[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DASPXGD6WK168SFRKHPP93":3,"skill-same:01M6DASPXGD6WK168SFRKHPP93":69},{"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":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":14,"repositoryHtmlUrl":42,"repositoryHomepageUrl":6,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":44,"install":45,"resources":58},"01M6DASPXGD6WK168SFRKHPP93","tooluniverse-lipidomics",null,"Analyze lipids, lipid metabolism, and lipid-disease associations using LIPID MAPS, HMDB, PubChem, KEGG, and CTD. Covers lipid identification, classification, pathway mapping, biomarker discovery, and disease links. Distinct from general metabolomics — focuses on lipid-specific biology (membrane composition, signaling lipids, lipoproteins, sphingolipids, eicosanoids). Use when asked about lipid profiling, lipidomics data interpretation, lipid biomarkers, lipid metabolism disorders, or lipid-disease connections.","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-lipidomics\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"tooluniverse lipidomics","interpretation","data","classification","Composition","discovery","analyze","profiling","use","maps","general","connections","signaling","metabolism","lipids","2026-11-10 10:12:30",false,"---\nname: tooluniverse-lipidomics\ndescription: Analyze lipids, lipid metabolism, and lipid-disease associations using LIPID MAPS, HMDB, PubChem, KEGG, and CTD. Covers lipid identification, classification, pathway mapping, biomarker discovery, and disease links. Distinct from general metabolomics — focuses on lipid-specific biology (membrane composition, signaling lipids, lipoproteins, sphingolipids, eicosanoids). Use when asked about lipid profiling, lipidomics data interpretation, lipid biomarkers, lipid metabolism disorders, or lipid-disease connections.\n---\n\n# Lipidomics Analysis\n\nIntegrated pipeline for lipid identification, classification, pathway mapping, and disease association analysis. Distinct from general metabolomics because lipids have unique classification systems (LIPID MAPS), specialized pathways (sphingolipid, eicosanoid, steroid), and disease associations (cardiovascular, neurodegeneration, metabolic syndrome).\n\n## Reasoning Strategy\n\nLipid identification starts with mass spectrometry: the lipid class is determined by the head group fragment mass (e.g., m\u002Fz 184 for phosphocholine in positive mode), total chain length and saturation from the precursor exact mass, and individual fatty acid chains from neutral loss or product ion scans. LIPID MAPS classification organizes lipids by chemical structure into 8 categories — knowing the category immediately tells you the likely biological context (sphingolipids → apoptosis\u002Fneurodegeneration; glycerophospholipids → membrane remodeling; eicosanoids → inflammation). Structural specificity matters biologically: Cer(d18:1\u002F16:0) and Cer(d18:1\u002F24:1) have different membrane properties and disease associations despite being the same lipid class. Always map changed lipids back to metabolic pathways because lipids are intermediates — an elevated ceramide could mean increased synthesis (CERS activity up), decreased degradation (ASAH1 down), or shunting from sphingomyelin (SMPD1 up).\n\n**LOOK UP DON'T GUESS**: Do not assume a lipid's LIPID MAPS ID, exact mass, or pathway membership — query `LipidMaps_search_by_name` first. Do not guess which diseases are associated with a lipid class; retrieve them from HMDB or CTD.