Плагин proteomics
Turn search-engine output tables into high-confidence biological insights: reading, QC, normalization, imputation, batch correction, differential expression, and biological interpretation.
Как установить плагин proteomics
Выполните обе команды в Claude Code. Первая один раз добавляет маркетплейс; вторая устанавливает плагин.
/plugin marketplace add MannLabs/proteomics-agent-skills
/plugin install proteomics@proteomics-agent-skillsЧто внутри proteomics
11 частей прочитано из файлов самого плагина.
Skills 11
Claude загружает это при соответствии задачи- 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.
Использование proteomics в Claude Code
Skills не требуют команд. Claude считывает их, когда ваш запрос соответствует их описанию.
Обновить, выключить или удалить
- Обновить плагин (shell)
- claude plugin update proteomics@proteomics-agent-skills
- Обновить marketplace
- /plugin marketplace update proteomics-agent-skills
- Временно выключить
- /plugin disable proteomics@proteomics-agent-skills
- Удалить
- /plugin uninstall proteomics@proteomics-agent-skills
Навыки в 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 файла17звёзд - 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 файла17звёзд - 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 файла17звёзд - 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 файла17звёзд - 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.
17звёзд - 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.
17звёзд - 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.
17звёзд - 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.
17звёзд - 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
17звёзд - 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.
17звёзд - 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.
17звёзд
Вопросы о плагине proteomics
Как установить плагин proteomics?
В Claude Code выполните /plugin marketplace add MannLabs/proteomics-agent-skills, затем /plugin install proteomics@proteomics-agent-skills.
Что включает плагин proteomics?
proteomics объединяет 11 навыков.
Как обновить или удалить плагин proteomics?
Обновите его с помощью claude plugin update proteomics@proteomics-agent-skills в вашем shell или на вкладке Installed в /plugin. Удалите его с помощью /plugin uninstall proteomics@proteomics-agent-skills.
Под какой лицензией распространяется плагин proteomics?
Apache-2.0, как указано в манифесте плагина.