Agent skill

Metabolomics Peak Detection

Load when running per-sample peak picking on a feature × intensity table via scipy.signal.findpeaks — emits per-(sample, feature) detected peaks with prominence and width.

Apache-2.0Auto-check passedData & Analytics

Install Metabolomics Peak Detection

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw metabolomics-peak-detection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/metabolomics/metabolomics-peak-detection .claude/skills/metabolomics-peak-detection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "metabolomics-peak-detection" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection into .claude/skills/metabolomics-peak-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-peak-detection", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw metabolomics-peak-detection --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/metabolomics/metabolomics-peak-detection .agents/skills/metabolomics-peak-detection && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "metabolomics-peak-detection" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection into .agents/skills/metabolomics-peak-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-peak-detection", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw metabolomics-peak-detection --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/metabolomics/metabolomics-peak-detection .cursor/skills/metabolomics-peak-detection && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "metabolomics-peak-detection" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection into .cursor/skills/metabolomics-peak-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-peak-detection", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/TianGzlab/OmicsClaw.git --path skills/metabolomics/metabolomics-peak-detection

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw metabolomics-peak-detection --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/metabolomics/metabolomics-peak-detection .gemini/skills/metabolomics-peak-detection && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "metabolomics-peak-detection" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection into .gemini/skills/metabolomics-peak-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-peak-detection", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw metabolomics-peak-detection

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/metabolomics/metabolomics-peak-detection .github/skills/metabolomics-peak-detection && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "metabolomics-peak-detection" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection into .github/skills/metabolomics-peak-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-peak-detection", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw metabolomics-peak-detection --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/metabolomics/metabolomics-peak-detection .opencode/skills/metabolomics-peak-detection && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "metabolomics-peak-detection" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection into .opencode/skills/metabolomics-peak-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-peak-detection", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: metabolomics-peak-detection
GitHub stars: 161
Token cost: ~914 tokens
SKILL.md length: 326 words
Files: 8 (incl. references)
Skills in repo: 88
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Load when running per-sample peak picking on a feature × intensity table via scipy.signal.findpeaks — emits per-(sample, feature) detected peaks with prominence and width.

Data & Analytics work in your project
SKILL.md covers When to use, Use from a step, API and Methods and parameters, plus 5 more sections
Runs Python scripts from its folder; calls python

What it does

Metabolomics Peak Detection is an agent skill from TianGzlab/OmicsClaw. Load when running per-sample peak picking on a feature × intensity table via scipy.signal.findpeaks — emits per-(sample, feature) detected peaks with prominence and width. Skip when working with mz / RT raw scans (use metabolomics-xcms-preprocessing); only normalising / quantifying (use metabolomics-quantification).

Its SKILL.md is about 910 tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `peak_detect.py`).

It sits in Data & Analytics. The repository describes itself as: Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper. The licence is Apache-2.0.

When your agent uses it

Data & Analytics work in your project

Example prompts

“/metabolomics-peak-detection”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 90a3bec. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Metabolomics Peak Detection loads about 914 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 87 tokens; SKILL.md has 326 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~87
When it runs · the whole SKILL.md, loaded when a task matches: ~914
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TianGzlab/OmicsClaw at commit 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 326 words, ~914 tokens.

Download SKILL.mdSave it as .claude/skills/metabolomics-peak-detection/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.

name: metabolomics-peak-detection
description: Load when running per-sample peak picking on a feature × intensity table via `scipy.signal.find_peaks` — emits per-(sample, feature) detected peaks with prominence and width. Skip when working with mz / RT raw scans (use metabolomics-xcms-preprocessing); only normalising / quantifying (use metabolomics-quantification).
trigger: peak detection, feature detection, XCMS, MZmine, MS-DIAL, peak picking
tags: metabolomics, peak-detection, find-peaks, xcms, mzmine

metabolomics-peak-detection

When to use

Detect peaks on tabular sample signals ordered by retention time. This is not raw mzML peak extraction; run XCMS externally for that workflow.

Use from a step

python

import pandas as pd
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("metabolomics-peak-detection")
data = read_input('features.csv', reader=pd.read_csv)
result = library.detect_peaks(data)
write_output(result, 'tables/result.csv')

examples/example_step.py runs a seeded synthetic example through the step runner and writes a table and Figure. Computations return new DataFrames, leave the input unchanged and expose diagnostics through run_info(result). Plotting functions write no files.

API

<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->

`detect_peaks(data, *, sample_cols=None, prominence=10000.0, height=None, distance=5)`

Detect per-sample peaks after sorting rows by retention time.

:param data: DataFrame with mz, rt and numeric sample intensities. :param sample_cols: Explicit columns; None uses the CLI intensity/sample name detection. :param prominence: CLI default 10000; adjust to the intensity scale. :param height: CLI default None; optionally require a minimum peak height. :param distance: CLI default 5; minimum separation in sorted row positions, not seconds. :returns: A new peak DataFrame with diagnostics in attrs['run_info']. :raises ValueError: No sample columns match or peak parameters are invalid.

