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Analyze TCR\u002FBCR repertoires to assess clonality, diversity, V(D)J gene usage, CDR3 characteristics, convergence, and predict epitope specificity. Integrate with single-cell data for clonotype-phenotype associations. Use for adaptive immune response profiling, cancer immunotherapy research, vaccine response assessment, autoimmune disease studies, or repertoire diversity analysis in immunology research.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-immune-repertoire-analysis\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31],"analysis","Immune","tooluniverse immune repertoire analysis","research","assessment","data","analyze","profiling","use","integrate","diversity","immune response","cell","2026-11-10 10:12:30",false,"---\nname: tooluniverse-immune-repertoire-analysis\ndescription: Comprehensive immune repertoire analysis for T-cell and B-cell receptor sequencing data. Analyze TCR\u002FBCR repertoires to assess clonality, diversity, V(D)J gene usage, CDR3 characteristics, convergence, and predict epitope specificity. Integrate with single-cell data for clonotype-phenotype associations. Use for adaptive immune response profiling, cancer immunotherapy research, vaccine response assessment, autoimmune disease studies, or repertoire diversity analysis in immunology research.\n---\n\n# ToolUniverse Immune Repertoire Analysis\n\nComprehensive skill for analyzing T-cell receptor (TCR) and B-cell receptor (BCR) repertoire sequencing data to characterize adaptive immune responses, clonal expansion, and antigen specificity.\n\n## Domain Reasoning\n\nRepertoire diversity reflects immune history. High clonality — a few clones dominating — indicates antigen-driven expansion, as seen in active infection, tumor-infiltrating lymphocytes, or chronic stimulation. Low diversity points to immunodeficiency or treatment-induced lymphopenia. Always compare observed metrics against healthy donor reference distributions before drawing conclusions; a Shannon entropy of 7 is unremarkable in a healthy adult but alarming post-chemotherapy.\n\n## LOOK UP DON'T GUESS\n\n- Clonotype frequency thresholds, CDR3 length ranges, and convergence ratios: query IEDB or VDJdb; do not assume values from memory.\n- Epitope specificities for expanded clones: search `iedb_search_tcell_assays` and `BVBRC_search_epitopes`; never infer antigen identity from CDR3 alone.\n- V gene family usage biases in healthy donors: retrieve published reference data or query ImmPort; do not assume baseline distributions are uniform.\n- Sequencing depth adequacy: compute rarefaction curves from the actual data; do not guess whether depth is sufficient.\n\n---\n\n## Overview\n\nAdaptive immune receptor repertoire sequencing (AIRR-seq) enables comprehensive profiling of T-cell and B-cell populations through high-throughput sequencing of TCR and BCR variable regions. This skill provides an 8-phase workflow for:\n- Clonotype identification and tracking\n- Diversity and clonality assessment\n- V(D)J gene usage analysis\n- CDR3 sequence characterization\n- Clonal expansion and convergence detection\n- Epitope specificity prediction\n- Integration with single-cell phenotyping\n- Longitudinal repertoire tracking\n\n---\n\n## Core Workflow\n\n### Phase 1: Data Import & Clonotype Definition\n\nLoad AIRR-seq data from common formats (MiXCR, ImmunoSEQ, AIRR standard, 10x Genomics VDJ). Standardize columns to: `cloneId`, `count`, `frequency`, `cdr3aa`, `cdr3nt`, `v_gene`, `j_gene`, `chain`. Define clonotypes using one of three methods:\n- **cdr3aa**: Amino acid CDR3 sequence only\n- **cdr3nt**: Nucleotide CDR3 sequence\n- **vj_cdr3**: V gene + J gene + CDR3aa (most common, recommended)\n\nAggregate by clonotype, sort by count, assign ranks.\n\n### Phase 2: Diversity & Clonality Analysis\n\nCalculate diversity metrics for the repertoire:\n- **Shannon entropy**: Overall diversity (higher = more diverse)\n- **Simpson index**: Probability two random clones are same\n- **Inverse Simpson**: Effective number of clonotypes\n- **Gini coefficient**: Inequality in clonotype distribution\n- **Clonality**: 1 - Pielou's evenness (higher = more clonal)\n- **Richness**: Number of unique clonotypes\n\nGenerate rarefaction curves to assess whether sequencing depth is sufficient.