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Learning","资料检索、知识整理与学习辅导",5531,[],{"records":101,"total":7,"current":16,"size":194},[102,114,123,133,143,153,163,172,182],{"accountNo":103,"login":104,"displayName":105,"avatarUrl":106,"accountType":107,"accountTypeDesc":108,"profileDescription":109,"skillCount":110,"repoCount":111,"professionCount":5,"scenarioCount":6,"totalStars":112,"updatedAt":113},"01M698VYDXZ6JW5WH3CRV650R8","microsoft","Microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","ORGANIZATION","组织","Open source projects and samples from Microsoft",4160,229,828772,"2026-11-13 04:10:52",{"accountNo":115,"login":116,"displayName":116,"avatarUrl":117,"accountType":107,"accountTypeDesc":108,"profileDescription":118,"skillCount":119,"repoCount":120,"professionCount":6,"scenarioCount":6,"totalStars":121,"updatedAt":122},"01M698P3FSAZ6B4R928ZF19KPT","openclaw","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252820863?v=4","Your personal, open source AI assistant.",362,32,437711,"2026-11-13 03:02:05",{"accountNo":124,"login":125,"displayName":126,"avatarUrl":127,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":128,"repoCount":129,"professionCount":6,"scenarioCount":130,"totalStars":131,"updatedAt":132},"01M6AC29YGCR4S2NTJFV0G8B81","openai","OpenAI","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F14957082?v=4",171,23,6,342695,"2026-11-13 04:03:41",{"accountNo":134,"login":135,"displayName":136,"avatarUrl":137,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":138,"repoCount":139,"professionCount":140,"scenarioCount":6,"totalStars":141,"updatedAt":142},"01M6AC83E6K4FDKK7HZ8WNMQKE","apache","The Apache Software Foundation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F47359?v=4",420,75,8,298176,"2026-11-13 03:14:28",{"accountNo":144,"login":145,"displayName":146,"avatarUrl":147,"accountType":107,"accountTypeDesc":108,"profileDescription":148,"skillCount":149,"repoCount":150,"professionCount":5,"scenarioCount":6,"totalStars":151,"updatedAt":152},"01M698VYEFDM0VJBZDYYJ2T83S","google","Google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","Google ❤️ Open Source",535,48,242366,"2026-11-13 04:17:09",{"accountNo":154,"login":155,"displayName":156,"avatarUrl":157,"accountType":107,"accountTypeDesc":108,"profileDescription":158,"skillCount":159,"repoCount":160,"professionCount":5,"scenarioCount":6,"totalStars":161,"updatedAt":162},"01M6AC83DS133TPXFSP1DHNCC6","github","GitHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","How people build software.",940,28,238441,"2026-11-13 03:57:35",{"accountNo":164,"login":165,"displayName":166,"avatarUrl":167,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":168,"repoCount":169,"professionCount":5,"scenarioCount":6,"totalStars":170,"updatedAt":171},"01M697XGDPXJAVQ8391V2BCYTE","anthropics","Anthropic","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4",674,17,231826,"2026-11-13 04:18:26",{"accountNo":173,"login":174,"displayName":175,"avatarUrl":176,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":177,"repoCount":178,"professionCount":179,"scenarioCount":6,"totalStars":180,"updatedAt":181},"01M6AC83EPDGBFN19MWYRREJD0","Significant-Gravitas","AutoGPT","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130738209?v=4",86,2,10,188014,"2026-11-13 02:38:59",{"accountNo":183,"login":184,"displayName":185,"avatarUrl":186,"accountType":187,"accountTypeDesc":188,"profileDescription":189,"skillCount":190,"repoCount":191,"professionCount":130,"scenarioCount":6,"totalStars":192,"updatedAt":193},"01M6DJCCDDG53MTYYRTXAXFG17","garrytan","Garry Tan","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F19957?v=4","USER","个人作者","Writes software, dreams. ",324,3,174614,"2026-11-13 04:05:07",9,{"records":196,"total":4,"current":577,"size":578},[197,233,253,280,300,321,334,354,375,391,410,429,449,484,507,524,548,555],{"skillNo":198,"name":199,"nameZh":18,"description":200,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":209,"categories":210,"keywords":211,"publishedAt":232},"01M6XFAG7YMR3WQ145T1QETJPJ","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneuropixels-analysis\u002FSKILL.md",[],[212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231],"analysis","neuropixels analysis","spike","metrics","data","open","use","recordings","Neural","motion","visual","synthetic","unit","biology","sorting","curation","recording","load","for","when","2026-11-10 10:12:30",{"skillNo":234,"name":235,"nameZh":18,"description":236,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":237,"categories":238,"keywords":239,"publishedAt":232},"01M6XFAAHV9P6C935WT0Q6FV9F","neurokit2","Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002FSKILL.md",[],[235,240,241,242,243,212,244,218,245,246,247,248,223,225,249,250,251,230,252,231],"research","assessment","toolkit","integration","system","complexity","biosignal","signals","brain","processing","multi","event","signal",{"skillNo":254,"name":255,"nameZh":18,"description":256,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":257,"categories":258,"keywords":263,"publishedAt":232},"01M6XF9VQ2WP34FQGA55RC68GA","molecular-cloning","Molecular cloning simulation and design. PCR amplicon prediction, restriction enzyme digestion, Golden Gate and Gibson assembly simulation, primer design, CRISPR sgRNA design, and plasmid annotation. For protein-level sequence analysis use biopython or esm; for database lookups use gene-database or ensembl-database.