[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DDJFSAMY4P60000MWV2TCJ":3,"skill-same:01M6DDJFSAMY4P60000MWV2TCJ":71},{"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":26,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6DDJFSAMY4P60000MWV2TCJ","mechanistic-ddi-pipeline",null,"Use to orchestrate a full mechanistic drug-drug interaction verdict by combining two MCP stacks - ToolUniverse CYP\u002FUGT\u002Ftransporter and DDI knowledge plus FDA-label evidence with Schrodinger structure tools (SMILES to 3D, descriptors, ADMET, docking) - fusing knowledge-base and structure-level evidence into one perpetrator-victim verdict with severity and management. NOT for knowledge-base DDI lookups alone (use ddi-admet-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\u002Freconcile-flags\u002Fimport-2026-08-28\u002Ftoolchain-root\u002Fskills\u002Fmechanistic-ddi-pipeline\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"mechanistic ddi pipeline","pipeline","use","tooluniverse","docking","full","knowledge-base","structure","base","evidence","fda","MCP","management","knowledge","3D","Tools","severity","orchestrate","research","2026-11-10 10:12:30",false,"---\nname: mechanistic-ddi-pipeline\ndescription: Use to orchestrate a full mechanistic drug-drug interaction verdict by combining two MCP stacks - ToolUniverse CYP\u002FUGT\u002Ftransporter and DDI knowledge plus FDA-label evidence with Schrodinger structure tools (SMILES to 3D, descriptors, ADMET, docking) - fusing knowledge-base and structure-level evidence into one perpetrator-victim verdict with severity and management. NOT for knowledge-base DDI lookups alone (use ddi-admet-research).\n---\n\n# Mechanistic DDI Pipeline (cross-stack orchestrator)\n\n## Overview\n\nThis skill is the GLUE between two servers. It fuses ToolUniverse knowledge (CYP\u002FUGT\u002Ftransporter roles, DDI records, FDA labels, literature) with Schrodinger structure-level computation (3D generation, drug-likeness descriptors, QikProp ADMET, optional Glide docking) into one perpetrator-victim DDI verdict. It does not replace the deep `tooluniverse-drug-drug-interaction` skill - it adds the structure layer.\n\nStacks used:\n\n- `tooluniverse` MCP (knowledge) - see the `ddi-admet-research` and `tooluniverse-drug-drug-interaction` skills for routing and tool details.\n- `schrodinger` MCP (structure) - see the `schrodinger-maestro-driver` skill for the 67 tools, the license-token split, and the full prep -> grid -> dock -> MM-GBSA pipeline (`glide_grid` builds the grid in-server).\n\n## When to Use\n\n- A DDI question where mechanism must be backed by both pathway knowledge AND molecular evidence (descriptors, ADMET, or a docking pose into a CYP\u002Ftarget structure).\n- A novel or under-documented compound (limited DDI records) where structure-derived properties inform the prediction.\n- A case where you must reconcile a knowledge-base claim with computed physchem\u002FADMET (e.g., does the structure support the proposed CYP-substrate role).\n\nIf the question is purely knowledge-based, use `ddi-admet-research` instead - do not spin up structure tools unnecessarily.\n\n## Orchestrated Workflow\n\n1. Frame the pair with the perpetrator-victim model (who changes whom). Apply the CYP\u002FUGT reasoning from `tooluniverse-drug-drug-interaction` BEFORE any tool call.\n2. Knowledge pass (ToolUniverse): resolve identifiers, then query CYP\u002Ftransporter roles (`ChEMBL_get_drug_mechanisms`), known interactions (`drugbank_get_drug_interactions_by_drug_name_or_id`), FDA label evidence (`DailyMed_get_spl_by_setid`), and literature (`PubMed_search_articles`).\n3. Structure pass (Schrodinger): for each drug, compute the drug-likeness panel (`molecule_properties(smiles)`); generate 3D where needed (`smiles_to_3d`); run `qikprop_admet` on a prepared structure for ADMET\u002Fphyschem that supports or challenges the predicted pathway.