\n\n### Phase 3: V(D)J Gene Usage Analysis\n\nAnalyze V and J gene usage patterns weighted by clonotype count:\n- V gene family usage frequencies\n- J gene family usage frequencies\n- V-J pairing frequencies\n- Statistical testing for biased usage (chi-square test vs. uniform expectation)\n\n### Phase 4: CDR3 Sequence Analysis\n\nCharacterize CDR3 sequences:\n- **Length distribution**: Typical TCR CDR3 = 12-18 aa; BCR CDR3 = 10-20 aa\n- **Amino acid composition**: Weighted by clonotype frequency\n- Flag unusual length distributions (may indicate PCR bias)\n\n### Phase 5: Clonal Expansion Detection\n\nIdentify expanded clonotypes above a frequency threshold (default: 95th percentile). Track clonotypes longitudinally across multiple timepoints to measure persistence, mean\u002Fmax frequency, and fold changes.\n\n### Phase 6: Convergence & Public Clonotypes\n\n- **Convergent recombination**: Same CDR3 amino acid from different nucleotide sequences (evidence of antigen-driven selection)\n- **Public clonotypes**: Shared across multiple samples\u002Findividuals (may indicate common antigen responses)\n\n### Phase 7: Epitope Prediction & Specificity\n\nQuery epitope databases for known TCR-epitope associations:\n- **IEDB** (`iedb_search_tcell_assays`): Search T-cell assay records by sequence or MHC class; use `iedb_search_epitopes` with `sequence_contains` for motif search\n- **BVBRC** (`BVBRC_search_epitopes`): Best for organism-based epitope discovery (e.g., `taxon_id=\"2697049\"` for SARS-CoV-2); returns epitope sequences with T-cell\u002FB-cell assay counts\n- **VDJdb** (manual): https:\u002F\u002Fvdjdb.cdr3.net\u002Fsearch\n- **PubMed literature** (`PubMed_search_articles`): Search for CDR3 + epitope\u002Fantigen\u002Fspecificity\n- **IEDB detail tools**: `iedb_get_epitope_antigens` (link epitope→antigen), `iedb_get_epitope_mhc` (MHC restriction)\n\n### Phase 8: Integration with Single-Cell Data\n\nLink TCR\u002FBCR clonotypes to cell phenotypes from paired single-cell RNA-seq:\n- Map clonotypes to cell barcodes\n- Identify expanded clonotype phenotypes on UMAP\n- Analyze clonotype-cluster associations (cross-tabulation)\n- Find cluster-specific clonotypes (>80% cells in one cluster)\n- Differential gene expression: expanded vs. non-expanded cells\n\n---\n\n## ToolUniverse Tool Integration\n\n**Key Tools Used**:\n- `iedb_search_tcell_assays` - T-cell assay records (sequence, MHC class filters)\n- `iedb_search_bcell` - B-cell assay records\n- `iedb_search_epitopes` - Epitope motif search via `sequence_contains`\n- `BVBRC_search_epitopes` - Organism-based epitope discovery (best for pathogen-specific queries)\n- `NCBI_SRA_search_runs` - Find public TCR\u002FBCR-seq datasets (use strategy=\"AMPLICON\")\n- `ImmPort_search_studies` - NIAID immunology studies (vaccine trials, flow cytometry)\n- `PubMed_search_articles` - Literature on TCR\u002FBCR specificity\n- `UniProt_get_entry_by_accession` - Antigen protein information\n\n**Integration with Other Skills**:\n- `tooluniverse-single-cell` - Single-cell transcriptomics\n- `tooluniverse-rnaseq-deseq2` - Bulk RNA-seq analysis\n- `tooluniverse-variant-analysis` - Somatic hypermutation analysis (BCR)\n\n---\n\n## Quick Start\n\n```python\nfrom tooluniverse import ToolUniverse\n\n# 1. Load data\ntcr_data = load_airr_data(\"clonotypes.txt\", format='mixcr')\n\n# 2. Define clonotypes\nclonotypes = define_clonotypes(tcr_data, method='vj_cdr3')\n\n# 3. Calculate diversity\ndiversity = calculate_diversity(clonotypes['count'])\nprint(f\"Shannon entropy: {diversity['shannon_entropy']:.2f}\")\n\n# 4. Detect expanded clones\nexpansion = detect_expanded_clones(clonotypes)\nprint(f\"Expanded clonotypes: {expansion['n_expanded']}\")\n\n# 5. Analyze V(D)J usage\nvdj_usage = analyze_vdj_usage(tcr_data)\n\n# 6. Query epitope databases\ntop_clones = expansion['expanded_clonotypes']['clonotype'].head(10)\nepitopes = query_epitope_database(top_clones)\n```\n\n---\n\n## Reasoning Framework for Result Interpretation\n\n### Evidence Grading\n\n| Grade | Criteria | Example |\n|-------|----------|---------|\n| **Strong** | Clonal expansion > 1% frequency, convergent recombination confirmed, epitope match in IEDB\u002FVDJdb | CDR3 at 5% frequency with 