\n\n**Key principles**:\n1. **LIPID MAPS classification first** — use the 8-category system (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, polyketides)\n2. **Structural specificity matters** — chain length, unsaturation, and sn-position affect biological function\n3. **Connect to pathways** — lipids are metabolic intermediates; always map to biosynthesis\u002Fdegradation pathways\n4. **Disease context** — many lipids are disease biomarkers (sphingolipids in neurodegeneration, oxidized lipids in CVD)\n5. **Evidence grading** — T1: clinical biomarker studies, T2: mechanistic studies, T3: association data, T4: computational prediction\n\n---\n\n## When to Use\n\n- \"Identify this lipid species from m\u002Fz and retention time\"\n- \"What pathways involve ceramide\u002Fsphingomyelin?\"\n- \"Lipid biomarkers for Alzheimer's disease\"\n- \"What diseases are associated with altered sphingolipid metabolism?\"\n- \"Map my lipidomics results to KEGG pathways\"\n- \"Compare lipid profiles between conditions\"\n\n**Not this skill**: For general metabolomics (amino acids, sugars, organic acids), use `tooluniverse-metabolomics`. For drug ADMET properties, use `tooluniverse-admet-prediction`.\n\n---\n\n## Core Tools\n\n| Tool | Use For |\n|------|---------|\n| `LipidMaps_search_by_name` | Lipid identification by name, abbreviation, or mass |\n| `LipidMaps_get_compound_by_id` | Detailed lipid info (structure, classification, pathways) |\n| `HMDB_search` \u002F `HMDB_get_metabolite` | Lipid metabolite details, disease associations |\n| `kegg_search_pathway` | Lipid metabolism pathways (keyword=`sphingolipid`, `glycerolipid`, etc.) |\n| `KEGG_get_pathway_genes` | Enzymes in lipid pathways |\n| `PubChem_get_compound_properties_by_CID` | Chemical properties (mass, formula, SMILES) |\n| `CTD_get_gene_diseases` | Gene-disease links for lipid metabolism enzymes |\n| `DisGeNET_search_gene` | Disease associations for lipid genes |\n| `PubMed_search_articles` | Published lipidomics studies |\n| `OpenTargets_get_associated_drugs_by_target_ensemblID` | Drugs targeting lipid metabolism enzymes |\n\n---\n\n## Workflow\n\n```\nPhase 0: Lipid Identity Resolution\n  Name\u002Fmass\u002Fabbreviation → LIPID MAPS ID → classification\n    |\nPhase 1: Structural Classification\n  LIPID MAPS 8-category system → subclass → molecular species\n    |\nPhase 2: Pathway Mapping\n  KEGG lipid metabolism → biosynthesis\u002Fdegradation enzymes\n    |\nPhase 3: Disease Associations\n  CTD\u002FDisGeNET\u002FHMDB → lipid-disease links with evidence\n    |\nPhase 4: Interpretation & Report\n  Biological significance → biomarker potential → recommendations\n```\n\n### Phase 0: Lipid Identity Resolution\n\n```\nLipidMaps_search_by_name(query=\"ceramide\")  → LMSP ID, exact mass, classification\nHMDB_search(compound_name=\"ceramide\")       → HMDB ID, disease links\nPubChem_get_CID_by_compound_name(name=\"ceramide\") → CID, SMILES\n```\n\n**LIPID MAPS search tips**:\n- Generic names work well: \"ceramide\", \"sphingomyelin\", \"phosphatidylcholine\"\n- Species-level abbreviations like \"Cer(d18:1\u002F16:0)\" may return 0 results — use the generic class name first, then filter by chain length from results\n- For exact mass search: use `LipidMaps_search_by_formula` with molecular formula (e.g., \"C34H67NO3\")\n- If name search fails, try PubChem: `PubChem_get_CID_by_compound_name(name=\"C16 Ceramide\")` then cross-reference\n\n### Phase 1: Structural Classification\n\nUse `LipidMaps_get_compound_by_id` to retrieve the LIPID MAPS 8-category classification (FA, GL, GP, SP, ST, PR, SL, PK) for any lipid. The category immediately signals biological context: SP (sphingolipids) → apoptosis\u002Fneurodegeneration; GP (glycerophospholipids) → membrane remodeling; FA-derived eicosanoids → inflammation.