: Report

Present a summary:

Dataset: file name, atom count
Instrument: name and detection efficiency used
Composition table with uncertainties
Suggest next steps: ROI analysis, proxigram, cluster analysis