[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DAY4V3B8CGCM2QTPMSYV36":3,"skill-same:01M6DAY4V3B8CGCM2QTPMSYV36":66},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":14,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":41,"install":42,"resources":55},"01M6DAY4V3B8CGCM2QTPMSYV36","tooluniverse-structural-proteomics",null,"Integrate structural biology data with proteomics for drug target validation. Retrieves protein structures from PDB (RCSB, PDBe), AlphaFold predictions, antibody structures (SAbDab), GPCR data (GPCRdb), binding pocket analysis (ProteinsPlus), and ligand interactions (BindingDB). Use when asked to find structures for a drug target, identify binding site ligands, cross-validate drug binding with structural data, assess structural druggability, or compare experimental vs predicted structures.","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-structural-proteomics\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30],"proteomics","structural","tooluniverse structural proteomics","validate","validation","data","analysis","site","find","use","integrate","structural biology","2026-11-10 10:12:30",false,"---\nname: tooluniverse-structural-proteomics\ndescription: Integrate structural biology data with proteomics for drug target validation. Retrieves protein structures from PDB (RCSB, PDBe), AlphaFold predictions, antibody structures (SAbDab), GPCR data (GPCRdb), binding pocket analysis (ProteinsPlus), and ligand interactions (BindingDB). Use when asked to find structures for a drug target, identify binding site ligands, cross-validate drug binding with structural data, assess structural druggability, or compare experimental vs predicted structures.\n---\n\n# Structural Proteomics for Drug Target Validation\n\nComprehensive structural data integration using ToolUniverse tools across PDB, AlphaFold, GPCRdb, SAbDab, and proteomics databases for drug target validation.\n\n## LOOK UP DON'T GUESS\n\n- PDB structures\u002Fresolutions: `PDBeSIFTS_get_best_structures` and `RCSBGraphQL_get_structure_summary`\n- AlphaFold confidence: `alphafold_get_summary`\n- Ligands\u002Faffinities: `PDBe_get_structure_ligands` and `BindingDB_get_ligands_by_uniprot`\n- Druggability: `ProteinsPlus_predict_binding_sites`\n\n## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n## Domain Reasoning\n\nResolution determines valid conclusions: \u003C2A = atom positions visible; 2-3A = side chains reliable, drug design supported; >3A = backbone only, binding site unreliable. Do not over-interpret low-resolution structures.\n\n---\n\n## Tool Inventory\n\n### PDB (RCSB)\n`RCSBAdvSearch_search_structures` (query_type, query_value, rows), `RCSBData_get_entry` (entry_id), `RCSBGraphQL_get_structure_summary` (pdb_id), `RCSBGraphQL_get_ligand_info` (pdb_id), `RCSB_get_chemical_component` (comp_id)\n\n### PDB (PDBe)\n`pdbe_get_entry_summary` (pdb_id), `PDBe_get_structure_ligands` (pdb_id), `PDBe_get_bound_molecules` (pdb_id), `PDBeSearch_search_structures` (query, rows), `PDBeSIFTS_get_best_structures` (uniprot_id), `PDBeSIFTS_get_all_structures` (uniprot_id), `PDBe_KB_get_ligand_sites` (pdb_id), `PDBe_KB_get_interface_residues` (pdb_id), `PDBeValidation_get_quality_scores` (pdb_id)\n\n### PDBe PISA\n`PDBePISA_get_interfaces` (pdb_id), `PDBePISA_get_assemblies` (pdb_id)\n\n### AlphaFold\n`alphafold_get_prediction` (qualifier=UniProt), `alphafold_get_summary` (qualifier), `alphafold_get_annotations` (qualifier)\n\n### Binding Sites\n`ProteinsPlus_predict_binding_sites` (pdb_id, chain), `BindingDB_get_ligands_by_uniprot` (uniprot_id), `BindingDB_get_ligands_by_pdb` (pdb_id), `BindingDB_get_targets_by_compound` (smiles)\n\n### Foldseek\n`Foldseek_search_structure` (sequence, mode=\"tmalign\"), `Foldseek_get_result` (ticket)\n\n### GPCRdb\n`GPCRdb_get_protein` (protein), `GPCRdb_get_structures` (protein), `GPCRdb_get_ligands` (protein), `GPCRdb_get_mutations` (protein). Accepts entry names, gene symbols (auto-converted to `{symbol.lower()}_human`), or UniProt accessions.