\n\n### Phase 2: Pathway Mapping\n\nKey lipid metabolism pathways in KEGG:\n\n| Pathway | KEGG ID | Key Enzymes | Disease Relevance |\n|---------|---------|-------------|-------------------|\n| Sphingolipid metabolism | hsa00600 | SMPD1, CERS1-6, ASAH1 | Niemann-Pick, Fabry, Gaucher |\n| Glycerophospholipid metabolism | hsa00564 | PLA2, LPCAT, LPIN | Barth syndrome, atherosclerosis |\n| Arachidonic acid metabolism | hsa00590 | COX1\u002F2, LOX, CYP450 | Inflammation, asthma, CVD |\n| Steroid biosynthesis | hsa00100 | HMGCR, CYP51A1, DHCR7 | Hypercholesterolemia, Smith-Lemli-Opitz |\n| Fatty acid biosynthesis | hsa00061 | FASN, ACC, SCD | Obesity, NAFLD, cancer |\n| Fatty acid degradation | hsa00071 | CPT1, ACADM, HADHA | MCAD deficiency, VLCAD deficiency |\n| Bile acid biosynthesis | hsa00120 | CYP7A1, CYP27A1 | Cholestasis, gallstones |\n| Ether lipid metabolism | hsa00565 | AGPS, GNPAT | Rhizomelic chondrodysplasia |\n\n```python\n# Map lipids to pathways\nkegg_search_pathway(keyword=\"sphingolipid\")  # → hsa00600\nKEGG_get_pathway_genes(pathway_id=\"hsa00600\")  # → SMPD1, CERS1, ...\n```\n\n### Phase 3: Disease Associations\n\nFor each lipid or lipid enzyme, check disease links:\n\n```python\nCTD_get_gene_diseases(input_terms=\"SMPD1\")  # sphingomyelinase → Niemann-Pick\nDisGeNET_search_gene(gene=\"SMPD1\")  # broader disease associations\nHMDB_get_metabolite(compound_name=\"ceramide\")  # metabolite-disease links\nPubMed_search_articles(query=\"ceramide biomarker Alzheimer\")  # clinical evidence\n```\n\n**Disease context**: Ceramide elevation → apoptosis, Alzheimer's, insulin resistance. Sphingomyelin depletion → Niemann-Pick. Oxidized phospholipids → CVD. Altered bile acid ratios → NAFLD, cholestasis. Eicosanoid elevation → inflammation. Always verify via HMDB or CTD rather than relying on memory.\n\n### Phase 4: Interpretation & Report\n\n**Computational procedure: Lipid class enrichment analysis**\n\n```python\n# When user provides a list of significantly changed lipids\nimport pandas as pd\nfrom scipy.stats import fisher_exact\n\n# Input: list of changed lipids with LIPID MAPS categories\nchanged = pd.DataFrame({\n    'lipid': ['Cer(d18:1\u002F16:0)', 'SM(d18:1\u002F16:0)', 'PC(16:0\u002F18:1)', 'LPC(18:0)'],\n    'category': ['SP', 'SP', 'GP', 'GP'],\n    'direction': ['up', 'down', 'unchanged', 'up'],\n    'fold_change': [2.1, 0.5, 1.1, 1.8]\n})\n\n# Count changed vs unchanged per category\nfrom collections import Counter\nchanged_cats = Counter(changed[changed['direction'] != 'unchanged']['category'])\ntotal_cats = Counter(changed['category'])\n\n# Report\nprint(\"Lipid class enrichment:\")\nfor cat in total_cats:\n    n_changed = changed_cats.get(cat, 0)\n    n_total = total_cats[cat]\n    print(f\"  {cat}: {n_changed}\u002F{n_total} changed\")\n\n# Interpretation\nif changed_cats.get('SP', 0) \u002F max(total_cats.get('SP', 1), 1) > 0.5:\n    print(\"→ Sphingolipid metabolism is significantly altered\")\n    print(\"  Consider: apoptosis, neurodegeneration, insulin resistance\")\n```\n\n**Report structure**:\n1. **Lipid Identity** — LIPID MAPS classification, structural features\n2. **Pathway Context** — which metabolic pathways are affected\n3. **Disease Associations** — evidence-graded disease links\n4. **Biomarker Assessment** — clinical utility of identified lipid changes\n5. **Mechanistic Model** — how lipid changes connect to disease biology\n6. **Recommendations** — validation experiments, clinical follow-up\n\n---\n\n## Limitations\n\n- **No raw MS data processing** — this skill interprets identified lipids, not raw spectra. Use LipidSearch, MS-DIAL, or LipiDex for identification first.\n- **LIPID MAPS coverage** — some rare or novel lipid species may not be in the database\n- **Quantitative thresholds** — fold-change cutoffs are context-dependent; the skill provides frameworks, not universal thresholds\n- **Species-specific** — most disease data is human; rat\u002Fmouse lipid metabolism can differ significantly\n","# Lipidomics Analysis\n\nIntegrated pipeline for lipid identification, classification, pathway mapping, and disease association analysis. Distinct from general metabolomics because lipids have unique classification systems (LIPID MAPS), specialized pathways (sphingolipid, eicosanoid, steroid), and disease associations (cardiovascular, neurodegeneration, metabolic syndrome).