proteomics 플러그인
Turn search-engine output tables into high-confidence biological insights: reading, QC, normalization, imputation, batch correction, differential expression, and biological interpretation.
proteomics 플러그인 설치 방법
두 명령을 모두 Claude Code 안에서 실행하십시오. 첫 번째는 마켓플레이스를 한 번 추가하고, 두 번째는 플러그인을 설치합니다.
/plugin marketplace add MannLabs/proteomics-agent-skills
/plugin install proteomics@proteomics-agent-skillsproteomics 구성 요소
플러그인 파일에서 읽어온 11개의 항목입니다.
Skills 11
작업이 일치하면 Claude가 로드합니다- 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.
Claude Code에서 proteomics 사용하기
Skills는 명령어가 필요하지 않습니다. 요청이 설명과 일치하면 Claude가 이를 읽습니다.
업데이트, 비활성화 또는 삭제
- 플러그인 업데이트 (shell)
- claude plugin update proteomics@proteomics-agent-skills
- marketplace 새로고침
- /plugin marketplace update proteomics-agent-skills
- 일시적으로 비활성화
- /plugin disable proteomics@proteomics-agent-skills
- 삭제
- /plugin uninstall proteomics@proteomics-agent-skills
proteomics의 스킬
- 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.
4개 파일17스타 - 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.
3개 파일17스타 - 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.
2개 파일17스타 - 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.
3개 파일17스타 - 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.
17스타 - 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.
17스타 - 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.
17스타 - 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.
17스타 - 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
17스타 - 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.
17스타 - 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.
17스타
proteomics 플러그인 질문
proteomics 플러그인은 어떻게 설치하나요?
Claude Code에서 /plugin marketplace add MannLabs/proteomics-agent-skills를 실행한 다음 /plugin install proteomics@proteomics-agent-skills을 실행하십시오.
proteomics 플러그인에는 무엇이 포함되나요?
proteomics은(는) 11개 스킬을(를) 묶습니다.
proteomics 플러그인을 어떻게 업데이트하거나 삭제하나요?
shell에서 claude plugin update proteomics@proteomics-agent-skills를 사용하여 업데이트하거나 /plugin의 Installed 탭에서 수행하세요. 삭제하려면 /plugin uninstall proteomics@proteomics-agent-skills을 사용하세요.
proteomics 플러그인의 라이선스는 무엇인가요?
플러그인 manifest에 명시된 대로 Apache-2.0입니다.