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Use when you have multiple independent peak-picking algorithms available and need to allow end-users to select among them for the same analytical task (peak detection in untargeted LC-MS data) without coupling the rest of your pipeline to each algorithm's API.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | algorithm-interface-abstraction |
| description | Use when you have multiple independent peak-picking algorithms available and need to allow end-users to select among them for the same analytical task (peak detection in untargeted LC-MS data) without coupling the rest of your pipeline to each algorithm's API. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3214","edam_topics":["http://edamontology.org/topic_3172","http://edamontology.org/topic_0091"],"tools":["Centwave","FeatureFinderMetabo","ADAP","SLAW"],"techniques":["LC-MS"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/acs.analchem.1c02687","title":"slaw"}] |
| evidence_spans | ["Wrapping of three main peak picking algorithms: Centwave, FeatureFinderMetabo, ADAP"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_slaw_cq","doi":"10.1021/acs.analchem.1c02687","title":"slaw"}],"dedup_kept_from":"coll_slaw_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.analchem.1c02687","all_source_dois":["10.1021/acs.analchem.1c02687"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Design a unified interface that wraps multiple interchangeable peak-picking algorithms (Centwave, FeatureFinderMetabo, ADAP) and routes raw LC-MS data to the selected algorithm via configuration-driven dispatch, standardizing outputs to a common feature table format. This abstraction isolates algorithm-specific details and allows users to swap peak-picking methods without rewriting downstream processing logic.
You have multiple independent peak-picking algorithms available and need to allow end-users to select among them for the same analytical task (peak detection in untargeted LC-MS data) without coupling the rest of your pipeline to each algorithm's API. Typical trigger: a configuration file specifying algorithm choice ('Centwave' vs 'FeatureFinderMetabo' vs 'ADAP') and raw mzML or netCDF input data.
Implement a routing layer that accepts a configuration key naming the target peak-picking algorithm and centroided LC-MS data (mzML or netCDF format). Route the data to the corresponding algorithm-specific wrapper module based on the configuration value. Execute the selected algorithm with user-supplied parameters. Standardize the algorithm's output (which may differ in column names, units, or structure) into a common internal format—typically a feature matrix with columns for m/z, retention time, and intensity. Return both the standardized feature table and algorithm metadata (e.g., algorithm name, version, parameters used) so downstream stages (alignment, grouping, gap-filling) operate uniformly. This decouples downstream code from algorithm-specific output schemas and allows parameter swapping via configuration without code changes.
docker run --rm -v /path/to/input:/input -v /path/to/output:/output zambonilab/slaw:latest