3 nucleotide variants encoding same amino acid, IEDB hit |\n| **Moderate** | Expanded clone (0.1-1%), V(D)J bias significant (chi-sq p \u003C 0.01), partial epitope match | Clone at 0.5% with TRBV20-1 bias, similar CDR3 motif in VDJdb |\n| **Weak** | Low-frequency expansion (0.01-0.1%), single timepoint only, no epitope database match | Moderately expanded clone without convergence or known specificity |\n| **Insufficient** | Below detection threshold, sequencing depth \u003C 10,000 clonotypes, no replication | Singleton clonotypes that may be PCR\u002Fsequencing artifacts |\n\n### Interpretation Guidance\n\n- **Clonality metrics**: Shannon diversity measures overall repertoire complexity (higher = more diverse, typical range 5-12 for healthy blood). Gini coefficient ranges from 0 (perfectly even) to 1 (single dominant clone); values > 0.3 suggest clonal expansion. Clonality (1 - Pielou's evenness) > 0.2 indicates moderate clonal dominance; > 0.5 suggests strong oligoclonal expansion (common in active infection or tumor-infiltrating lymphocytes).\n- **V(D)J usage significance**: Biased V or J gene usage (chi-square p \u003C 0.01 vs expected uniform distribution) may indicate antigen-driven selection. However, baseline V gene usage is not uniform even in healthy repertoires due to genomic proximity and recombination efficiency. Compare against healthy donor reference distributions rather than uniform expectation when possible.\n- **CDR3 convergence meaning**: Convergent recombination (same CDR3 amino acid from different nucleotide sequences) is strong evidence of antigen-driven selection because independent recombination events converged on the same receptor. Public clonotypes (shared across individuals) further strengthen this inference. A convergence ratio > 2 (nucleotide variants per amino acid sequence) for expanded clones is noteworthy.\n- **Sequencing depth**: Rarefaction curves that plateau indicate sufficient depth. If the curve is still rising, richness and diversity estimates are underestimates. Minimum recommended depth: 50,000-100,000 total reads for bulk TCR-seq.\n- **Longitudinal tracking**: Persistent clones across timepoints with stable or increasing frequency indicate antigen-driven maintenance. Transient expansions that disappear may reflect acute responses.\n\n### Synthesis Questions\n\n1. Does the observed clonal expansion pattern (Gini coefficient, top-clone frequency) match the expected immune context (e.g., post-vaccination expansion, tumor-infiltrating lymphocyte oligoclonality)?\n2. Are convergent CDR3 sequences found across multiple individuals in the cohort, suggesting a public response to a shared antigen?\n3. Do expanded clonotypes show biased V gene usage consistent with known antigen-specific repertoire features (e.g., TRBV20-1 enrichment in CMV-specific responses)?\n4. Is the sequencing depth sufficient (rarefaction plateau reached) to reliably estimate diversity metrics and detect low-frequency expanded clones?\n5. For longitudinal data, do clonal dynamics (expansion, contraction, persistence) correlate with clinical outcomes or treatment response?\n\n---\n\n## References\n\n- Dash P, et al. (2017) Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature\n- Glanville J, et al. (2017) Identifying specificity groups in the T cell receptor repertoire. Nature\n- Stubbington MJT, et al. (2016) T cell fate and clonality inference from single-cell transcriptomes. Nature Methods\n- Vander Heiden JA, et al. (2014) pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics\n\n---\n\n## See Also\n\n- `ANALYSIS_DETAILS.md` - Detailed code snippets for all 8 phases\n- `USE_CASES.md` - Complete use cases (immunotherapy, vaccine, autoimmune, single-cell integration) and best practices\n","# ToolUniverse Immune Repertoire Analysis\n\nComprehensive skill for analyzing T-cell receptor (TCR) and B-cell receptor (BCR) repertoire sequencing data to characterize adaptive immune responses, clonal expansion, and antigen specificity.\n\n## Domain Reasoning\n\nRepertoire diversity reflects immune history. High clonality — a few clones dominating — indicates antigen-driven expansion, as seen in active infection, tumor-infiltrating lymphocytes, or chronic stimulation. Low diversity points to immunodeficiency or treatment-induced lymphopenia. Always compare observed metrics against healthy donor reference distributions before drawing conclusions; a Shannon entropy of 7 is unremarkable in a healthy adult but alarming post-chemotherapy.