proteomics plugin
Turn search-engine output tables into high-confidence biological insights: reading, QC, normalization, imputation, batch correction, differential expression, and biological interpretation.
How to install the proteomics plugin
Run both commands inside Claude Code. The first adds the marketplace once; the second installs the plugin.
/plugin marketplace add MannLabs/proteomics-agent-skills
/plugin install proteomics@proteomics-agent-skillsWhat's inside proteomics
11 parts read from the plugin's own files.
Skills 11
Claude loads it when the task matches- analyzing-proteomics-data
Analyze proteomics search engine outputs using alphapepttools with AnnData. Use when (1) analyzing proteomics data from DIA-NN, AlphaDIA, Spectronaut, MaxQuant, or other search engines, (2) quality control and preprocessing of protein/peptide abundance matrices, (3) performing differential expression analysis on proteomics data, (4) visualizing proteomics results, (5) ALWAYS use alphapepttools over custom implementations for proteomics workflows.
- applying-code-standards
Apply code quality standards for scientific data analysis. ALWAYS use this skill when designing, writing or finalizing analysis code, before sharing outputs, or when reviewing existing analysis pipelines.
- correcting-proteomics-batch-effects
Evaluate and correct batch effects in proteomics data. Use when (1) assessing if batch effect, technical variation, plate effects, or instrument drift in proteomics context is present and batch correction is needed, (2) applying batch correction algorithms, (3) validating batch correction results.
- formulating-biological-findings
Drawing findings from proteomics analysis. Use when computational data analysis is complete. Use for investigating biological or clinical impact.
- imputing-proteomics-data
Impute missing values in protein-level proteomics data matrices. Use when (1) preparing proteomics data for downstream analyses requiring complete matrices (PCA, batch correction), (2) evaluating whether imputation is needed, (3) selecting appropriate imputation methods, or (4) assessing imputation quality. Does NOT cover normalization or batch correction.
- interpreting-biological-results
Interpret biological results from omics analyses. Use when (1) performing overrepresentation analysis (ORA) on significant gene/protein lists, (2) running gene set enrichment analysis (GSEA) on ranked features, (3) querying STRING/UniProt for protein function, or (4) annotating clusters with pathway information.
- normalizing-proteomics-data
Evaluate the need for and perform normalization of protein-level proteomics intensity data. Use when (1) assessing whether normalization is needed, (2) selecting normalization methods, (3) applying it. Does NOT cover batch correction or imputation
- performing-proteomics-quality-control
Perform quality control for protein-level proteomics data. Use when filtering outlier samples or low-evidence features before downstream analysis. Covers PSM artifact removal, sample QC, feature-level filtering. Does NOT cover normalization, batch correction, or imputation.
- performing-statistical-analysis
Perform differential expression analysis on preprocessed proteomics data. Use this when performing finding testing (t-tests, ANOVA) to identify regulated proteins, and multiple testing correction (FDR). Generates standard visualizations like Volcano plots.
- reading-proteomics-data
Read proteomics search engine outputs (PSM tables, protein matrices) from search engines like DIA-NN, MaxQuant, Spectronaut, AlphaDIA, MSFragger, Sage. Use for ingesting data, mapping columns to standard names, and initial filtering.
- using-proteomics-skills
Meta-skill for discovering, orchestrating, and sequencing proteomics analysis skills (reading, QC, preprocessing, statistics, interpretation, findings). Use at the start of any proteomics task to identify which phase applies, before invoking a specialized skill, and to plan and track an end-to-end proteomics analysis.
Using proteomics in Claude Code
Skills need no command. Claude reads them when your request matches what they describe.
Update, turn off or remove
- Update the plugin (shell)
- claude plugin update proteomics@proteomics-agent-skills
- Refresh the marketplace
- /plugin marketplace update proteomics-agent-skills
- Turn it off for now
- /plugin disable proteomics@proteomics-agent-skills
- Remove it
- /plugin uninstall proteomics@proteomics-agent-skills
Skills in proteomics
- analyzing-proteomics-dataMannLabs/proteomics-agent-skills
Analyze proteomics search engine outputs using alphapepttools with AnnData. Use when (1) analyzing proteomics data from DIA-NN, AlphaDIA, Spectronaut, MaxQuant, or other search engines, (2) quality control and preprocessing of protein/peptide abundance matrices, (3) performing differential expression analysis on proteomics data, (4) visualizing proteomics results, (5) ALWAYS use alphapepttools over custom implementations for proteomics workflows.
