[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DAM47ZA35H2A2N6X0PV427":3,"skill-same:01M6DAM47ZA35H2A2N6X0PV427":63},{"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":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":14,"repositoryHtmlUrl":40,"repositoryHomepageUrl":6,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":42,"install":43,"resources":56},"01M6DAM47ZA35H2A2N6X0PV427","tooluniverse-adverse-outcome-pathway",null,"Map environmental\u002Findustrial chemicals to mechanistic adverse outcome pathways (AOPs) using AOPWiki, quantify toxicological hazard (PubChemTox GHS\u002Fcarcinogen classification, LD50 values), and link chemical stressors to gene targets and disease endpoints via CTD for regulatory risk assessment. Use when asked about AOP stressor mapping, GHS hazard categories, LD50 data, IARC carcinogen classification, or mechanism-based risk assessment for non-drug chemicals.","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-adverse-outcome-pathway\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31],"outcome","tooluniverse adverse outcome pathway","risk assessment","assessment","data","risk","classification","use","regulatory","endpoints","map","chemical","environmental","2026-11-10 10:12:30",false,"---\nname: tooluniverse-adverse-outcome-pathway\ndescription: >\n  Map environmental\u002Findustrial chemicals to mechanistic adverse outcome pathways (AOPs) using\n  AOPWiki, quantify toxicological hazard (PubChemTox GHS\u002Fcarcinogen classification, LD50 values),\n  and link chemical stressors to gene targets and disease endpoints via CTD for regulatory risk\n  assessment. Use when asked about AOP stressor mapping, GHS hazard categories, LD50 data,\n  IARC carcinogen classification, or mechanism-based risk assessment for non-drug chemicals.\n---\n\n# Adverse Outcome Pathway & Regulatory Risk Assessment\n\nDistinct from drug safety (see tooluniverse-toxicology): this skill targets **environmental and\nindustrial chemicals** where the focus is AOP stressor mapping, GHS classification, LD50 hazard\nquantification, and IARC carcinogen status — not FAERS signals or FDA drug labels.\n\n## When to Use\n\nApply when researcher asks about:\n- \"What AOPs are associated with [pesticide\u002Fsolvent\u002Findustrial chemical]?\"\n- \"What is the GHS hazard classification for [compound]?\"\n- \"What is the LD50 for [compound]?\"\n- \"Is [compound] a carcinogen (IARC classification)?\"\n- \"Which genes does [chemical] interact with (CTD)?\"\n- \"Regulatory risk assessment for [environmental chemical]\"\n- \"What diseases are associated with [chemical] exposure?\"\n\nDo NOT use for FDA-approved drugs with FAERS data — use `tooluniverse-toxicology` instead.\n\n## Key Tools\n\n| Tool | Purpose | Key Params |\n|------|---------|-----------|\n| `AOPWiki_list_aops` | Discover AOPs by keyword | `keyword` (organ, effect, or target name) |\n| `AOPWiki_get_aop` | Full AOP details: MIE, key events, stressors | `aop_id` (int) |\n| `PubChemTox_get_toxicity_summary` | Narrative toxicity overview | `cid` (PubChem CID) |\n| `PubChemTox_get_ghs_classification` | GHS hazard category + pictograms | `cid` |\n| `PubChemTox_get_carcinogen_classification` | IARC\u002FNTP\u002FEPA carcinogen status | `cid` |\n| `PubChemTox_get_toxicity_values` | LD50\u002FLC50 by route and species | `cid` |\n| `PubChemTox_get_acute_effects` | Signs and symptoms of acute exposure | `cid` |\n| `CTD_get_chemical_gene_interactions` | Chemical-gene molecular interactions | `input_terms` (name or MeSH ID) |\n| `CTD_get_chemical_diseases` | Chemical-disease associations | `input_terms` |\n| `PubChem_get_CID_by_compound_name` | Resolve compound name to PubChem CID | `name` |\n\n## Workflow\n\n### Phase 1: Compound Identity Resolution\n\nResolve chemical name to PubChem CID before all PubChemTox calls.