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Agent skill

Proteomics De

by ClawBio in ClawBio/ClawBio

Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output.

MITAuto-check passedResearch & Science

Install Proteomics De

skills CLI
$ npx skills add ClawBio/ClawBio --skill proteomics-de -a claude-code

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

GitHub CLI
$ gh skill install ClawBio/ClawBio proteomics-de --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/ClawBio/ClawBio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/proteomics-de .claude/skills/proteomics-de && 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/

Facts

Skill name
proteomics-de
GitHub stars
1.2k
Used in
1 other repo
Token cost
~1.6k tokens
SKILL.md length
518 words
Files
7
Skills in repo
104
Repo updated
First seen
Licence
MIT

At a glance

Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output.

  • Works in 8 steps: Multi-format Input Support → Preprocessing Strategy → Intensity Transformation → …
  • Tasks that involve Bioinformatics
  • SKILL.md covers Domain Decisions, Safety Rules, Agent Boundary and Input Contract, plus 3 more sections
  • Runs Python scripts from its folder; calls python

What it does

Proteomics De is an agent skill from ClawBio/ClawBio. Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files (for example `proteomics_de.py` and `tests/test_proteomics_de.py`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: 🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free. The licence is MIT.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/proteomics-de”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Multi-format Input Support
  2. Preprocessing Strategy
  3. Intensity Transformation
  4. Missing Value Imputation
  5. Statistical Testing
  6. s0-based FDR Correction
  7. Significance Thresholding
  8. Visualization Outputs

What it can do on your machine

Read from SKILL.md and the folder at commit 5e045e3. 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

Proteomics De loads about 1.6k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 518 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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 ClawBio/ClawBio at commit 5e045e3, republished under its MIT licence (© ClawBio). 518 words, ~1,637 tokens.

Download SKILL.mdSave it as .claude/skills/proteomics-de/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
proteomics-de
description
Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.
license
MIT
metadata.version
0.1.0

🥚 Proteomics Differential Expression

This skill performs differential expression analysis on label-free quantitative (LFQ) intensity data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.

Domain Decisions

1. Multi-format Input Support
  • Supports MaxQuant proteinGroups.txt
    • Automatic filtering of reverse hits, contaminants, and site-only identifications
  • Supports DIA-NN output
    • Automatically extracts protein IDs and .raw intensity columns
2. Preprocessing Strategy
  • MaxQuant:
    • Filters:
      • Reverse
      • Potential contaminant / Contaminant
      • Only identified by site
  • DIA-NN:
    • Extracts protein identifiers and intensity matrix directly
3. Intensity Transformation
  • LFQ intensities are transformed using log2 scaling
  • Ensures approximate normality for downstream statistical testing
4. Missing Value Imputation
  • Uses down-shifted Gaussian imputation
    • Mean shifted by: median - shift × std
    • Default:
      • shift = 1.8
      • scale = 0.3
  • Assumption:
    • Missing values represent low-abundance proteins
5. Statistical Testing
  • Two-sample t-test between treatment and control groups
  • Default degrees of freedom:
    • df = 4 (for 3 vs 3 replicates)
6. s0-based FDR Correction
  • Uses s0-based thresholding to stabilize variance
  • Combines:
    • log2 fold change
    • p-value
  • Based on:
    • Giai Gianetto et al. (2016)
7. Significance Thresholding
  • Default:
    • FDR = 0.05
    • s0 = 0.1
  • Produces:
    • Adjusted significance boundary (used in volcano plot)
8. Visualization Outputs
  • PCA plot
  • Volcano plot (with s0 curve)
  • Imputation distribution comparison

Safety Rules

  • Local-first

    • No data upload without explicit user consent

  • Statistical caution

    • Statistical results should be interpreted with caution and not overinterpreted
    • Avoid drawing conclusions beyond what the data supports

  • Missing data assumptions

    • Imputation assumes missing values correspond to low abundance
    • May not hold in all experimental designs

  • Small sample limitations

    • t-test reliability depends on sufficient replicates

  • Reproducibility

    • All parameters and commands are logged

  • No hallucinated science

    • All methods are based on established proteomics workflows

Agent Boundary

This skill DOES:
  • Perform differential expression analysis on LFQ proteomics data
  • Handle MaxQuant and DIA-NN outputs
  • Generate statistical results and visualizations
  • Produce reproducible reports
This skill DOES NOT:
  • Process raw mass spectrometry data (e.g. RAW files)
  • Perform peptide identification or database search
  • Conduct pathway or functional enrichment analysis
  • Provide biological interpretation of results
Show full SKILL.md (197 more words)Show less

