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/metabolomics-xcms-preprocessing

Load when exercising the CLI and replay pipeline with a synthetic LC-MS peak table. Skip real mzML preprocessing (run XCMS externally); table peak picking belongs to metabolomics-peak-detection.

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omicsclaw
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Install
$ npx -y skills add tiangzlab/omicsclaw --skill metabolomics-xcms-preprocessing --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/metabolomics-xcms-preprocessing

Context preview

The summary Claude sees to decide when to auto-load this skill.

Load when exercising the CLI and replay pipeline with a synthetic LC-MS peak table. Skip real mzML preprocessing (run XCMS externally); table peak picking belongs to metabolomics-peak-detection.

SKILL.md

metabolomics-xcms-preprocessing.SKILL.md
name: metabolomics-xcms-preprocessing
description: Load when exercising the CLI and replay pipeline with a synthetic LC-MS peak table. Skip real mzML preprocessing (run XCMS externally); table peak picking belongs to metabolomics-peak-detection.
trigger: xcms demo, synthetic metabolomics peaks
tags:
- metabolomics
- demo
- cli-only

metabolomics-xcms-preprocessing

When to use

This CLI generates synthetic peak tables only. It does not read mzML spectra, run XCMS, align retention times or perform gap filling. Real `--input` runs fail before writing results. Use XCMS externally for raw LC-MS analysis. The skill remains CLI-only because it has no implemented scientific analysis that can be exposed as a computational function library.

Inputs & Outputs

`--demo` needs no input. Outputs are `tables/peak_table.csv`, `report.md`, `result.json` and `reproducibility/commands.sh`. The table contains synthetic m/z bounds, retention-time bounds, integrated/maximum intensities and five sample columns. There are no Figures or AnnData outputs.

Flow

1. Reject real input and invalid ppm/peak-width settings. 2. Generate a seeded synthetic peak table using the supplied demo parameters. 3. Write the CSV, report, result envelope and command record.

Gotchas

  • `xcms_preprocess_python` uses seed 42 and never reads spectra. The name is historical.
  • `--demo --ppm 5` changes `tables/peak_table.csv` from 1,200 to 240 rows with default peak widths; these are simulated parameter responses.
  • `result.json` summary contains n_samples, n_peaks, mz_min, mz_max, rt_min and rt_max. They describe the simulation only.

Key CLI

python skills/metabolomics/metabolomics-xcms-preprocessing/metabolomics_xcms_preprocessing.py --demo --output /tmp/xcms_demo

Steps call `run_cli('metabolomics-xcms-preprocessing', '--demo')`. [examples/example_step.py](examples/example_step.py) records and checks the synthetic peak table through the step runner and fresh replay.

See also

  • [Parameters](references/parameters.md)
  • [Methodology](references/methodology.md)
  • [Output contract](references/output_contract.md)
  • metabolomics-peak-detection and metabolomics-quantification accept feature tables.

Dependencies

`numpy`, `pandas`

Read more
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