\n\n```\nPubChem_get_CID_by_compound_name(name=\"benzo[a]pyrene\")\n-> cid: 9153 (use for all PubChemTox calls)\n```\n\nNote: CTD tools accept the chemical name directly (`input_terms` param) — no CID needed.\n\n### Phase 2: AOP Discovery\n\nFind relevant AOPs by searching organ targets and mechanism keywords.\n\n```\nAOPWiki_list_aops(keyword=\"lung\")          # organ-level\nAOPWiki_list_aops(keyword=\"DNA damage\")    # mechanism-level\nAOPWiki_list_aops(keyword=\"AhR\")           # receptor-level\n```\n\nSelect 2-4 candidate AOPs from results, then retrieve full details:\n\n```\nAOPWiki_get_aop(aop_id=58)  # returns MIE, key events, stressors, biological plausibility\n```\n\nKey fields in `AOPWiki_get_aop` response:\n- `stressors`: list of chemicals that trigger this AOP (check if query compound is listed)\n- `molecular_initiating_event`: the first molecular perturbation\n- `key_events`: ordered chain of biological events\n- `adverse_outcome`: apical regulatory endpoint\n\n### Phase 3: Hazard Quantification (PubChemTox)\n\nRun all four hazard queries in parallel using the resolved CID:\n\n```\nPubChemTox_get_ghs_classification(cid=9153)        # GHS category + pictogram\nPubChemTox_get_carcinogen_classification(cid=9153)  # IARC Group 1\u002F2A\u002F2B\u002F3\nPubChemTox_get_toxicity_values(cid=9153)            # LD50 by route\u002Fspecies\nPubChemTox_get_acute_effects(cid=9153)              # signs\u002Fsymptoms\n```\n\nNote: `PubChemTox_get_target_organs` sometimes returns no data — treat as optional.\n\n### Phase 4: Toxicogenomics (CTD)\n\nMap chemical to gene targets and disease associations:\n\n```\nCTD_get_chemical_gene_interactions(input_terms=\"benzo[a]pyrene\")\nCTD_get_chemical_diseases(input_terms=\"benzo[a]pyrene\")\n```\n\nCross-reference CTD gene targets with AOP key event genes from Phase 2.\n\n## Tool Parameter Reference\n\n| Tool | Required | Optional | Notes |\n|------|---------|---------|-------|\n| `AOPWiki_list_aops` | `keyword` | — | Use organ (\"liver\"), effect (\"apoptosis\"), or receptor (\"PPARalpha\") |\n| `AOPWiki_get_aop` | `aop_id` | — | Integer ID from list_aops output |\n| `PubChemTox_get_toxicity_summary` | `cid` | — | PubChem CID integer |\n| `PubChemTox_get_ghs_classification` | `cid` | — | Returns pictogram_labels e.g. \"Health Hazard\" |\n| `PubChemTox_get_carcinogen_classification` | `cid` | — | IARC Group in `classifications[].classification` |\n| `PubChemTox_get_toxicity_values` | `cid` | — | Values like \"LD50 Rat oral 2400 mg\u002Fkg\" |\n| `PubChemTox_get_acute_effects` | `cid` | — | Sometimes sparse; not all compounds have data |\n| `CTD_get_chemical_gene_interactions` | `input_terms` | — | Accepts name or MeSH ID (e.g., \"D001564\") |\n| `CTD_get_chemical_diseases` | `input_terms` | — | Filter `DirectEvidence` = \"marker\u002Fmechanism\" for curated |\n| `PubChem_get_CID_by_compound_name` | `name` | — | Returns CID + SMILES; required before PubChemTox calls |\n\n## Common Patterns\n\n```python\n# Pattern: Confirm compound is a stressor in a specific AOP\naop = AOPWiki_get_aop(aop_id=58)\nstressors = [s[\"name\"] for s in aop[\"data\"][\"stressors\"]]\n# Check if query chemical appears in stressors list\n\n# Pattern: Extract curated CTD disease associations only\ndiseases = CTD_get_chemical_diseases(input_terms=\"rotenone\")\ncurated = [d for d in diseases[\"data\"] if d.get(\"DirectEvidence\")]\n\n# Pattern: GHS carcinogen check\ncarcinogen = PubChemTox_get_carcinogen_classification(cid=9153)\niarc = [c for c in carcinogen[\"data\"][\"classifications\"] if \"IARC\" in c.get(\"source\", \"\")]\n```\n\n## Reasoning Framework for Result Interpretation\n\n### Evidence Grading\n\n| Grade | Criteria | Example |\n|-------|----------|---------|\n| **Strong** | AOP in OECD-endorsed status, compound listed as stressor, CTD + AOPWiki concordant | AOP 58 (AhR → liver tumor) endorsed, benzo[a]pyrene confirmed stressor |\n| **Moderate** | AOP under review or well-documented, compound class match but not individually listed | AOP links PPARalpha activation to liver effects; query compound is a fibrate analog |\n| **Weak** | AOP in development, compound not listed but shares MIE target via CTD gene overlap | CTD shows gene target overlap with AOP key event genes, but no direct stressor listing |\n| **Insufficient** | No AOP found, no CTD gene-disease link, hazard data sparse | Novel compound with no toxicological database entries |\n\n### Interpretation Guidance\n\n- **AOP weight-of-evidence assessment**: OECD-endorsed AOPs have undergone expert review and represent the highest confidence mechanistic pathways. AOPs \"under development\" in AOPWiki may have incomplete key event relationships. Evaluate each AOP by: (1) biological plausibility of key event relationships, (2) empirical support (dose-response concordance), (3) essentiality of key events (blocking KE prevents AO).\n- **Key event relationship (KER) strength**: Strong KERs have dose-response and temporal concordance between upstream and downstream key events. Moderate KERs have correlative evidence. Weak KERs are based on plausibility alone. The weakest KER in the chain determines the overall AOP confidence for that pathway.\n- **Stressor potency interpretation**: LD50 values indicate acute toxicity (lower = more toxic). GHS categories: Cat 1 (LD50 \u003C= 5 mg\u002Fkg, fatal), Cat 2 (5-50, fatal), Cat 3 (50-300, toxic), Cat 4 (300-2000, harmful), Cat 5 (2000-5000, may be harmful). IARC Group 1 = confirmed carcinogen, 2A = probable, 2B = possible, 3 = not classifiable. Always report route of exposure and species for LD50 values.\n- **CTD integration**: CTD \"direct evidence\" (curated marker\u002Fmechanism) is stronger than \"inferred\" associations. When CTD gene targets overlap with AOP key event genes, this supports the mechanistic link between the compound and the adverse outcome.\n- **Regulatory context**: For risk assessment, combine hazard identification (IARC, GHS) with exposure assessment. A potent carcinogen at negligible exposure may pose lower risk than a moderate toxicant at high exposure.\n\n### Synthesis Questions\n\n1. Is the query compound explicitly listed as a stressor in the identified AOP, or is the link inferred from shared molecular targets (CTD gene overlap)?\n2. Do the key event relationships in the AOP chain have sufficient empirical support (dose-response concordance, temporal sequence), or are there weak links that reduce confidence?\n3. Are the hazard data (LD50, GHS, IARC) consistent across sources, and do they support the severity implied by the AOP adverse outcome?\n4. Does the CTD gene-disease evidence corroborate the AOP's predicted adverse outcome, or are there discrepancies suggesting alternative pathways?\n5. For regulatory decision-making, is the combined weight of evidence (AOP mechanism + hazard quantification + exposure context) sufficient to support a risk classification?\n\n---\n\n## Fallback Chains\n\n| Primary | Fallback | When |\n|---------|---------|------|\n| `AOPWiki_list_aops` with specific keyword | Broader organ term | No results |\n| `PubChemTox_get_target_organs` | `PubChemTox_get_toxicity_summary` | Returns empty |\n| `CTD_get_chemical_diseases` | `CTD_get_gene_diseases` + gene from CTD interactions | Compound name not recognized |\n","# Adverse Outcome Pathway & Regulatory Risk Assessment\n\nDistinct from drug safety (see tooluniverse-toxicology): this skill targets **environmental and\nindustrial chemicals** where the focus is AOP stressor mapping, GHS classification, LD50 hazard\nquantification, and IARC carcinogen status — not FAERS signals or FDA drug labels.