\n\n### SAbDab\n`SAbDab_search_structures` (query\u002Fantigen), `SAbDab_get_structure` (pdb_id), `TheraSAbDab_search_therapeutics` (query), `TheraSAbDab_search_by_target` (target)\n\n### Domains\n`InterPro_get_protein_domains` (uniprot_id), `Pfam_get_protein_annotations` (uniprot_id), `UniProt_get_entry_by_accession` (accession)\n\n### Proteomics\n`ProteomeXchange_search_datasets` (query), `ProteomeXchange_get_dataset` (dataset_id)\n\n---\n\n## Workflow 1: Find All Structures for a Drug Target\n\n```\nPhase 0: Resolve protein → UniProt ID, gene symbol, organism\nPhase 1: PDBeSIFTS_get_best_structures → RCSBGraphQL_get_structure_summary → PDBeValidation\nPhase 2: alphafold_get_prediction\u002Fsummary → compare pLDDT with experimental coverage\nPhase 3: IF GPCR → GPCRdb; IF antibody target → SAbDab\u002FTheraSAbDab\nPhase 4: InterPro\u002FPfam domain mapping → identify unresolved regions\nPhase 5: Summary table (PDB ID, method, resolution, ligands, coverage, quality)\n```\n\n**Decisions**: Resolution \u003C2.5A for drug design. X-ray > Cryo-EM > NMR > AlphaFold for binding sites. Holo > apo structures.\n\n## Workflow 2: Identify Binding Pocket Ligands\n\n```\nPhase 1: PDBe_get_structure_ligands + RCSBGraphQL_get_ligand_info + PDBe_KB_get_ligand_sites\nPhase 2: ProteinsPlus_predict_binding_sites → druggability score, pocket residues\nPhase 3: BindingDB_get_ligands_by_pdb\u002Funiprot → Ki, Kd, IC50\nPhase 4: RCSB_get_chemical_component for key ligands\n```\n\n**Filter artifacts**: GOL, EDO, SO4, PEG, ACT, CL, NA. Keep cofactors (ATP, NAD, HEM) and catalytic metals (ZN, MG) if relevant.\n\n## Workflow 3: Cross-Validate Drug Binding\n\n```\nPhase 1: Find co-crystal structures → filter for drug\u002Fanalogs\nPhase 2: BindingDB affinity data (Ki, Kd, IC50)\nPhase 3: ProteinsPlus + PDBe-KB binding site characterization\nPhase 4: PDBeValidation quality → binding site well-resolved?\nPhase 5: AlphaFold + Foldseek structural comparison\nPhase 6: GPCR-specific (if applicable) → active\u002Finactive states, pharmacology, resistance mutations\nPhase 7: Antibody-specific (if applicable) → epitope mapping\nPhase 8: Evidence integration\n```\n\n---\n\n## Tool Parameter Gotchas\n\n| Tool | Mistake | Correct |\n|------|---------|---------|\n| `alphafold_get_prediction\u002Fsummary` | `uniprot_id` | `qualifier` |\n| `GPCRdb_get_protein` | `gene_name` | `protein` |\n| `PDBeSIFTS_get_best_structures` | gene symbol | `uniprot_id` (e.g., \"P04637\") |\n| `Foldseek_search_structure` | `mode=\"3diaa\"` | `mode=\"tmalign\"` |\n| `SAbDab_search_structures` | `name` | `query` or `antigen` |\n| `RCSB_get_chemical_component` | `ligand_id` | `comp_id` |\n\n---\n\n## Evidence Grading\n\n| Tier | Confidence |\n|------|------------|\n| T1 | Co-crystal (\u003C2.5A) + binding affinity data |\n| T2 | Experimental structure + computational prediction |\n| T3 | AlphaFold + pocket analysis + known ligand analogs |\n| T4 | Homology model or low-resolution only |\n\n## Interpretation\n\n| Metric | High | Acceptable | Caution |\n|--------|------|-----------|---------|\n| Resolution | \u003C2.0A (X-ray) \u002F \u003C3.0A (cryo-EM) | 2.0-2.5A \u002F 3.0-4.0A | >3.0A \u002F >4.5A |\n| R-free | \u003C0.25 | 0.25-0.30 | >0.30 |\n| AlphaFold pLDDT | >90 | 70-90 | \u003C70 (disordered) |\n\nDoGSiteScorer >0.6 = druggable; \u003C0.4 = unlikely druggable. PISA assemblies should be cross-validated with SEC-MALS\u002Fnative MS.\n\n## Limitations\n\n- BindingDB: 60s+ for popular targets\n- AlphaFold: lacks ligand context\n- GPCRdb: Class A-F GPCRs only\n- PDBePISA: `operation` is internal, not a public parameter\n","# Structural Proteomics for Drug Target Validation\n\nComprehensive structural data integration using ToolUniverse tools across PDB, AlphaFold, GPCRdb, SAbDab, and proteomics databases for drug target validation.