\n\n## Reasoning Strategy\n\nLipid identification starts with mass spectrometry: the lipid class is determined by the head group fragment mass (e.g., m\u002Fz 184 for phosphocholine in positive mode), total chain length and saturation from the precursor exact mass, and individual fatty acid chains from neutral loss or product ion scans. LIPID MAPS classification organizes lipids by chemical structure into 8 categories — knowing the category immediately tells you the likely biological context (sphingolipids → apoptosis\u002Fneurodegeneration; glycerophospholipids → membrane remodeling; eicosanoids → inflammation). Structural specificity matters biologically: Cer(d18:1\u002F16:0) and Cer(d18:1\u002F24:1) have different membrane properties and disease associations despite being the same lipid class. Always map changed lipids back to metabolic pathways because lipids are intermediates — an elevated ceramide could mean increased synthesis (CERS activity up), decreased degradation (ASAH1 down), or shunting from sphingomyelin (SMPD1 up).\n\n**LOOK UP DON'T GUESS**: Do not assume a lipid's LIPID MAPS ID, exact mass, or pathway membership — query `LipidMaps_search_by_name` first. Do not guess which diseases are associated with a lipid class; retrieve them from HMDB or CTD.\n\n**Key principles**:\n1. **LIPID MAPS classification first** — use the 8-category system (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, polyketides)\n2. **Structural specificity matters** — chain length, unsaturation, and sn-position affect biological function\n3. **Connect to pathways** — lipids are metabolic intermediates; always map to biosynthesis\u002Fdegradation pathways\n4. **Disease context** — many lipids are disease biomarkers (sphingolipids in neurodegeneration, oxidized lipids in CVD)\n5. **Evidence grading** — T1: clinical biomarker studies, T2: mechanistic studies, T3: association data, T4: computational prediction\n\n---\n\n## When to Use\n\n- \"Identify this lipid species from m\u002Fz and retention time\"\n- \"What pathways involve ceramide\u002Fsphingomyelin?\"\n- \"Lipid biomarkers for Alzheimer's disease\"\n- \"What diseases are associated with altered sphingolipid metabolism?\"\n- \"Map my lipidomics results to KEGG pathways\"\n- \"Compare lipid profiles between conditions\"\n\n**Not this skill**: For general metabolomics (amino acids, sugars, organic acids), use `tooluniverse-metabolomics`. For drug ADMET properties, use `tooluniverse-admet-prediction`.\n\n---\n\n## Core Tools\n\n| Tool | Use For |\n|------|---------|\n| `LipidMaps_search_by_name` | Lipid identification by name, abbreviation, or mass |\n| `LipidMaps_get_compound_by_id` | Detailed lipid info (structure, classification, pathways) |\n| `HMDB_search` \u002F `HMDB_get_metabolite` | Lipid metabolite details, disease associations |\n| `kegg_search_pathway` | Lipid metabolism pathways (keyword=`sphingolipid`, `glycerolipid`, etc.) |\n| `KEGG_get_pathway_genes` | Enzymes in lipid pathways |\n| `PubChem_get_compound_properties_by_CID` | Chemical properties (mass, formula, SMILES) |\n| `CTD_get_gene_diseases` | Gene-disease links for lipid metabolism enzymes |\n| `DisGeNET_search_gene` | Disease associations for lipid genes |\n| `PubMed_search_articles` | Published lipidomics studies |\n| `OpenTargets_get_associated_drugs_by_target_ensemblID` | Drugs targeting lipid metabolism enzymes |\n\n---\n\n## Workflow\n\n```\nPhase 0: Lipid Identity Resolution\n  Name\u002Fmass\u002Fabbreviation → LIPID MAPS ID → classification\n    |\nPhase 1: Structural Classification\n  LIPID MAPS 8-category system → subclass → molecular species\n    |\nPhase 2: Pathway Mapping\n  KEGG lipid metabolism → biosynthesis\u002Fdegradation enzymes\n    |\nPhase 3: Disease Associations\n  CTD\u002FDisGeNET\u002FHMDB → lipid-disease links with evidence\n    |\nPhase 4: Interpretation & Report\n  Biological significance → biomarker potential → recommendations\n```\n\n### Phase 0: Lipid Identity Resolution\n\n```\nLipidMaps_search_by_name(query=\"ceramide\")  → LMSP ID, exact mass, classification\nHMDB_search(compound_name=\"ceramide\")       → HMDB ID, disease links\nPubChem_get_CID_by_compound_name(name=\"ceramide\") → CID, SMILES\n```\n\n**LIPID MAPS search tips**:\n- Generic names work well: \"ceramide\", \"sphingomyelin\", \"phosphatidylcholine\"\n- Species-level abbreviations like \"Cer(d18:1\u002F16:0)\" may return 0 results — use the generic class name first, then filter by chain length from results\n- For exact mass search: use `LipidMaps_search_by_formula` with molecular formula (e.g., \"C34H67NO3\")\n- If name search fails, try PubChem: `PubChem_get_CID_by_compound_name(name=\"C16 Ceramide\")` then cross-reference\n\n### Phase 1: Structural Classification\n\nUse `LipidMaps_get_compound_by_id` to retrieve the LIPID MAPS 8-category classification (FA, GL, GP, SP, ST, PR, SL, PK) for any lipid. The category immediately signals biological context: SP (sphingolipids) → apoptosis\u002Fneurodegeneration; GP (glycerophospholipids) → membrane remodeling; FA-derived eicosanoids → inflammation.\n\n### Phase 2: Pathway Mapping\n\nKey lipid metabolism pathways in KEGG:\n\n| Pathway | KEGG ID | Key Enzymes | Disease Relevance |\n|---------|---------|-------------|-------------------|\n| Sphingolipid metabolism | hsa00600 | SMPD1, CERS1-6, ASAH1 | Niemann-Pick, Fabry, Gaucher |\n| Glycerophospholipid metabolism | hsa00564 | PLA2, LPCAT, LPIN | Barth syndrome, atherosclerosis |\n| Arachidonic acid metabolism | hsa00590 | COX1\u002F2, LOX, CYP450 | Inflammation, asthma, CVD |\n| Steroid biosynthesis | hsa00100 | HMGCR, CYP51A1, DHCR7 | Hypercholesterolemia, Smith-Lemli-Opitz |\n| Fatty acid biosynthesis | hsa00061 | FASN, ACC, SCD | Obesity, NAFLD, cancer |\n| Fatty acid degradation | hsa00071 | CPT1, ACADM, HADHA | MCAD deficiency, VLCAD deficiency |\n| Bile acid biosynthesis | hsa00120 | CYP7A1, CYP27A1 | Cholestasis, gallstones |\n| Ether lipid metabolism | hsa00565 | AGPS, GNPAT | Rhizomelic chondrodysplasia |\n\n```python\n# Map lipids to pathways\nkegg_search_pathway(keyword=\"sphingolipid\")  # → hsa00600\nKEGG_get_pathway_genes(pathway_id=\"hsa00600\")  # → SMPD1, CERS1, ...\n```\n\n### Phase 3: Disease Associations\n\nFor each lipid or lipid enzyme, check disease links:\n\n```python\nCTD_get_gene_diseases(input_terms=\"SMPD1\")  # sphingomyelinase → Niemann-Pick\nDisGeNET_search_gene(gene=\"SMPD1\")  # broader disease associations\nHMDB_get_metabolite(compound_name=\"ceramide\")  # metabolite-disease links\nPubMed_search_articles(query=\"ceramide biomarker Alzheimer\")  # clinical evidence\n```\n\n**Disease context**: Ceramide elevation → apoptosis, Alzheimer's, insulin resistance. Sphingomyelin depletion → Niemann-Pick. Oxidized phospholipids → CVD. Altered bile acid ratios → NAFLD, cholestasis. Eicosanoid elevation → inflammation. Always verify via HMDB or CTD rather than relying on memory.\n\n### Phase 4: Interpretation & Report\n\n**Computational procedure: Lipid class enrichment analysis**\n\n```python\n# When user provides a list of significantly changed lipids\nimport pandas as pd\nfrom scipy.stats import fisher_exact\n\n# Input: list of changed lipids with LIPID MAPS categories\nchanged = pd.DataFrame({\n    'lipid': ['Cer(d18:1\u002F16:0)', 'SM(d18:1\u002F16:0)', 'PC(16:0\u002F18:1)', 'LPC(18:0)'],\n    'category': ['SP', 'SP', 'GP', 'GP'],\n    'direction': ['up', 'down', 'unchanged', 'up'],\n    'fold_change': [2.1, 0.5, 1.1, 1.8]\n})\n\n# Count changed vs unchanged per category\nfrom collections import Counter\nchanged_cats = Counter(changed[changed['direction'] != 'unchanged']['category'])\ntotal_cats = Counter(changed['category'])\n\n# Report\nprint(\"Lipid class enrichment:\")\nfor cat in total_cats:\n    n_changed = changed_cats.get(cat, 0)\n    