`run_info(data, *, keep=True)`

Read diagnostics attached to a returned table.

:param data: DataFrame returned by this library. :param keep: Default True; use False in the CLI to remove diagnostics. :returns: An independent dictionary describing the run. :raises ValueError: The table carries no run_info.

`peaks_figure(data)`

Plot detected peak intensity against retention time.

:param data: Peak table returned by detect_peaks. :returns: A matplotlib Figure. :raises KeyError: Required peak columns are absent.

<!-- api:end -->

Methods and parameters

scipy.signal.find_peaks uses prominence, optional height and a row-index distance after sorting by rt. Widths are measured at half prominence in row-index units.

Gotchas

detect_peaks expects mz and rt plus sample/intensity columns. distance and width are row positions, not seconds. NaNs follow scipy signal semantics and are not imputed. Empty outputs keep their CSV column schema.

Inputs and outputs

CSV input; tables/detected_peaks.csv, report.md and result.json. Demo mode also writes its synthetic input CSV at the output root. The function library returns objects; the CLI and step own file writes.

CLI

bash

python skills/metabolomics/metabolomics-peak-detection/peak_detect.py --demo --output /tmp/metabolomics_peak_detection

Dependencies

numpy, pandas, scipy, matplotlib

Files

SKILL.md and 7 other files (references) in skills/metabolomics/metabolomics-peak-detection of TianGzlab/OmicsClaw.

SKILL.md
_api.py
examples/example_step.py
peak_detect.py
references/methodology.md
references/output_contract.md
references/parameters.md
tests/test_api.py

Open the folder on GitHubat commit 90a3bec

Compare with similar skills

Metabolomics Peak Detection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Metabolomics Peak Detection compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Metabolomics Peak Detection this skillTianGzlab/OmicsClaw	161	—	~914	Automated safety check: Pass	Apache-2.0	3 days ago
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Exploratory Data Analysisspacering-net/codeg	3.9k	14 repos	~3.6k	Automated safety check: Pass	MIT	today
Scikit LearnzLanqing/codex-claude-academic-skills	4.7k	16 repos	~3.9k	Automated safety check: Pass	BSD-3-Clause	4 mo ago
Chart Visualizationbytedance/deer-flow	84k	1 repos	~840	Automated safety check: Pass	MIT	today
TimesFM Forecastinggoogle-research/timesfm	34k	—	~4.7k	Automated safety check: Pass	Apache-2.0	11 days ago

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Questions about Metabolomics Peak Detection

What does Metabolomics Peak Detection do?

Load when running per-sample peak picking on a feature × intensity table via scipy.signal.findpeaks — emits per-(sample, feature) detected peaks with prominence and width. Metabolomics Peak Detection is an agent skill from TianGzlab/OmicsClaw.findpeaks — emits per-(sample, feature) detected peaks with prominence and width.

When should I use Metabolomics Peak Detection?

Metabolomics Peak Detection fits situations like: data & Analytics work in your project.

How do I install Metabolomics Peak Detection in Claude Code?

Run `npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a claude-code`. Or copy the skill folder (skills/metabolomics/metabolomics-peak-detection in TianGzlab/OmicsClaw) into .claude/skills/metabolomics-peak-detection in your project. Claude Code loads it when a task matches its description.

How do I install Metabolomics Peak Detection in Codex?

Run `npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a codex`. Or copy the skill folder (skills/metabolomics/metabolomics-peak-detection in TianGzlab/OmicsClaw) into .agents/skills/metabolomics-peak-detection in your project. Codex loads it when a task matches its description.

Can I use Metabolomics Peak Detection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TianGzlab/OmicsClaw --skill metabolomics-peak-detection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/metabolomics-peak-detection, .gemini/skills/metabolomics-peak-detection, .github/skills/metabolomics-peak-detection and .opencode/skills/metabolomics-peak-detection in your project.

What does Metabolomics Peak Detection need to run?

Going by SKILL.md and its folder, Metabolomics Peak Detection needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Metabolomics Peak Detection access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Metabolomics Peak Detection safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Metabolomics Peak Detection use?

Metabolomics Peak Detection is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Metabolomics Peak Detection use?

About 914 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 314 tokens, read only when the agent opens those files.

What are the alternatives to Metabolomics Peak Detection?

Skills that share tags, products or a category with Metabolomics Peak Detection: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Metabolomics Peak Detection?

TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on October 7, 2026.

Source: TianGzlab/OmicsClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Metabolomics Peak Detection

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

metabolomics-peak-detection

When to use

Use from a step

API

detect_peaks(data, *, sample_cols=None, prominence=10000.0, height=None, distance=5)

run_info(data, *, keep=True)

peaks_figure(data)

Methods and parameters

Gotchas

Inputs and outputs

CLI

See also

Dependencies

Files

Compare with similar skills

Similar skills

More from TianGzlab/OmicsClaw

Related

Questions about Metabolomics Peak Detection

`detect_peaks(data, *, sample_cols=None, prominence=10000.0, height=None, distance=5)`

`run_info(data, *, keep=True)`

`peaks_figure(data)`