\n\n## LOOK UP DON'T GUESS\n\n- PDB structures\u002Fresolutions: `PDBeSIFTS_get_best_structures` and `RCSBGraphQL_get_structure_summary`\n- AlphaFold confidence: `alphafold_get_summary`\n- Ligands\u002Faffinities: `PDBe_get_structure_ligands` and `BindingDB_get_ligands_by_uniprot`\n- Druggability: `ProteinsPlus_predict_binding_sites`\n\n## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n## Domain Reasoning\n\nResolution determines valid conclusions: \u003C2A = atom positions visible; 2-3A = side chains reliable, drug design supported; >3A = backbone only, binding site unreliable. Do not over-interpret low-resolution structures.\n\n---\n\n## Tool Inventory\n\n### PDB (RCSB)\n`RCSBAdvSearch_search_structures` (query_type, query_value, rows), `RCSBData_get_entry` (entry_id), `RCSBGraphQL_get_structure_summary` (pdb_id), `RCSBGraphQL_get_ligand_info` (pdb_id), `RCSB_get_chemical_component` (comp_id)\n\n### PDB (PDBe)\n`pdbe_get_entry_summary` (pdb_id), `PDBe_get_structure_ligands` (pdb_id), `PDBe_get_bound_molecules` (pdb_id), `PDBeSearch_search_structures` (query, rows), `PDBeSIFTS_get_best_structures` (uniprot_id), `PDBeSIFTS_get_all_structures` (uniprot_id), `PDBe_KB_get_ligand_sites` (pdb_id), `PDBe_KB_get_interface_residues` (pdb_id), `PDBeValidation_get_quality_scores` (pdb_id)\n\n### PDBe PISA\n`PDBePISA_get_interfaces` (pdb_id), `PDBePISA_get_assemblies` (pdb_id)\n\n### AlphaFold\n`alphafold_get_prediction` (qualifier=UniProt), `alphafold_get_summary` (qualifier), `alphafold_get_annotations` (qualifier)\n\n### Binding Sites\n`ProteinsPlus_predict_binding_sites` (pdb_id, chain), `BindingDB_get_ligands_by_uniprot` (uniprot_id), `BindingDB_get_ligands_by_pdb` (pdb_id), `BindingDB_get_targets_by_compound` (smiles)\n\n### Foldseek\n`Foldseek_search_structure` (sequence, mode=\"tmalign\"), `Foldseek_get_result` (ticket)\n\n### GPCRdb\n`GPCRdb_get_protein` (protein), `GPCRdb_get_structures` (protein), `GPCRdb_get_ligands` (protein), `GPCRdb_get_mutations` (protein). Accepts entry names, gene symbols (auto-converted to `{symbol.lower()}_human`), or UniProt accessions.\n\n### SAbDab\n`SAbDab_search_structures` (query\u002Fantigen), `SAbDab_get_structure` (pdb_id), `TheraSAbDab_search_therapeutics` (query), `TheraSAbDab_search_by_target` (target)\n\n### Domains\n`InterPro_get_protein_domains` (uniprot_id), `Pfam_get_protein_annotations` (uniprot_id), `UniProt_get_entry_by_accession` (accession)\n\n### Proteomics\n`ProteomeXchange_search_datasets` (query), `ProteomeXchange_get_dataset` (dataset_id)\n\n---\n\n## Workflow 1: Find All Structures for a Drug Target\n\n```\nPhase 0: Resolve protein → UniProt ID, gene symbol, organism\nPhase 1: PDBeSIFTS_get_best_structures → RCSBGraphQL_get_structure_summary → PDBeValidation\nPhase 2: alphafold_get_prediction\u002Fsummary → compare pLDDT with experimental coverage\nPhase 3: IF GPCR → GPCRdb; IF antibody target → SAbDab\u002FTheraSAbDab\nPhase 4: InterPro\u002FPfam domain mapping → identify unresolved regions\nPhase 5: Summary table (PDB ID, method, resolution, ligands, coverage, quality)\n```\n\n**Decisions**: Resolution \u003C2.5A for drug design. X-ray > Cryo-EM > NMR > AlphaFold for binding sites. Holo > apo structures.\n\n## Workflow 2: Identify Binding Pocket Ligands\n\n```\nPhase 1: PDBe_get_structure_ligands + RCSBGraphQL_get_ligand_info + PDBe_KB_get_ligand_sites\nPhase 2: ProteinsPlus_predict_binding_sites → druggability score, pocket residues\nPhase 3: BindingDB_get_ligands_by_pdb\u002Funiprot → Ki, Kd, IC50\nPhase 4: RCSB_get_chemical_component for key ligands\n```\n\n**Filter artifacts**: GOL, EDO, SO4, PEG, ACT, CL, NA. Keep cofactors (ATP, NAD, HEM) and catalytic metals (ZN, MG) if relevant.\n\n## Workflow 3: Cross-Validate Drug Binding\n\n```\nPhase 1: Find co-crystal structures → filter for drug\u002Fanalogs\nPhase 2: BindingDB affinity data (Ki, Kd, IC50)\nPhase 3: ProteinsPlus + PDBe-KB binding site characterization\nPhase 4: PDBeValidation quality → binding site well-resolved?