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metabolomics-statistics

metabolomics-statistics is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 96 tokens per session (1,306 once invoked), scanned A, original, Apache-2.0.

A statistical analysis tool for a metabolomics CSV with features in rows and samples in columns. It compares two sample groups using common statistical tests and adjusts the results for multiple comparisons.

In plain words
What is it for?
Use it for two-group metabolomics analysis with t-tests, Wilcoxon tests, ANOVA, or Kruskal-Wallis tests, including false-discovery-rate results.
Why use it?
It helps identify measurements that differ between groups while reducing misleading significance caused by testing many features. Group columns can be selected by prefixes or split at the midpoint.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 160repo 1mo ago A scan SkillSpector: warn 96 tokens original Apache-2.0

Good fit Use it for two-group metabolomics analysis with t-tests, Wilcoxon tests, ANOVA, or Kruskal-Wallis tests, including false-discovery-rate results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tiangzlab/omicsclaw/metabolomics-statistics
View source ↗ TianGzlab/OmicsClaw tiangzlab.github.io
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add TianGzlab/OmicsClaw --skill metabolomics-statistics
Clone the repo
git clone --depth 1 https://github.com/TianGzlab/OmicsClaw

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for metabolomics-statistics

README.md
[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/metabolomics-statistics/github.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/metabolomics-statistics)
Your own site
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/metabolomics-statistics"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/metabolomics-statistics/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for metabolomics-statistics

Your own site · 80×15
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/metabolomics-statistics"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/metabolomics-statistics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,306 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Rogue Agent · line 3
    Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.
    Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00096 $0.01306
Opus 5 $0.00048 $0.00653
Sonnet 5 $0.00019 $0.00261
Haiku 4.5 $0.00010 $0.00131

Measured 5d ago against content hash 47a776299315, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

metabolomics-statistics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (metabolomics_statistics.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/metabolomics/metabolomics-statistics/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

metabolomics-statistics

When to use

The user has a wide feature × sample CSV (rows = features as index, columns = samples) and wants univariate two-group testing. Four backends:

  • ttest (default) — Welch's two-sample t-test.
  • wilcoxon — Mann-Whitney U (non-parametric).
  • anova — one-way ANOVA (two-group case ≡ equal-variance t-test).
  • kruskal — Kruskal-Wallis (non-parametric ANOVA).

--group1-prefix / --group2-prefix select sample columns by prefix; without them the script splits at column-midpoint with a warning. Significance threshold via --alpha (default 0.05); BH-FDR adjusted.

For metabolomics-DE with default ctrl / treat column prefixes use metabolomics-de. For raw spectra use metabolomics-xcms-preprocessing.

Inputs & Outputs

Inputs

  • File types: .csv
  • Accepts artifact metabolomics.feature_matrix (csv)

Outputs

  • tables/statistics.csv
  • tables/significant.csv
  • report.md
  • result.json

Flow

  1. Load CSV with pd.read_csv(args.input_path, index_col=0) (metabolomics_statistics.py:325).
  2. If both --group1-prefix and --group2-prefix are set, filter columns by c.startswith(prefix) (metabolomics_statistics.py:330-331); else fall back to midpoint split with a warning (:333-340).
  3. If either group is empty, raise ValueError("Could not determine group columns. ...") at :344.
  4. Dispatch on --method (:209 rejects unknown with ValueError); per-feature test → pvalue + BH-adjusted fdr.
  5. Filter fdr < args.alpha → tables/significant.csv (:363).
  6. Write tables/statistics.csv (metabolomics_statistics.py:360) + report + result.json.

Gotchas

  • Group prefixes are OPTIONAL with midpoint fallback. metabolomics_statistics.py:329-340 only honours --group1-prefix / --group2-prefix when BOTH are passed; missing one or both falls back to midpoint split (first half / second half) with a warning. Always pass BOTH for explicit group control.
  • Empty group ⇒ ValueError. metabolomics_statistics.py:344 raises if either group's column list is empty (e.g. typo in prefix). Sanity-check --group1-prefix / --group2-prefix against your column names.
  • Index column 0 is the feature ID. pd.read_csv(args.input_path, index_col=0) (:325) is unconditional — make sure your feature-ID column is the FIRST column in the CSV.
  • anova = equal-variance t-test in the two-group case (metabolomics_statistics.py:138-140). For more than two groups, this skill silently assumes two — extend group_cols lists or use a different tool for true multi-group ANOVA.
  • wilcoxon here is Mann-Whitney U (independent samples), NOT paired Wilcoxon signed-rank. Don't use it for paired designs.
  • --input REQUIRED unless --demo. metabolomics_statistics.py:324 raises ValueError("--input required when not using --demo").
  • log2FC direction depends on group order. group2_mean - group1_mean convention; pass groups in the right order.

Read the full file on GitHub · 108 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 108 lines · 96 tokens per session scan A 47a776299315

Subscribe to this mod's changes

metabolomics-statistics is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 96 tokens to every session and 1,306 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Scientificanovabh-fdrbioinformaticsknowledge-graphkruskalllm-agentmetabolomicsmulti-agentsmulti-omicsstatisticsttestwilcoxon
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