\n\n## LOOK UP DON'T GUESS\n\n- Clonotype frequency thresholds, CDR3 length ranges, and convergence ratios: query IEDB or VDJdb; do not assume values from memory.\n- Epitope specificities for expanded clones: search `iedb_search_tcell_assays` and `BVBRC_search_epitopes`; never infer antigen identity from CDR3 alone.\n- V gene family usage biases in healthy donors: retrieve published reference data or query ImmPort; do not assume baseline distributions are uniform.\n- Sequencing depth adequacy: compute rarefaction curves from the actual data; do not guess whether depth is sufficient.\n\n---\n\n## Overview\n\nAdaptive immune receptor repertoire sequencing (AIRR-seq) enables comprehensive profiling of T-cell and B-cell populations through high-throughput sequencing of TCR and BCR variable regions. This skill provides an 8-phase workflow for:\n- Clonotype identification and tracking\n- Diversity and clonality assessment\n- V(D)J gene usage analysis\n- CDR3 sequence characterization\n- Clonal expansion and convergence detection\n- Epitope specificity prediction\n- Integration with single-cell phenotyping\n- Longitudinal repertoire tracking\n\n---\n\n## Core Workflow\n\n### Phase 1: Data Import & Clonotype Definition\n\nLoad AIRR-seq data from common formats (MiXCR, ImmunoSEQ, AIRR standard, 10x Genomics VDJ). Standardize columns to: `cloneId`, `count`, `frequency`, `cdr3aa`, `cdr3nt`, `v_gene`, `j_gene`, `chain`. Define clonotypes using one of three methods:\n- **cdr3aa**: Amino acid CDR3 sequence only\n- **cdr3nt**: Nucleotide CDR3 sequence\n- **vj_cdr3**: V gene + J gene + CDR3aa (most common, recommended)\n\nAggregate by clonotype, sort by count, assign ranks.\n\n### Phase 2: Diversity & Clonality Analysis\n\nCalculate diversity metrics for the repertoire:\n- **Shannon entropy**: Overall diversity (higher = more diverse)\n- **Simpson index**: Probability two random clones are same\n- **Inverse Simpson**: Effective number of clonotypes\n- **Gini coefficient**: Inequality in clonotype distribution\n- **Clonality**: 1 - Pielou's evenness (higher = more clonal)\n- **Richness**: Number of unique clonotypes\n\nGenerate rarefaction curves to assess whether sequencing depth is sufficient.\n\n### Phase 3: V(D)J Gene Usage Analysis\n\nAnalyze V and J gene usage patterns weighted by clonotype count:\n- V gene family usage frequencies\n- J gene family usage frequencies\n- V-J pairing frequencies\n- Statistical testing for biased usage (chi-square test vs. uniform expectation)\n\n### Phase 4: CDR3 Sequence Analysis\n\nCharacterize CDR3 sequences:\n- **Length distribution**: Typical TCR CDR3 = 12-18 aa; BCR CDR3 = 10-20 aa\n- **Amino acid composition**: Weighted by clonotype frequency\n- Flag unusual length distributions (may indicate PCR bias)\n\n### Phase 5: Clonal Expansion Detection\n\nIdentify expanded clonotypes above a frequency threshold (default: 95th percentile). Track clonotypes longitudinally across multiple timepoints to measure persistence, mean\u002Fmax frequency, and fold changes.\n\n### Phase 6: Convergence & Public Clonotypes\n\n- **Convergent recombination**: Same CDR3 amino acid from different nucleotide sequences (evidence of antigen-driven selection)\n- **Public clonotypes**: Shared across multiple samples\u002Findividuals (may indicate common antigen responses)\n\n### Phase 7: Epitope Prediction & Specificity\n\nQuery epitope databases for known TCR-epitope associations:\n- **IEDB** (`iedb_search_tcell_assays`): Search T-cell assay records by sequence or MHC class; use `iedb_search_epitopes` with `sequence_contains` for motif search\n- **BVBRC** (`BVBRC_search_epitopes`): Best for organism-based epitope discovery (e.g., `taxon_id=\"2697049\"` for SARS-CoV-2); returns epitope sequences with T-cell\u002FB-cell assay counts\n- **VDJdb** (manual): https:\u002F\u002Fvdjdb.cdr3.net\u002Fsearch\n- **PubMed literature** (`PubMed_search_articles`): Search for CDR3 + epitope\u002Fantigen\u002Fspecificity\n- **IEDB detail tools**: `iedb_get_epitope_antigens` (link epitope→antigen), `iedb_get_epitope_mhc` (MHC