Input Contract

Supported Input Formats
  1. MaxQuant proteinGroups.txt
  2. DIA-NN output (.tsv / .txt)
Metadata Requirements
  • .csv or .tsv
  • Must include:
    • sample_id
    • group

Supports:

  • raw names
  • full paths (e.g. /path/sample.raw)

Output Structure

proteomics_de_report/
├── report.md
├── figures/
│   ├── imputation_distribution.png
│   ├── pca.png
│   └── volcano.png
├── tables/
│   ├── imputed_proteinGroups.csv
│   └── de_results.csv
├── ro-crate-metadata.json
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Usage

Demo
bash
python proteomics_de.py \
  --demo \
  --output report_dir
MaxQuant Input
bash
python proteomics_de.py \
  --input proteinGroups.txt \
  --input-type maxquant \
  --metadata metadata.csv \
  --contrast "treated,control" \
  --output report_dir
DIA-NN Input
bash
python proteomics_de.py \
  --input diann_output.tsv \
  --input-type diann \
  --metadata metadata.csv \
  --contrast "treated,control" \
  --output report_dir
Parameters
ParameterDescriptionDefault
--inputInput file path-
--input-typemaxquant or diannmaxquant
--metadataMetadata file-
--contrasttreatment,controltreated,control
--s0s0 parameter0.1
--fdrFDR threshold0.05
--ttest-dfDegrees of freedom4
--imputation-shiftImputation shift1.8
--imputation-scaleImputation scale0.3
--outputOutput directory-

References

  • test_proteinGroups.txt is from: Keilhauer EC, Hein MY, Mann M. Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS). Mol Cell Proteomics. 2015 Jan;14(1):120-35. doi: 10.1074/mcp.M114.041012. Epub 2014 Nov 2. PMID: 25363814; PMCID: PMC4288248.
  • s0 correction algorithm is from: Giai Gianetto Q, Couté Y, Bruley C, Burger T. Uses and misuses of the fudge factor in quantitative discovery proteomics. Proteomics. 2016 Jul;16(14):1955-60. doi: 10.1002/pmic.201600132. PMID: 27272648.
  • s0 correction algorithm is cited by: Michaelis AC, Brunner AD, Zwiebel M, Meier F, Strauss MT, Bludau I, Mann M. The social and structural architecture of the yeast protein interactome. Nature. 2023 Dec;624(7990):192-200. doi: 10.1038/s41586-023-06739-5. Epub 2023 Nov 15. PMID: 37968396; PMCID: PMC10700138.

© ClawBio, MIT. 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 6 other files in skills/proteomics-de of ClawBio/ClawBio.

  • SKILL.md
  • examples/test_diann.tsv
  • examples/test_metadata.csv
  • examples/test_metadata_diann.csv
  • examples/test_proteinGroups.txt
  • proteomics_de.py
  • tests/test_proteomics_de.py

Open the folder on GitHubat commit 5e045e3

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in ClawBio/ClawBio, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Proteomics De 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.

Proteomics De compared with similar skills
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13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
MFA Pipeline Orchestratoraiming-lab/AutoResearchClaw15k—~923Automated safety check: PassMIT

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Categories

Questions about Proteomics De

What does Proteomics De do?

Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Proteomics De is an agent skill from ClawBio/ClawBio. Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output.

When should I use Proteomics De?

Proteomics De fits situations like: tasks that involve Bioinformatics.

How do I install Proteomics De in Claude Code?

Run `npx skills add ClawBio/ClawBio --skill proteomics-de -a claude-code`. Or copy the skill folder (skills/proteomics-de in ClawBio/ClawBio) into .claude/skills/proteomics-de in your project. Claude Code loads it when a task matches its description.

How do I install Proteomics De in Codex?

Run `npx skills add ClawBio/ClawBio --skill proteomics-de -a codex`. Or copy the skill folder (skills/proteomics-de in ClawBio/ClawBio) into .agents/skills/proteomics-de in your project. Codex loads it when a task matches its description.

Can I use Proteomics De 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 ClawBio/ClawBio --skill proteomics-de -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/proteomics-de, .gemini/skills/proteomics-de, .github/skills/proteomics-de and .opencode/skills/proteomics-de in your project.

What does Proteomics De need to run?

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

Does Proteomics De 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 Proteomics De 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 Proteomics De use?

Proteomics De is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Proteomics De use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Proteomics De?

Skills that share tags, products or a category with Proteomics De: Dbsnp Database (google-deepmind/science-skills, 3.2k stars), Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars) and Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Proteomics De?

ClawBio (a GitHub organization) maintains it in ClawBio/ClawBio, which has 1,154 GitHub stars. The repository holds 104 skills in this directory. The repository was last updated on October 7, 2026.

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