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fmolecular-cloning\u002FSKILL.md",[259,262],{"categoryNo":260,"categoryName":261,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":21,"categoryName":22,"taxonomyType":11,"taxonomyTypeDesc":12},[264,265,266,267,268,269,270,212,271,218,272,273,274,225,275,230,276,277,278,279],"cloning","molecular","molecular cloning","design","database","PCR","biopython","simulation","gate","crispr","primer","primer design","gene","sequence","prediction","Protein",{"skillNo":281,"name":282,"nameZh":18,"description":283,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":284,"categories":285,"keywords":286,"publishedAt":232},"01M6XF9MXYQ9MB2A375JFHEY59","microbial-dynamics","Microbial population dynamics modeling and analysis. Bacterial growth curve fitting (logistic, Gompertz, Baranyi), Lotka-Volterra community dynamics, Gillespie stochastic simulation, biofilm quantification, CFU enumeration, and genome annotation. For metabolic modeling use cobrapy; for sequence analysis use biopython.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fmicrobial-dynamics\u002FSKILL.md",[],[287,288,289,270,290,212,271,291,218,292,293,294,295,296,297,225,230,298,277,299],"dynamics","microbial dynamics","enumeration","cobrapy","growth","community","modeling","genome annotation","annotation","quantification","sequence analysis","curve","Curve Fitting",{"skillNo":301,"name":302,"nameZh":18,"description":303,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":304,"categories":305,"keywords":306,"publishedAt":232},"01M6XF8PXSY18GDMV6Q8TYEQFE","immunology-assays","Computational analysis of immunology experimental data. ATAC-seq differential accessibility, immune cell tracking from microscopy, ELISA data processing with 4-parameter logistic fitting, immunohistochemistry quantification, antibody titer analysis, and cell cycle phase duration estimation. For flow cytometry use flow-cytometry-analysis; for scRNA-seq use scanpy.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fimmunology-assays\u002FSKILL.md",[],[307,308,309,310,216,212,311,312,218,313,314,315,316,317,225,249,318,319,230,320],"immunology","immunology assays","accessibility","flow","scanpy","data processing","Immune","cycle","estimation","computational","atac","with","seq","phase",{"skillNo":322,"name":323,"nameZh":18,"description":324,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":325,"categories":326,"keywords":327,"publishedAt":232},"01M6XF88PGFRCFPTKK88PGS2ER","glycobiology","Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fglycobiology\u002FSKILL.md",[],[323,328,268,212,329,218,330,331,332,333,225,230,278,279],"python","site","queries","structure","alphafold","resources",{"skillNo":335,"name":336,"nameZh":18,"description":337,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":338,"categories":339,"keywords":340,"publishedAt":232},"01M6XF7E76RTMCNTF1JQ56B69Z","flow-cytometry-analysis","Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual\u002Fautomated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fflow-cytometry-analysis\u002FSKILL.md",[],[310,212,341,342,343,344,345,346,218,347,314,348,349,350,225,318,351,230,352,353],"flow cytometry analysis","workflows","file","pipeline","flowio","parsing","only","cell","file handling","complete","manual","gating","compensation",{"skillNo":355,"name":356,"nameZh":18,"description":357,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":358,"categories":359,"keywords":363,"publishedAt":232},"01M6XF71APXC3X5FP7117KNW1X","esm","Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fesm\u002FSKILL.md",[360],{"categoryNo":361,"categoryName":362,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000010","后端与接口",[356,267,364,365,366,270,367,368,331,369,370,371,372,223,225,277,373,231,374,278],"deep learning","tasks","forge","api","model","across","local","deep","proteins","generative","sequences",{"skillNo":376,"name":377,"nameZh":18,"description":378,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":379,"categories":380,"keywords":381,"publishedAt":232},"01M6XF6D6ECCEX8N18HW7M7ZCG","curated-bio-datasets","Guide to accessing curated biological datasets for computational biology. COSMIC cancer data, GTEx expression, GWAS catalog, GeneBass exome variants, BioGRID interactions, MSigDB gene sets, DisGeNET disease-gene associations, and GO ontology. For specific database APIs use individual database skills (cosmic-database, gwas-database, etc.).