\n\n## When to Use\n\nApply when researcher asks about:\n- \"What AOPs are associated with [pesticide\u002Fsolvent\u002Findustrial chemical]?\"\n- \"What is the GHS hazard classification for [compound]?\"\n- \"What is the LD50 for [compound]?\"\n- \"Is [compound] a carcinogen (IARC classification)?\"\n- \"Which genes does [chemical] interact with (CTD)?\"\n- \"Regulatory risk assessment for [environmental chemical]\"\n- \"What diseases are associated with [chemical] exposure?\"\n\nDo NOT use for FDA-approved drugs with FAERS data — use `tooluniverse-toxicology` instead.\n\n## Key Tools\n\n| Tool | Purpose | Key Params |\n|------|---------|-----------|\n| `AOPWiki_list_aops` | Discover AOPs by keyword | `keyword` (organ, effect, or target name) |\n| `AOPWiki_get_aop` | Full AOP details: MIE, key events, stressors | `aop_id` (int) |\n| `PubChemTox_get_toxicity_summary` | Narrative toxicity overview | `cid` (PubChem CID) |\n| `PubChemTox_get_ghs_classification` | GHS hazard category + pictograms | `cid` |\n| `PubChemTox_get_carcinogen_classification` | IARC\u002FNTP\u002FEPA carcinogen status | `cid` |\n| `PubChemTox_get_toxicity_values` | LD50\u002FLC50 by route and species | `cid` |\n| `PubChemTox_get_acute_effects` | Signs and symptoms of acute exposure | `cid` |\n| `CTD_get_chemical_gene_interactions` | Chemical-gene molecular interactions | `input_terms` (name or MeSH ID) |\n| `CTD_get_chemical_diseases` | Chemical-disease associations | `input_terms` |\n| `PubChem_get_CID_by_compound_name` | Resolve compound name to PubChem CID | `name` |\n\n## Workflow\n\n### Phase 1: Compound Identity Resolution\n\nResolve chemical name to PubChem CID before all PubChemTox calls.\n\n```\nPubChem_get_CID_by_compound_name(name=\"benzo[a]pyrene\")\n-> cid: 9153 (use for all PubChemTox calls)\n```\n\nNote: CTD tools accept the chemical name directly (`input_terms` param) — no CID needed.\n\n### Phase 2: AOP Discovery\n\nFind relevant AOPs by searching organ targets and mechanism keywords.\n\n```\nAOPWiki_list_aops(keyword=\"lung\")          # organ-level\nAOPWiki_list_aops(keyword=\"DNA damage\")    # mechanism-level\nAOPWiki_list_aops(keyword=\"AhR\")           # receptor-level\n```\n\nSelect 2-4 candidate AOPs from results, then retrieve full details:\n\n```\nAOPWiki_get_aop(aop_id=58)  # returns MIE, key events, stressors, biological plausibility\n```\n\nKey fields in `AOPWiki_get_aop` response:\n- `stressors`: list of chemicals that trigger this AOP (check if query compound is listed)\n- `molecular_initiating_event`: the first molecular perturbation\n- `key_events`: ordered chain of biological events\n- `adverse_outcome`: apical regulatory endpoint\n\n### Phase 3: Hazard Quantification (PubChemTox)\n\nRun all four hazard queries in parallel using the resolved CID:\n\n```\nPubChemTox_get_ghs_classification(cid=9153)        # GHS category + pictogram\nPubChemTox_get_carcinogen_classification(cid=9153)  # IARC Group 1\u002F2A\u002F2B\u002F3\nPubChemTox_get_toxicity_values(cid=9153)            # LD50 by route\u002Fspecies\nPubChemTox_get_acute_effects(cid=9153)              # signs\u002Fsymptoms\n```\n\nNote: `PubChemTox_get_target_organs` sometimes returns no data — treat as optional.\n\n### Phase 4: Toxicogenomics (CTD)\n\nMap chemical to gene targets and disease associations:\n\n```\nCTD_get_chemical_gene_interactions(input_terms=\"benzo[a]pyrene\")\nCTD_get_chemical_diseases(input_terms=\"benzo[a]pyrene\")\n```\n\nCross-reference CTD gene targets with AOP key event genes from Phase 2.