\n4. Optional docking corroboration: prep the receptor (`prepwizard`) and build a grid (`glide_grid`) for a CYP isoform\u002Ftarget, then dock the perpetrator\u002Fvictim with `glide_dock` to test the proposed binding\u002Finhibition hypothesis, optionally rescoring with `prime_mmgbsa`. No receptor structure on hand = skip docking (see `structure-based-screening`).\n5. Synthesize the verdict: combine mechanism (PK via CYP\u002FUGT\u002Ftransporter, PD overlap), severity (Contraindicated\u002FMajor\u002FModerate\u002FMinor), direction (inhibitor up \u002F inducer down), and structure-derived support into a single ranked conclusion with management + monitoring.\n6. Grade every claim: FDA label (★★★) > clinical study (★★☆) > theoretical\u002Fstructure-inferred (★☆☆). Mark structure-derived inferences as supporting, not definitive.\n\n## Conflict Resolution\n\n- Knowledge says interaction, structure says implausible (e.g. descriptors\u002FADMET inconsistent with the claimed pathway): report both, flag the conflict, prefer the FDA-label\u002Fclinical-study tier and note the structural caveat.\n- Structure suggests a pathway with no DDI record: label it theoretical (★☆☆); recommend monitoring, not a hard verdict.\n\n## Common Mistakes\n\n- Letting this skill duplicate `tooluniverse-drug-drug-interaction` - delegate the knowledge reasoning, add only the structure layer.\n- Calling `glide_dock` without first running `glide_grid` - it needs the grid `.zip` (and `glide_grid` needs a box center: coords or `ligand_molnum`).\n- Treating computed ADMET\u002Fdescriptors as clinical evidence - they are supporting (★☆☆), never override FDA labels.\n- Burning license tokens on Schrodinger drivers (`ligprep`\u002F`qikprop_admet`\u002F`glide_dock`, long `-WAIT` timeouts) when the knowledge pass already answers the question.\n\n## Related Skills\n\n- `ddi-admet-research` - knowledge-only ToolUniverse front-end \u002F router.\n- `schrodinger-maestro-driver` - the structure MCP details and the full docking pipeline.\n- `tooluniverse-drug-drug-interaction` - the deep DDI reasoning framework this orchestrator delegates to.\n","# Mechanistic DDI Pipeline (cross-stack orchestrator)\n\n## Overview\n\nThis skill is the GLUE between two servers. It fuses ToolUniverse knowledge (CYP\u002FUGT\u002Ftransporter roles, DDI records, FDA labels, literature) with Schrodinger structure-level computation (3D generation, drug-likeness descriptors, QikProp ADMET, optional Glide docking) into one perpetrator-victim DDI verdict. It does not replace the deep `tooluniverse-drug-drug-interaction` skill - it adds the structure layer.\n\nStacks used:\n\n- `tooluniverse` MCP (knowledge) - see the `ddi-admet-research` and `tooluniverse-drug-drug-interaction` skills for routing and tool details.\n- `schrodinger` MCP (structure) - see the `schrodinger-maestro-driver` skill for the 67 tools, the license-token split, and the full prep -> grid -> dock -> MM-GBSA pipeline (`glide_grid` builds the grid in-server).\n\n## When to Use\n\n- A DDI question where mechanism must be backed by both pathway knowledge AND molecular evidence (descriptors, ADMET, or a docking pose into a CYP\u002Ftarget structure).\n- A novel or under-documented compound (limited DDI records) where structure-derived properties inform the prediction.\n- A case where you must reconcile a knowledge-base claim with computed physchem\u002FADMET (e.g., does the structure support the proposed CYP-substrate role).\n\nIf the question is purely knowledge-based, use `ddi-admet-research` instead - do not spin up structure tools unnecessarily.\n\n## Orchestrated Workflow\n\n1. Frame the pair with the perpetrator-victim model (who changes whom). Apply the CYP\u002FUGT reasoning from `tooluniverse-drug-drug-interaction` BEFORE any tool call.\n2. Knowledge pass (ToolUniverse): resolve identifiers, then query CYP\u002Ftransporter roles (`ChEMBL_get_drug_mechanisms`), known interactions (`drugbank_get_drug_interactions_by_drug_name_or_id`), FDA label evidence (`DailyMed_get_spl_by_setid`), and literature (`PubMed_search_articles`).\n3. Structure pass (Schrodinger): for each drug, compute the drug-likeness panel (`molecule_properties(smiles)`); generate 3D where needed (`smiles_to_3d`); run `qikprop_admet` on a prepared structure for ADMET\u002Fphyschem that supports or challenges the predicted pathway.