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fcurated-bio-datasets\u002FSKILL.md",[],[382,383,384,268,216,385,386,218,387,388,316,389,225,230,276,390],"datasets","curated bio datasets","go","catalog","guide","ontology","computational biology","expression","variants",{"skillNo":392,"name":393,"nameZh":18,"description":394,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":395,"categories":396,"keywords":397,"publishedAt":232},"01M6XF5NJJQXSYXGFK9PJSD6E3","clinical-imaging","Clinical and physiological imaging analysis. Diffusion MRI ADC maps, micro-CT bone morphometry, hemodynamic parameter analysis, circadian rhythm cosinor analysis, ciliary beat frequency (FFT), and tissue deformation optical flow. For DICOM file handling use pydicom; for biosignals use neurokit2.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fclinical-imaging\u002FSKILL.md",[],[398,399,400,343,401,310,212,235,402,218,403,349,404,225,405,406,407,408,230,409],"clinical","imaging","clinical imaging","micro","pydicom","maps","diffusion","bone","MRI","CT","FFT","Frequency",{"skillNo":411,"name":412,"nameZh":18,"description":413,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":414,"categories":415,"keywords":416,"publishedAt":232},"01M6XF56RXMBTH205NFV1Y1NYR","cancer-genomics-analysis","Computational cancer genomics workflows. Somatic mutation detection and annotation, structural variation characterization, copy number analysis, tumor purity\u002Fploidy estimation, NMF metagene extraction, and DNA damage response network analysis. For cancer mutation databases use cosmic-database; for variant clinical significance use clinvar-database.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fcancer-genomics-analysis\u002FSKILL.md",[],[212,417,418,342,268,419,420,315,421,422,316,423,398,424,295,425,225,426,427,428],"genomics","cancer genomics analysis","network","databases","mutation","DNA","structural","variant clinical significance","nmf","network analysis","clinvar","number",{"skillNo":430,"name":431,"nameZh":18,"description":432,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":433,"categories":434,"keywords":438,"publishedAt":232},"01M6XF4MHKYRFNGHG3AXVR641T","bioimage-analysis","Microscopy image analysis for cell biology. Cell segmentation (Cellpose, watershed), object tracking (trackpy), morphology quantification, colony counting, colocalization analysis, and cytoskeleton characterization. For pathology WSI use pathml; for flow cytometry use flow-cytometry-analysis.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fbioimage-analysis\u002FSKILL.md",[435],{"categoryNo":436,"categoryName":437,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000016","图像与多媒体",[212,439,440,310,441,442,443,218,444,348,296,445,446,225,447,230,448],"bioimage analysis","image","segmentation","pathml","tracking","cell biology","counting","image analysis","object","pathology",{"skillNo":450,"name":451,"nameZh":18,"description":452,"descriptionZh":18,"repositoryFullName":453,"ownerLogin":454,"ownerAvatarUrl":455,"ownerAccountNo":456,"ownerDisplayName":18,"repositoryNo":457,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":458,"categories":459,"keywords":464,"publishedAt":232},"01M6M41QDJTHQTHPCR2YPE8WBG","sepia","Make AI-generated writing read as human-written, in fiction and in professional prose. Repairs the narrative architecture of fiction and stories (based on StoryScope, arXiv:2604.03136); routes professional text through domain rules for release notes, announcements, PR and issue replies, code-review comments, incident postmortems, tickets, work orders, technical articles, and blog posts. Four operations - write, review (diagnose AI tells without editing), refactor (minimal in-place edits), recreate (full rewrite). Use when asked to humanize, de-AI, unslop, or strip AI flavor from any text; when writing or revising any of these document types; or whenever output must not read as machine-written.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fsepia\u002FSKILL.md",[460,463],{"categoryNo":461,"categoryName":462,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":35,"categoryName":36,"taxonomyType":11,"taxonomyTypeDesc":12},[451,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,231],"writing","review","architecture","notes","release notes","code","code-review","unslop","text","work","generated","humanize","incident","human","de","professional","narrative","types","posts",{"skillNo":485,"name":486,"nameZh":18,"description":487,"descriptionZh":18,"repositoryFullName":453,"ownerLogin":454,"ownerAvatarUrl":455,"ownerAccountNo":456,"ownerDisplayName":18,"repositoryNo":457,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":488,"categories":489,"keywords":490,"publishedAt":232},"01M6M40M1S6F6R7VQ89YD36TES","pua-ja","Japanese version. Forces exhaustive problem-solving with corporate PUA rhetoric and structured debugging. Trigger when: (1) task failed 2+ times or stuck tweaking same approach; (2) about to say 'I cannot', suggest manual work, or blame environment without verifying; (3) being passive—not searching, not reading source, just waiting; (4) user frustration: 'try harder', 'stop giving up', 'もっと頑張れ', 'なんでまた失敗したの', 'なんとかしろ', or similar. Also for complex debugging, env issues, config\u002Fdeployment failures. All task types: code, config, research, writing, deployment, infra, API. Do NOT trigger on first-attempt failures or when a known fix is executing.