n_total = total_cats[cat]\n    print(f\"  {cat}: {n_changed}\u002F{n_total} changed\")\n\n# Interpretation\nif changed_cats.get('SP', 0) \u002F max(total_cats.get('SP', 1), 1) > 0.5:\n    print(\"→ Sphingolipid metabolism is significantly altered\")\n    print(\"  Consider: apoptosis, neurodegeneration, insulin resistance\")\n```\n\n**Report structure**:\n1. **Lipid Identity** — LIPID MAPS classification, structural features\n2. **Pathway Context** — which metabolic pathways are affected\n3. **Disease Associations** — evidence-graded disease links\n4. **Biomarker Assessment** — clinical utility of identified lipid changes\n5. **Mechanistic Model** — how lipid changes connect to disease biology\n6. **Recommendations** — validation experiments, clinical follow-up\n\n---\n\n## Limitations\n\n- **No raw MS data processing** — this skill interprets identified lipids, not raw spectra. Use LipidSearch, MS-DIAL, or LipiDex for identification first.\n- **LIPID MAPS coverage** — some rare or novel lipid species may not be in the database\n- **Quantitative thresholds** — fold-change cutoffs are context-dependent; the skill provides frameworks, not universal thresholds\n- **Species-specific** — most disease data is human; rat\u002Fmouse lipid metabolism can differ significantly\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-lipidomics","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":46,"prompt":56,"zipAvailable":57},[47,50,53],{"tool":48,"command":49},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-lipidomics",{"tool":51,"command":52},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-lipidomics",{"tool":54,"command":55},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-lipidomics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-lipidomics\u002FSKILL.md",true,[59,65],{"path":60,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":63,"sourceHtmlUrl":64},"agents\u002Fopenai.yaml","REFERENCE","参考文档",221,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fswiftui-liquid-glass\u002Fagents\u002Fopenai.yaml",{"path":66,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":67,"sourceHtmlUrl":68},"references\u002Fliquid-glass.md",8159,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fswiftui-liquid-glass\u002Freferences\u002Fliquid-glass.md",[70,91,121,148,162,178],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":34},"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",[],[77,78,79,80,81,82,83,84,85,27,86,87,88,89,90],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":102,"publishedAt":34},"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",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[103,104,105,106,107,25,108,109,110,111,112,113,114,115,27,116,117,118,119,120],"openmm md","how","decision","pick","linux","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":133,"publishedAt":34},"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",[127,130],{"categoryNo":128,"categoryName":129,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":131,"categoryName":132,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000008","测试与质量",[134,135,136,137,138,139,140,141,142,143,104,144,145,146,147],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":34},"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",[],[150,155,156,157,158,159,160,161],"setup","report","project","installation","project setup","issues","state",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":169,"publishedAt":34},"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",[168],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[164,170,82,171,172,173,27,174,175,176,177],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":179,"name":155,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":183,"publishedAt":34},"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",[],[155,157,118,184,159,185],"files","interactive"]