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Set up protein\u002Fsmall molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fchemistry-and-drug-discovery\u002Fmolecular-dynamics\u002FSKILL.md",[],[470,318,471,339,472,473,474,475,476,477,478,479,480,481,482,483,484,485,255,226,486,487,488,489],"molecular dynamics","dynamics","SET","analyze","up","maps","surfaces","biophysics","force fields","structural biology","structural","define","free","binding","biology","trajectories","openmm","energy","Protein","Simulations",{"skillNo":491,"name":492,"nameZh":18,"description":450,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":451,"licenseFileUrl":493,"sourceHtmlUrl":494,"categories":495,"keywords":498,"publishedAt":232},"01M6XF2A5SC632FKMM3ARBKD5C","brand-guidelines-anthropic","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fbrand-guidelines-anthropic\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fbrand-guidelines-anthropic\u002FSKILL.md",[496,497],{"categoryNo":426,"categoryName":427,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":66,"categoryName":67,"taxonomyType":58,"taxonomyTypeDesc":59},[431,432,499,433,356,459,360,221,250,460,461,462,227,231],"brand guidelines anthropic",{"skillNo":501,"name":502,"nameZh":18,"description":503,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":504,"categories":505,"keywords":506,"publishedAt":232},"01M6XF20T70SH59TT3WHQ5HS7K","brainstorming","Use before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fbrainstorming\u002FSKILL.md",[],[502,507,508,509,510,511,221,512,250,513,514,227,515,516,231],"architecture","collaboration","reasoning","work","behavior","creative","ideas","transforms","vague","features",{"skillNo":518,"name":519,"nameZh":18,"description":520,"descriptionZh":18,"repositoryFullName":303,"ownerLogin":304,"ownerAvatarUrl":305,"ownerAccountNo":306,"ownerDisplayName":18,"repositoryNo":307,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":521,"categories":522,"keywords":523,"publishedAt":232},"01M6M03MCRP2T4MXMMBPGRST2M","diffdock","Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB\u002FSMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fchemistry-and-drug-discovery\u002Fdiffdock\u002FSKILL.md",[],[519,524,525,385,526,318,527,483,528,529,530,226,531,321,488,532,533,534,535],"design","virtual","docking","virtual screening","diffusion","pdb","screening","predict","SMILES","Molecular Docking","Protein-Ligand","Drug Design",{"skillNo":537,"name":538,"nameZh":18,"description":539,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":540,"categories":541,"keywords":543,"publishedAt":232},"01M6XF1SV3XAQND8FPV80CMKYH","box-automation","Automate Box operations including file upload\u002Fdownload, content search, folder management, collaboration, metadata queries, and sign requests through Composio's Box toolkit.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fbox-automation\u002FSKILL.md",[542],{"categoryNo":333,"categoryName":334,"taxonomyType":11,"taxonomyTypeDesc":12},[337,544,545,508,379,546,547,548,414,549,415,550,250,416,551,552,553,554,555,227,231],"box","box automation","search","content","file","management","queries","upload","content search","sign","folder","requests",{"skillNo":557,"name":558,"nameZh":18,"description":559,"descriptionZh":18,"repositoryFullName":303,"ownerLogin":304,"ownerAvatarUrl":305,"ownerAccountNo":306,"ownerDisplayName":18,"repositoryNo":307,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":560,"categories":561,"keywords":562,"publishedAt":232},"01M6M03EM9QY58S6X4WGBMWA17","deepchem","Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fchemistry-and-drug-discovery\u002Fdeepchem\u002FSKILL.md",[],[558,563,564,565,312,566,567,568,301,569,221,570,316,317,571,318,255,572,296,226,230,321,322],"workflows","experiments","pytorch","benchmark","graph","first","torchdrug","models","options","quick",{"skillNo":574,"name":575,"nameZh":18,"description":576,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":577,"categories":578,"keywords":579,"publishedAt":232},"01M6XF1KVW47V15DW1JGSBWP2C","blueprint","Turn a one-line objective into a step-by-step construction plan any coding agent can execute cold. Each step has a self-contained context brief — a fresh agent in a new session can pick up any step without reading prior steps.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fblueprint\u002FSKILL.md",[],[580,267,581,582,583,584,585,586,587,361,474,250,588,589,590,591,592,366,593,594,231,595],"Blueprint","coding agent","planning","brief","new session","plan","pick","execute","self","fresh","line","cold","reading","objective","construction plan","new",10850,18]