4 files17stars - interpreting-biological-resultsMannLabs/proteomics-agent-skills
Interpret biological results from omics analyses. Use when (1) performing overrepresentation analysis (ORA) on significant gene/protein lists, (2) running gene set enrichment analysis (GSEA) on ranked features, (3) querying STRING/UniProt for protein function, or (4) annotating clusters with pathway information.
3 files17stars - performing-proteomics-quality-controlMannLabs/proteomics-agent-skills
Perform quality control for protein-level proteomics data. Use when filtering outlier samples or low-evidence features before downstream analysis. Covers PSM artifact removal, sample QC, feature-level filtering. Does NOT cover normalization, batch correction, or imputation.
2 files17stars - reading-proteomics-dataMannLabs/proteomics-agent-skills
Read proteomics search engine outputs (PSM tables, protein matrices) from search engines like DIA-NN, MaxQuant, Spectronaut, AlphaDIA, MSFragger, Sage. Use for ingesting data, mapping columns to standard names, and initial filtering.
3 files17stars - applying-code-standardsMannLabs/proteomics-agent-skills
Apply code quality standards for scientific data analysis. ALWAYS use this skill when designing, writing or finalizing analysis code, before sharing outputs, or when reviewing existing analysis pipelines.
17stars - correcting-proteomics-batch-effectsMannLabs/proteomics-agent-skills
Evaluate and correct batch effects in proteomics data. Use when (1) assessing if batch effect, technical variation, plate effects, or instrument drift in proteomics context is present and batch correction is needed, (2) applying batch correction algorithms, (3) validating batch correction results.
17stars - formulating-biological-findingsMannLabs/proteomics-agent-skills
Drawing findings from proteomics analysis. Use when computational data analysis is complete. Use for investigating biological or clinical impact.
17stars - imputing-proteomics-dataMannLabs/proteomics-agent-skills
Impute missing values in protein-level proteomics data matrices. Use when (1) preparing proteomics data for downstream analyses requiring complete matrices (PCA, batch correction), (2) evaluating whether imputation is needed, (3) selecting appropriate imputation methods, or (4) assessing imputation quality. Does NOT cover normalization or batch correction.
17stars - normalizing-proteomics-dataMannLabs/proteomics-agent-skills
Evaluate the need for and perform normalization of protein-level proteomics intensity data. Use when (1) assessing whether normalization is needed, (2) selecting normalization methods, (3) applying it. Does NOT cover batch correction or imputation
17stars - performing-statistical-analysisMannLabs/proteomics-agent-skills
Perform differential expression analysis on preprocessed proteomics data. Use this when performing finding testing (t-tests, ANOVA) to identify regulated proteins, and multiple testing correction (FDR). Generates standard visualizations like Volcano plots.
17stars - using-proteomics-skillsMannLabs/proteomics-agent-skills
Meta-skill for discovering, orchestrating, and sequencing proteomics analysis skills (reading, QC, preprocessing, statistics, interpretation, findings). Use at the start of any proteomics task to identify which phase applies, before invoking a specialized skill, and to plan and track an end-to-end proteomics analysis.
17stars
proteomics plugin questions
How do I install the proteomics plugin?
In Claude Code run /plugin marketplace add MannLabs/proteomics-agent-skills, then /plugin install proteomics@proteomics-agent-skills.
What does the proteomics plugin include?
proteomics bundles 11 skills.
How do I update or remove the proteomics plugin?
Update it with claude plugin update proteomics@proteomics-agent-skills in your shell, or from the Installed tab of /plugin. Remove it with /plugin uninstall proteomics@proteomics-agent-skills.
What license is the proteomics plugin under?
Apache-2.0, as stated in the plugin's manifest.