Data Table Analysis
NVIDIA-AI-Blueprints/deep-researcher-agent
A skill your agent uses for converting researched facts or user-provided data into structured tables by writing code, then running Python/pandas calculations in the job-scoped sandbox.
Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table.
Install Metabolomics Normalization
$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TianGzlab/OmicsClaw metabolomics-normalization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/metabolomics/metabolomics-normalization .claude/skills/metabolomics-normalization && rm -rf skills-srcUse ~/.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/
Install the "metabolomics-normalization" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-normalization into .claude/skills/metabolomics-normalization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-normalization", 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 install https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-normalizationType 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.
$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TianGzlab/OmicsClaw metabolomics-normalization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/metabolomics/metabolomics-normalization .agents/skills/metabolomics-normalization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "metabolomics-normalization" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-normalization into .agents/skills/metabolomics-normalization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-normalization", 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.
$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TianGzlab/OmicsClaw metabolomics-normalization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/metabolomics/metabolomics-normalization .cursor/skills/metabolomics-normalization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "metabolomics-normalization" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-normalization into .cursor/skills/metabolomics-normalization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-normalization", 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 https://github.com/TianGzlab/OmicsClaw.git --path skills/metabolomics/metabolomics-normalization--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TianGzlab/OmicsClaw metabolomics-normalization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/metabolomics/metabolomics-normalization .gemini/skills/metabolomics-normalization && rm -rf skills-srcUse ~/.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/
Install the "metabolomics-normalization" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-normalization into .gemini/skills/metabolomics-normalization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-normalization", 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.
$ gh skill install TianGzlab/OmicsClaw metabolomics-normalizationInstalls 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).
$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/metabolomics/metabolomics-normalization .github/skills/metabolomics-normalization && rm -rf skills-srcUse ~/.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/
Install the "metabolomics-normalization" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-normalization into .github/skills/metabolomics-normalization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-normalization", 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.
$ npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install TianGzlab/OmicsClaw metabolomics-normalization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/metabolomics/metabolomics-normalization .opencode/skills/metabolomics-normalization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "metabolomics-normalization" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-normalization into .opencode/skills/metabolomics-normalization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metabolomics-normalization", 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-normalization- GitHub stars
- 161
- Token cost
- ~836 tokens
- SKILL.md length
- 270 words
- Files
- 8 (incl. references)
- Skills in repo
- 88
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table.
- Tasks that involve Database schema design
- 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
- Tasks that involve CSV and tabular files
What it does
Metabolomics Normalization is an agent skill from TianGzlab/OmicsClaw. Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table. Skip when also imputing (use metabolomics-quantification); raw spectra (use metabolomics-xcms-preprocessing).
Its SKILL.md is about 840 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 `metabolomics_normalization.py`).
It sits in Data & Analytics, covering Database schema design, CSV and tabular files and DataFrames. 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
- Tasks that involve Database schema design
- Tasks that involve CSV and tabular files
- Tasks that involve DataFrames
Example prompts
- “/metabolomics-normalization”
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 Normalization loads about 836 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 270 words of instructions outside code blocks.
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). 270 words, ~836 tokens.
Download SKILL.mdSave it as
.claude/skills/metabolomics-normalization/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- metabolomics-normalization
- description
- Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table. Skip when also imputing (use metabolomics-quantification); raw spectra (use metabolomics-xcms-preprocessing).
- trigger
- metabolomics normalization, scaling, NOREVA, TIC normalization
- tags
- metabolomics, normalization, pqn, quantile, median, log
metabolomics-normalization
When to use
Normalize a numeric feature-by-sample table. Use metabolomics-quantification when missing-value imputation is also needed.
Use from a step
import pandas as pd
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("metabolomics-normalization")
data = read_input('features.csv', reader=lambda path: pd.read_csv(path, index_col=0))
result = library.normalize(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 -->normalize(data, *, method='median')
Return a normalized copy, preserving feature and sample labels.
:param data: Numeric feature-by-sample DataFrame; NaNs retain method semantics. :param method: CLI default median; quantile, total, pqn or log are alternatives. :returns: A new DataFrame with method and dimensions in attrs['run_info']. :raises ValueError: The requested method is unknown.