restriction)\n\n### Phase 8: Integration with Single-Cell Data\n\nLink TCR\u002FBCR clonotypes to cell phenotypes from paired single-cell RNA-seq:\n- Map clonotypes to cell barcodes\n- Identify expanded clonotype phenotypes on UMAP\n- Analyze clonotype-cluster associations (cross-tabulation)\n- Find cluster-specific clonotypes (>80% cells in one cluster)\n- Differential gene expression: expanded vs. non-expanded cells\n\n---\n\n## ToolUniverse Tool Integration\n\n**Key Tools Used**:\n- `iedb_search_tcell_assays` - T-cell assay records (sequence, MHC class filters)\n- `iedb_search_bcell` - B-cell assay records\n- `iedb_search_epitopes` - Epitope motif search via `sequence_contains`\n- `BVBRC_search_epitopes` - Organism-based epitope discovery (best for pathogen-specific queries)\n- `NCBI_SRA_search_runs` - Find public TCR\u002FBCR-seq datasets (use strategy=\"AMPLICON\")\n- `ImmPort_search_studies` - NIAID immunology studies (vaccine trials, flow cytometry)\n- `PubMed_search_articles` - Literature on TCR\u002FBCR specificity\n- `UniProt_get_entry_by_accession` - Antigen protein information\n\n**Integration with Other Skills**:\n- `tooluniverse-single-cell` - Single-cell transcriptomics\n- `tooluniverse-rnaseq-deseq2` - Bulk RNA-seq analysis\n- `tooluniverse-variant-analysis` - Somatic hypermutation analysis (BCR)\n\n---\n\n## Quick Start\n\n```python\nfrom tooluniverse import ToolUniverse\n\n# 1. Load data\ntcr_data = load_airr_data(\"clonotypes.txt\", format='mixcr')\n\n# 2. Define clonotypes\nclonotypes = define_clonotypes(tcr_data, method='vj_cdr3')\n\n# 3. Calculate diversity\ndiversity = calculate_diversity(clonotypes['count'])\nprint(f\"Shannon entropy: {diversity['shannon_entropy']:.2f}\")\n\n# 4. Detect expanded clones\nexpansion = detect_expanded_clones(clonotypes)\nprint(f\"Expanded clonotypes: {expansion['n_expanded']}\")\n\n# 5. Analyze V(D)J usage\nvdj_usage = analyze_vdj_usage(tcr_data)\n\n# 6. Query epitope databases\ntop_clones = expansion['expanded_clonotypes']['clonotype'].head(10)\nepitopes = query_epitope_database(top_clones)\n```\n\n---\n\n## Reasoning Framework for Result Interpretation\n\n### Evidence Grading\n\n| Grade | Criteria | Example |\n|-------|----------|---------|\n| **Strong** | Clonal expansion > 1% frequency, convergent recombination confirmed, epitope match in IEDB\u002FVDJdb | CDR3 at 5% frequency with 3 nucleotide variants encoding same amino acid, IEDB hit |\n| **Moderate** | Expanded clone (0.1-1%), V(D)J bias significant (chi-sq p \u003C 0.01), partial epitope match | Clone at 0.5% with TRBV20-1 bias, similar CDR3 motif in VDJdb |\n| **Weak** | Low-frequency expansion (0.01-0.1%), single timepoint only, no epitope database match | Moderately expanded clone without convergence or known specificity |\n| **Insufficient** | Below detection threshold, sequencing depth \u003C 10,000 clonotypes, no replication | Singleton clonotypes that may be PCR\u002Fsequencing artifacts |\n\n### Interpretation Guidance\n\n- **Clonality metrics**: Shannon diversity measures overall repertoire complexity (higher = more diverse, typical range 5-12 for healthy blood). Gini coefficient ranges from 0 (perfectly even) to 1 (single dominant clone); values > 0.3 suggest clonal expansion. Clonality (1 - Pielou's evenness) > 0.2 indicates moderate clonal dominance; > 0.5 suggests strong oligoclonal expansion (common in active infection or tumor-infiltrating lymphocytes).\n- **V(D)J usage significance**: Biased V or J gene usage (chi-square p \u003C 0.01 vs expected uniform distribution) may indicate antigen-driven selection. However, baseline V gene usage is not uniform even in healthy repertoires due to genomic proximity and recombination efficiency. Compare against healthy donor reference distributions rather than uniform expectation when possible.\n- **CDR3 convergence meaning**: Convergent recombination (same CDR3 amino acid from different nucleotide sequences) is strong evidence of antigen-driven selection because independent recombination events converged on the same receptor. Public clonotypes (shared across individuals) further strengthen this inference. A convergence ratio > 2 (nucleotide variants per amino acid sequence) for expanded clones is noteworthy.