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fpua-ja\u002FSKILL.md",[],[491,492,465,493,494,495,496,497,498,499,500,501,502,503,504,505,506,482,351,230,231],"pua","pua ja","deployment","debugging","issues","just","fix","environment","version","up","japanese","do","problem-solving","infra","trigger","problem",{"skillNo":508,"name":509,"nameZh":18,"description":510,"descriptionZh":18,"repositoryFullName":453,"ownerLogin":454,"ownerAvatarUrl":455,"ownerAccountNo":456,"ownerDisplayName":18,"repositoryNo":457,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":511,"categories":512,"keywords":513,"publishedAt":232},"01M6M40994JH8MXBP1SRFEK4AQ","pua-en","Forces exhaustive problem-solving with corporate PUA rhetoric and structured debugging. Trigger when: (1) task failed 2+ times or stuck tweaking same approach; (2) about to say 'I cannot', suggest manual work, or blame environment without verifying; (3) being passive—not searching, not reading source, just waiting; (4) user frustration: 'try harder', 'stop giving up', 'figure it out', 'again???', or similar. Also for complex debugging, env issues, config\u002Fdeployment failures. All task types: code, config, research, writing, deployment, infra, API. Do NOT trigger on first-attempt failures or when a known fix is executing.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fpua-en\u002FSKILL.md",[],[514,491,515,465,240,516,493,517,497,518,519,502,503,504,520,506,521,522,523,230],"en","pua en","config","user","say","stop","env","about","reading","corporate",{"skillNo":525,"name":526,"nameZh":18,"description":527,"descriptionZh":18,"repositoryFullName":453,"ownerLogin":454,"ownerAvatarUrl":455,"ownerAccountNo":456,"ownerDisplayName":18,"repositoryNo":457,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":528,"sourceHtmlUrl":529,"categories":530,"keywords":533,"publishedAt":232},"01M6M3Z1QE5YY3S0FH3RZEANR9","humanizer","Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes inflated symbolism, promotional language, vague attribution, em dash overuse, rule of three, AI vocabulary, passive voice, negative parallelisms, and filler.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer\u002FSKILL.md",[531,532],{"categoryNo":461,"categoryName":462,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":35,"categoryName":36,"taxonomyType":11,"taxonomyTypeDesc":12},[526,465,470,534,535,473,536,537,538,539,475,540,541,542,478,543,544,545,546,547,231],"em dash","attribution","editing","read","write","edit","remove","opencode","three","make","more","fixes","vague","dash",{"skillNo":549,"name":451,"nameZh":18,"description":452,"descriptionZh":18,"repositoryFullName":453,"ownerLogin":454,"ownerAvatarUrl":455,"ownerAccountNo":456,"ownerDisplayName":18,"repositoryNo":457,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":550,"categories":551,"keywords":554,"publishedAt":232},"01M6M3RYAKY9EWA6SFKP5S01NR","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-Craft\u002Fsepia\u002FSKILL.md",[552,553],{"categoryNo":461,"categoryName":462,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":35,"categoryName":36,"taxonomyType":11,"taxonomyTypeDesc":12},[451,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,231],{"skillNo":556,"name":557,"nameZh":18,"description":558,"descriptionZh":18,"repositoryFullName":453,"ownerLogin":454,"ownerAvatarUrl":455,"ownerAccountNo":456,"ownerDisplayName":18,"repositoryNo":457,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":559,"categories":560,"keywords":561,"publishedAt":232},"01M6M34GDSQJAMT3J7N473MAFM","gsap-utils","Official GSAP skill for gsap.utils — clamp, mapRange, normalize, interpolate, random, snap, toArray, wrap, pipe. Use when the user asks about gsap.utils, clamp, mapRange, random, snap, toArray, wrap, or helper utilities in GSAP.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWeb-Design\u002Ffrontend-and-motion-implementation\u002Fopen-design-gsap-utils\u002FSKILL.md",[],[562,563,564,155,565,566,567,568,218,221,569,570,571,572,573,521,574,575,230,576,231],"gsap","utils","gsap utils","prototype","official","https","Animation","wrap","pipe","normalize","helper","Utilities","random","snap","value",10826,18]