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.
distribution_figure(data)
Plot normalized sample distributions without writing a file.
:param data: Numeric feature-by-sample DataFrame. :returns: A matplotlib Figure. :raises ValueError: No numeric columns are available.
<!-- api:end -->Methods and parameters
Median and total scale each column to the median column median or sum. Quantile maps ranks to averaged sorted values. PQN uses a TIC-normalized reference to estimate quotients, then divides the original intensities. Log computes log2(x+1).
Gotchas
normalizepreserves NaNs according to the method and performs no imputation. Zero divisors become NaN. The input index is retained intables/normalized.csv.
Inputs and outputs
CSV input; tables/normalized.csv, report.md and result.json. The CLI writes reproducibility/commands.sh.
The function library returns objects; the CLI and step own file writes.
CLI
python skills/metabolomics/metabolomics-normalization/metabolomics_normalization.py --demo --output /tmp/metabolomics_normalizationSee also
Dependencies
numpy, pandas, matplotlib
© TianGzlab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 7 other files (references) in skills/metabolomics/metabolomics-normalization of TianGzlab/OmicsClaw.
- SKILL.md
- _api.py
- examples/example_step.py
- metabolomics_normalization.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 Normalization 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Metabolomics Normalization this skillTianGzlab/OmicsClaw | 161 | — | ~836 | Automated safety check: Pass | Apache-2.0 | |
| Data Table AnalysisNVIDIA-AI-Blueprints/deep-researcher-agent | 883 | — | ~2.5k | Automated safety check: Pass | Apache-2.0 | |
| CSV Data Summarizercoffeefuelbump/csv-data-summarizer-claude-skill | 468 | 2 repos | ~1.4k | Automated safety check: Pass | None | |
| Deidentify A Datasetmaziyarpanahi/openmed | 5.5k | — | ~760 | Automated safety check: Pass | Apache-2.0 | |
| CSV Processingbenchflow-ai/skillsbench | 1.8k | — | ~455 | Automated safety check: Pass | Apache-2.0 | |
| Duckdb EnaAAaqwq/AGI-Super-Team | 105 | 1 repos | ~1.6k | Automated safety check: Pass | MIT |
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Related
Questions about Metabolomics Normalization
What does Metabolomics Normalization do?
Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table. Metabolomics Normalization is an agent skill from TianGzlab/OmicsClaw. Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table.
When should I use Metabolomics Normalization?
Metabolomics Normalization fits situations like: tasks that involve Database schema design; tasks that involve CSV and tabular files; tasks that involve DataFrames.
How do I install Metabolomics Normalization in Claude Code?
Run `npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a claude-code`. Or copy the skill folder (skills/metabolomics/metabolomics-normalization in TianGzlab/OmicsClaw) into .claude/skills/metabolomics-normalization in your project. Claude Code loads it when a task matches its description.
How do I install Metabolomics Normalization in Codex?
Run `npx skills add TianGzlab/OmicsClaw --skill metabolomics-normalization -a codex`. Or copy the skill folder (skills/metabolomics/metabolomics-normalization in TianGzlab/OmicsClaw) into .agents/skills/metabolomics-normalization in your project. Codex loads it when a task matches its description.
Can I use Metabolomics Normalization 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-normalization -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-normalization, .gemini/skills/metabolomics-normalization, .github/skills/metabolomics-normalization and .opencode/skills/metabolomics-normalization in your project.
What does Metabolomics Normalization need to run?
Going by SKILL.md and its folder, Metabolomics Normalization needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Metabolomics Normalization 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 Normalization 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 Normalization use?
Metabolomics Normalization 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 Normalization use?
About 836 tokens (SKILL.md is roughly 3.3k 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 321 tokens, read only when the agent opens those files.
What are the alternatives to Metabolomics Normalization?
Skills that share tags, products or a category with Metabolomics Normalization: Data Table Analysis (NVIDIA-AI-Blueprints/deep-researcher-agent, 883 stars), CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars), Deidentify A Dataset (maziyarpanahi/openmed, 5.5k stars) and CSV Processing (benchflow-ai/skillsbench, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Metabolomics Normalization?
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.