\nPhase 5: AlphaFold + Foldseek structural comparison\nPhase 6: GPCR-specific (if applicable) → active\u002Finactive states, pharmacology, resistance mutations\nPhase 7: Antibody-specific (if applicable) → epitope mapping\nPhase 8: Evidence integration\n```\n\n---\n\n## Tool Parameter Gotchas\n\n| Tool | Mistake | Correct |\n|------|---------|---------|\n| `alphafold_get_prediction\u002Fsummary` | `uniprot_id` | `qualifier` |\n| `GPCRdb_get_protein` | `gene_name` | `protein` |\n| `PDBeSIFTS_get_best_structures` | gene symbol | `uniprot_id` (e.g., \"P04637\") |\n| `Foldseek_search_structure` | `mode=\"3diaa\"` | `mode=\"tmalign\"` |\n| `SAbDab_search_structures` | `name` | `query` or `antigen` |\n| `RCSB_get_chemical_component` | `ligand_id` | `comp_id` |\n\n---\n\n## Evidence Grading\n\n| Tier | Confidence |\n|------|------------|\n| T1 | Co-crystal (\u003C2.5A) + binding affinity data |\n| T2 | Experimental structure + computational prediction |\n| T3 | AlphaFold + pocket analysis + known ligand analogs |\n| T4 | Homology model or low-resolution only |\n\n## Interpretation\n\n| Metric | High | Acceptable | Caution |\n|--------|------|-----------|---------|\n| Resolution | \u003C2.0A (X-ray) \u002F \u003C3.0A (cryo-EM) | 2.0-2.5A \u002F 3.0-4.0A | >3.0A \u002F >4.5A |\n| R-free | \u003C0.25 | 0.25-0.30 | >0.30 |\n| AlphaFold pLDDT | >90 | 70-90 | \u003C70 (disordered) |\n\nDoGSiteScorer >0.6 = druggable; \u003C0.4 = unlikely druggable. PISA assemblies should be cross-validated with SEC-MALS\u002Fnative MS.\n\n## Limitations\n\n- BindingDB: 60s+ for popular targets\n- AlphaFold: lacks ligand context\n- GPCRdb: Class A-F GPCRs only\n- PDBePISA: `operation` is internal, not a public parameter\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-structural-proteomics","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-structural-proteomics",{"tool":48,"command":49},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-structural-proteomics",{"tool":51,"command":52},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-structural-proteomics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-structural-proteomics\u002FSKILL.md",true,[56,62],{"path":57,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":60,"sourceHtmlUrl":61},"resources\u002Fimplementation-playbook.md","REFERENCE","参考文档",8520,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Ftemporal-golang-pro\u002Fresources\u002Fimplementation-playbook.md",{"path":63,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":64,"sourceHtmlUrl":65},"resources\u002Ftesting-strategies.md",4306,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Ftemporal-golang-pro\u002Fresources\u002Ftesting-strategies.md",[67,88,119,146,160,176],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":73,"publishedAt":31},"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",[],[74,75,76,77,78,79,80,81,82,28,83,84,85,86,87],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":99,"publishedAt":31},"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",[94],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[100,101,102,103,104,105,106,107,108,109,110,111,112,113,28,114,115,116,117,118],"openmm md","how","decision","pick","linux","analyze","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":31},"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":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":129,"categoryName":130,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000008","测试与质量",[132,133,134,135,136,137,138,139,140,141,101,142,143,144,145],"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":31},"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":31},"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":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},[162,168,79,169,170,171,28,172,173,174,175],"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":31},"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"]