\n- **Sequencing depth**: Rarefaction curves that plateau indicate sufficient depth. If the curve is still rising, richness and diversity estimates are underestimates. Minimum recommended depth: 50,000-100,000 total reads for bulk TCR-seq.\n- **Longitudinal tracking**: Persistent clones across timepoints with stable or increasing frequency indicate antigen-driven maintenance. Transient expansions that disappear may reflect acute responses.\n\n### Synthesis Questions\n\n1. Does the observed clonal expansion pattern (Gini coefficient, top-clone frequency) match the expected immune context (e.g., post-vaccination expansion, tumor-infiltrating lymphocyte oligoclonality)?\n2. Are convergent CDR3 sequences found across multiple individuals in the cohort, suggesting a public response to a shared antigen?\n3. Do expanded clonotypes show biased V gene usage consistent with known antigen-specific repertoire features (e.g., TRBV20-1 enrichment in CMV-specific responses)?\n4. Is the sequencing depth sufficient (rarefaction plateau reached) to reliably estimate diversity metrics and detect low-frequency expanded clones?\n5. For longitudinal data, do clonal dynamics (expansion, contraction, persistence) correlate with clinical outcomes or treatment response?\n\n---\n\n## References\n\n- Dash P, et al. (2017) Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature\n- Glanville J, et al. (2017) Identifying specificity groups in the T cell receptor repertoire. Nature\n- Stubbington MJT, et al. (2016) T cell fate and clonality inference from single-cell transcriptomes. Nature Methods\n- Vander Heiden JA, et al. (2014) pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics\n\n---\n\n## See Also\n\n- `ANALYSIS_DETAILS.md` - Detailed code snippets for all 8 phases\n- `USE_CASES.md` - Complete use cases (immunotherapy, vaccine, autoimmune, single-cell integration) and best practices\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-immune-repertoire-analysis","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-immune-repertoire-analysis",{"tool":49,"command":50},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-immune-repertoire-analysis",{"tool":52,"command":53},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-immune-repertoire-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-immune-repertoire-analysis\u002FSKILL.md",true,[57,63],{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"ANALYSIS_DETAILS.md","REFERENCE","参考文档",15432,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-immune-repertoire-analysis\u002FANALYSIS_DETAILS.md",{"path":64,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":65,"sourceHtmlUrl":66},"USE_CASES.md",5521,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-immune-repertoire-analysis\u002FUSE_CASES.md",[68,89,119,146,160,176],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":32},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[75,76,77,78,79,80,81,82,83,27,84,85,86,87,88],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":100,"publishedAt":32},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[101,102,103,104,105,25,106,107,108,109,110,111,112,113,27,114,115,116,117,118],"openmm md","how","decision","pick","linux","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":131,"publishedAt":32},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[125,128],{"categoryNo":126,"categoryName":127,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":129,"categoryName":130,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000008","测试与质量",[132,133,134,135,136,137,138,139,140,141,102,142,143,144,145],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":32},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[148,153,154,155,156,157,158,159],"setup","report","project","installation","project setup","issues","state",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":167,"publishedAt":32},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[166],{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},[162,168,80,169,170,171,27,172,173,174,175],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":177,"name":153,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":32},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[153,155,116,182,157,183],"files","interactive"]