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Use when working with quantum algorithms, simulations, or quantum hardware including (1) Building quantum circuits with gates and measurements, (2) Running quantum algorithms (VQE, QAOA, Grover), (3) Transpiling\u002Foptimizing circuits for hardware, (4) Executing on IBM Quantum or other providers, (5) Quantum chemistry and materials science, (6) Quantum machine learning, (7) Visualizing circuits and results, or (8) Any quantum computing development task.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqiskit\u002FSKILL.md",[],[467,472,473,423,474,447,475,476,477,478,236,479,428,480,481,482,483,242,484,463,485,464],"machine learning","development","quantum chemistry","ibm","Providers","science","learning","task","Materials Science","gates","machine","quantum algorithms","materials","running",{"skillNo":487,"name":488,"nameZh":13,"description":489,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":490,"categories":491,"keywords":493,"publishedAt":244},"01M6AFM1JTA5QHNKKSWAYADCYM","pytorch-lightning","Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU\u002FTPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpytorch-lightning\u002FSKILL.md",[492],{"categoryNo":218,"categoryName":219,"taxonomyType":6,"taxonomyTypeDesc":7},[494,495,496,497,498,499,500,501,502,331,503,504,505,506,507,258,508,509,510,511,266,512,513,514,515],"pytorch","lightning","pytorch lightning","implement","tpu","distributed","deep learning","training","code","data pipelines","logging","gpu","DeepSpeed","Distributed Training","data","configure","organize","deep","FSDP","DDP","Callbacks","Scalable",{"skillNo":517,"name":518,"nameZh":13,"description":519,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":520,"categories":521,"keywords":523,"publishedAt":244},"01M6AFKYMEE89AKHW33ZS4JFA6","pytdc","Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpytdc\u002FSKILL.md",[522],{"categoryNo":218,"categoryName":219,"taxonomyType":6,"taxonomyTypeDesc":7},[518,524,525,508,526,527,528,529,406,530,242,531,532,533],"scaffold","ml","discovery","ready","datasets","benchmarks","drug discovery","splits","prediction","toxicity",{"skillNo":535,"name":536,"nameZh":13,"description":537,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":538,"categories":539,"keywords":540,"publishedAt":244},"01M6AFKW0913AAZQ2PC51GY45W","pysam","Genomic file toolkit. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignments, VCF\u002FBCF variants, FASTA\u002FFASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpysam\u002FSKILL.md",[],[536,423,541,508,542,543,544,233,234,545,546,547,548,549,550,551,242,552,553,554],"file","data processing","data processing pipelines","calculate","coverage","NGS","FASTQ","bam","sam","vcf","processing","variants","sequences","FASTA",{"skillNo":556,"name":557,"nameZh":13,"description":558,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":559,"categories":560,"keywords":562,"publishedAt":244},"01M6AFKRDQ324PQ1ZTT39RVPGT","pyopenms","Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS\u002FMS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpyopenms\u002FSKILL.md",[561],{"categoryNo":443,"categoryName":444,"taxonomyType":6,"taxonomyTypeDesc":7},[557,563,446,422,564,541,565,508,566,286,567,568,569,570,571,572,573,551,430,242,574,463,554],"data analysis","experiments","analyzing","detection","ms","quantitative analysis","mass spectrometry","file handling","feature","metabolomics","identification","signal",{"skillNo":576,"name":577,"nameZh":13,"description":578,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":579,"categories":580,"keywords":582,"publishedAt":244},"01M6AFKMTNE52DN0R6RSR7RAN8","pymoo","Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA\u002FD, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpymoo\u002FSKILL.md",[581],{"categoryNo":218,"categoryName":219,"taxonomyType":6,"taxonomyTypeDesc":7},[577,225,331,583,584,529,585,586,242,587,588,589,590,591],"engineering","Optimization","iii","multi","constraint","objective","pareto","NSGA-II","multi-objective",{"skillNo":593,"name":594,"nameZh":13,"description":595,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":596,"categories":597,"keywords":600,"publishedAt":244},"01M6AFKDKKDST88G616H6TZ41A","pymatgen","Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpymatgen\u002FSKILL.md",[598,599],{"categoryNo":396,"categoryName":397,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":218,"categoryName":219,"taxonomyType":6,"taxonomyTypeDesc":7},[594,423,601,477,602,603,262,239,604,605,480,606,607,242,484,608,609],"integration","format","conversion","project","computational","crystal","phase diagrams","phase","Materials Project",{"skillNo":611,"name":612,"nameZh":13,"description":613,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":614,"categories":615,"keywords":617,"publishedAt":244},"01M6AFKA0VK1RA5G8YF4TG5R8X","pylabrobot","Laboratory automation toolkit for controlling liquid handlers, plate readers, pumps, heater shakers, incubators, centrifuges, and analytical equipment. Use this skill when automating laboratory workflows, programming liquid handling robots (Hamilton STAR, Opentrons OT-2, Tecan EVO), integrating lab equipment, managing deck layouts and resources (plates, tips, containers), reading plates, or creating reproducible laboratory protocols. Applicable for both simulated protocols and physical hardware control.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpylabrobot\u002FSKILL.md",[616],{"categoryNo":218,"categoryName":219,"taxonomyType":6,"taxonomyTypeDesc":7},[612,618,619,422,423,620,621,622,236,623,624,625,626,627,628,629,630,242,631,463],"containers","automation","protocols","Reproducible","programming","deck","analytical","STAR","resources","pumps","reading","lab","tips","plate",153,18]