\n4. Optional docking corroboration: prep the receptor (`prepwizard`) and build a grid (`glide_grid`) for a CYP isoform\u002Ftarget, then dock the perpetrator\u002Fvictim with `glide_dock` to test the proposed binding\u002Finhibition hypothesis, optionally rescoring with `prime_mmgbsa`. No receptor structure on hand = skip docking (see `structure-based-screening`).\n5. Synthesize the verdict: combine mechanism (PK via CYP\u002FUGT\u002Ftransporter, PD overlap), severity (Contraindicated\u002FMajor\u002FModerate\u002FMinor), direction (inhibitor up \u002F inducer down), and structure-derived support into a single ranked conclusion with management + monitoring.\n6. Grade every claim: FDA label (★★★) > clinical study (★★☆) > theoretical\u002Fstructure-inferred (★☆☆). Mark structure-derived inferences as supporting, not definitive.\n\n## Conflict Resolution\n\n- Knowledge says interaction, structure says implausible (e.g. descriptors\u002FADMET inconsistent with the claimed pathway): report both, flag the conflict, prefer the FDA-label\u002Fclinical-study tier and note the structural caveat.\n- Structure suggests a pathway with no DDI record: label it theoretical (★☆☆); recommend monitoring, not a hard verdict.\n\n## Common Mistakes\n\n- Letting this skill duplicate `tooluniverse-drug-drug-interaction` - delegate the knowledge reasoning, add only the structure layer.\n- Calling `glide_dock` without first running `glide_grid` - it needs the grid `.zip` (and `glide_grid` needs a box center: coords or `ligand_molnum`).\n- Treating computed ADMET\u002Fdescriptors as clinical evidence - they are supporting (★☆☆), never override FDA labels.\n- Burning license tokens on Schrodinger drivers (`ligprep`\u002F`qikprop_admet`\u002F`glide_dock`, long `-WAIT` timeouts) when the knowledge pass already answers the question.\n\n## Related Skills\n\n- `ddi-admet-research` - knowledge-only ToolUniverse front-end \u002F router.\n- `schrodinger-maestro-driver` - the structure MCP details and the full docking pipeline.\n- `tooluniverse-drug-drug-interaction` - the deep DDI reasoning framework this orchestrator delegates to.\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5","reconcile-flags\u002Fimport-2026-08-28\u002Ftoolchain-root\u002Fskills\u002Fmechanistic-ddi-pipeline","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill mechanistic-ddi-pipeline",{"tool":63,"command":64},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill mechanistic-ddi-pipeline",{"tool":66,"command":67},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill mechanistic-ddi-pipeline","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Freconcile-flags\u002Fimport-2026-08-28\u002Ftoolchain-root\u002Fskills\u002Fmechanistic-ddi-pipeline\u002FSKILL.md",true,[],[72,93,119,146,160,176],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":46},"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",[],[79,80,81,82,83,84,85,86,87,29,88,89,90,91,92],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":100,"publishedAt":46},"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",[99],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[101,102,103,104,105,106,107,108,109,110,111,112,113,114,29,115,116,117,118,38],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do",{"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":46},"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":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":129,"categoryName":130,"taxonomyType":21,"taxonomyTypeDesc":22},"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":46},"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":46},"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":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[162,168,84,169,170,171,29,172,173,174,175],"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":46},"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",[],[153,155,117,182,157,183],"files","interactive"]