\n\n## Tool Parameter Reference\n\n| Tool | Required | Optional | Notes |\n|------|---------|---------|-------|\n| `AOPWiki_list_aops` | `keyword` | — | Use organ (\"liver\"), effect (\"apoptosis\"), or receptor (\"PPARalpha\") |\n| `AOPWiki_get_aop` | `aop_id` | — | Integer ID from list_aops output |\n| `PubChemTox_get_toxicity_summary` | `cid` | — | PubChem CID integer |\n| `PubChemTox_get_ghs_classification` | `cid` | — | Returns pictogram_labels e.g. \"Health Hazard\" |\n| `PubChemTox_get_carcinogen_classification` | `cid` | — | IARC Group in `classifications[].classification` |\n| `PubChemTox_get_toxicity_values` | `cid` | — | Values like \"LD50 Rat oral 2400 mg\u002Fkg\" |\n| `PubChemTox_get_acute_effects` | `cid` | — | Sometimes sparse; not all compounds have data |\n| `CTD_get_chemical_gene_interactions` | `input_terms` | — | Accepts name or MeSH ID (e.g., \"D001564\") |\n| `CTD_get_chemical_diseases` | `input_terms` | — | Filter `DirectEvidence` = \"marker\u002Fmechanism\" for curated |\n| `PubChem_get_CID_by_compound_name` | `name` | — | Returns CID + SMILES; required before PubChemTox calls |\n\n## Common Patterns\n\n```python\n# Pattern: Confirm compound is a stressor in a specific AOP\naop = AOPWiki_get_aop(aop_id=58)\nstressors = [s[\"name\"] for s in aop[\"data\"][\"stressors\"]]\n# Check if query chemical appears in stressors list\n\n# Pattern: Extract curated CTD disease associations only\ndiseases = CTD_get_chemical_diseases(input_terms=\"rotenone\")\ncurated = [d for d in diseases[\"data\"] if d.get(\"DirectEvidence\")]\n\n# Pattern: GHS carcinogen check\ncarcinogen = PubChemTox_get_carcinogen_classification(cid=9153)\niarc = [c for c in carcinogen[\"data\"][\"classifications\"] if \"IARC\" in c.get(\"source\", \"\")]\n```\n\n## Reasoning Framework for Result Interpretation\n\n### Evidence Grading\n\n| Grade | Criteria | Example |\n|-------|----------|---------|\n| **Strong** | AOP in OECD-endorsed status, compound listed as stressor, CTD + AOPWiki concordant | AOP 58 (AhR → liver tumor) endorsed, benzo[a]pyrene confirmed stressor |\n| **Moderate** | AOP under review or well-documented, compound class match but not individually listed | AOP links PPARalpha activation to liver effects; query compound is a fibrate analog |\n| **Weak** | AOP in development, compound not listed but shares MIE target via CTD gene overlap | CTD shows gene target overlap with AOP key event genes, but no direct stressor listing |\n| **Insufficient** | No AOP found, no CTD gene-disease link, hazard data sparse | Novel compound with no toxicological database entries |\n\n### Interpretation Guidance\n\n- **AOP weight-of-evidence assessment**: OECD-endorsed AOPs have undergone expert review and represent the highest confidence mechanistic pathways. AOPs \"under development\" in AOPWiki may have incomplete key event relationships. Evaluate each AOP by: (1) biological plausibility of key event relationships, (2) empirical support (dose-response concordance), (3) essentiality of key events (blocking KE prevents AO).\n- **Key event relationship (KER) strength**: Strong KERs have dose-response and temporal concordance between upstream and downstream key events. Moderate KERs have correlative evidence. Weak KERs are based on plausibility alone. The weakest KER in the chain determines the overall AOP confidence for that pathway.\n- **Stressor potency interpretation**: LD50 values indicate acute toxicity (lower = more toxic). GHS categories: Cat 1 (LD50 \u003C= 5 mg\u002Fkg, fatal), Cat 2 (5-50, fatal), Cat 3 (50-300, toxic), Cat 4 (300-2000, harmful), Cat 5 (2000-5000, may be harmful). IARC Group 1 = confirmed carcinogen, 2A = probable, 2B = possible, 3 = not classifiable. Always report route of exposure and species for LD50 values.\n- **CTD integration**: CTD \"direct evidence\" (curated marker\u002Fmechanism) is stronger than \"inferred\" associations. When CTD gene targets overlap with AOP key event genes, this supports the mechanistic link between the compound and the adverse outcome.\n- **Regulatory context**: For risk assessment, combine hazard identification (IARC, GHS) with exposure assessment. A potent carcinogen at negligible exposure may pose lower risk than a moderate toxicant at high exposure.\n\n### Synthesis Questions\n\n1. Is the query compound explicitly listed as a stressor in the identified AOP, or is the link inferred from shared molecular targets (CTD gene overlap)?\n2. Do the key event relationships in the AOP chain have sufficient empirical support (dose-response concordance, temporal sequence), or are there weak links that reduce confidence?\n3. Are the hazard data (LD50, GHS, IARC) consistent across sources, and do they support the severity implied by the AOP adverse outcome?\n4. Does the CTD gene-disease evidence corroborate the AOP's predicted adverse outcome, or are there discrepancies suggesting alternative pathways?\n5. For regulatory decision-making, is the combined weight of evidence (AOP mechanism + hazard quantification + exposure context) sufficient to support a risk classification?\n\n---\n\n## Fallback Chains\n\n| Primary | Fallback | When |\n|---------|---------|------|\n| `AOPWiki_list_aops` with specific keyword | Broader organ term | No results |\n| `PubChemTox_get_target_organs` | `PubChemTox_get_toxicity_summary` | Returns empty |\n| `CTD_get_chemical_diseases` | `CTD_get_gene_diseases` + gene from CTD interactions | Compound name not recognized |\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-adverse-outcome-pathway","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-adverse-outcome-pathway",{"tool":49,"command":50},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-adverse-outcome-pathway",{"tool":52,"command":53},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-adverse-outcome-pathway","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-adverse-outcome-pathway\u002FSKILL.md",true,[57],{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"resources\u002Fimplementation-playbook.md","REFERENCE","参考文档",13329,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fsql-optimization-patterns\u002Fresources\u002Fimplementation-playbook.md",[64,84,115,142,156,172],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"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",[],[71,72,73,24,74,75,76,77,78,26,79,80,81,82,83],"clarify requirements","clarify","requirements","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":95,"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",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[96,97,98,99,100,101,102,103,104,105,106,107,108,109,26,110,111,112,113,114],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":127,"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",[121,124],{"categoryNo":122,"categoryName":123,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":125,"categoryName":126,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000008","测试与质量",[128,129,130,131,132,133,134,135,136,137,97,138,139,140,141],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"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",[],[144,149,150,151,152,153,154,155],"setup","report","project","installation","project setup","issues","state",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":163,"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",[162],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},[158,164,75,165,166,167,26,168,169,170,171],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":173,"name":149,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":177,"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",[],[149,151,112,178,153,179],"files","interactive"]