[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"creator:01M6EB0FZZ4BPQPC4HET4QY6NM":3,"creator-list:01M6EB0FZZ4BPQPC4HET4QY6NM:stars:1:":23},{"accountNo":4,"login":5,"displayName":6,"avatarUrl":7,"accountType":8,"accountTypeDesc":9,"profileDescription":10,"skillCount":11,"repoCount":12,"professionCount":13,"scenarioCount":14,"totalStars":15,"updatedAt":16,"htmlUrl":17,"blogUrl":18,"company":19,"location":20,"followersCount":21,"publicReposCount":22},"01M6EB0FZZ4BPQPC4HET4QY6NM","levalencia","Luis Valencia ","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6962857?v=4","USER","个人作者","Microsoft MVP - Artificial Intelligence\r\nMachine Learning Enthusiast\r\n",431,1,10,7,3,"2026-05-02 06:55:36","https:\u002F\u002Fgithub.com\u002Flevalencia","https:\u002F\u002Fmedium.com\u002F@luisevalencia",null,"Brussels",37,17,{"records":24,"total":11,"current":12,"size":462},[25,51,89,116,142,170,203,230,246,274,294,310,333,353,374,398,417,438],{"skillNo":26,"name":27,"nameZh":19,"description":28,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":32,"categories":33,"keywords":34,"publishedAt":50},"01M6EM8ES5M9J4S8N78NJST0YC","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","levalencia\u002Fagent-god-mode","01M6EB0G4HWHPWW0H9X7QKSAHP","MIT","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzarr-python\u002FSKILL.md",[],[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"python","zarr python","storage","integration","s3","gcs","cloud","dask","parallel","pipelines","arrays","numpy","scientific computing","scale","scientific","2026-11-10 10:12:30",{"skillNo":52,"name":53,"nameZh":19,"description":54,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":55,"categories":56,"keywords":70,"publishedAt":50},"01M6EM8AR6ZFKZ9YVW8TTG850Z","zabbix","Zabbix monitoring system automation via API and Python. Use when: (1) Managing hosts, templates, items, triggers, or host groups, (2) Automating monitoring configuration, (3) Sending data via Zabbix trapper\u002Fsender, (4) Querying historical data or events, (5) Bulk operations on Zabbix objects, (6) Maintenance window management, (7) User\u002Fpermission management","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzabbix-api-skill\u002FSKILL.md",[57,62,65],{"categoryNo":58,"categoryName":59,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":63,"categoryName":64,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":66,"categoryName":67,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[53,35,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88],"automation","window management","monitoring","data","templates","api","system","operations","use","maintenance","objects","permission","bulk operations","triggers","host","events","via","bulk",{"skillNo":90,"name":91,"nameZh":19,"description":92,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":50},"01M6EM6Q9JG846ECQY9A210A26","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fx-twitter-growth\u002FSKILL.md",[],[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115],"twitter","x twitter growth","growth","competitive intelligence","search","analyzing","plan","marketing","web search","competitors","analyze","write","optimize","social","intelligence","viral","growth engine","mechanics","grow","competitive",{"skillNo":117,"name":118,"nameZh":19,"description":119,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":120,"categories":121,"keywords":122,"publishedAt":50},"01M6EM3FQNPYNKPC4CV9TCVNGD","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fwhat-if-oracle\u002FSKILL.md",[],[123,124,125,126,127,128,129,130,131,132,133,134,79,135,136,137,138,139,140,141],"oracle","what if oracle","test","planning","ux","futures","risk","analysis","explore","decision","branch","question","exploration","options","stress-test","strategies","trigger","case","multi",{"skillNo":143,"name":144,"nameZh":19,"description":145,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":146,"categories":147,"keywords":151,"publishedAt":50},"01M6EM08C06RGDYCJ86HAPK0FF","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvercel-react-best-practices\u002FSKILL.md",[148],{"categoryNo":149,"categoryName":150,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000009","前端开发",[152,153,154,155,156,157,158,159,160,161,162,163,164,74,165,166,167,168,169,84],"react","vercel react best practices","best practices","react best practices","vercel","writing","performance","components","react components","guidelines","tasks","code","Optimization","Next.js","refactoring","performance optimization","next","pages",{"skillNo":171,"name":172,"nameZh":19,"description":173,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":174,"categories":175,"keywords":186,"publishedAt":50},"01M6EM00S13326QM08DTM94J6Z","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvenue-templates\u002FSKILL.md",[176,179,182,183],{"categoryNo":177,"categoryName":178,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":180,"categoryName":181,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":63,"categoryName":64,"taxonomyType":60,"taxonomyTypeDesc":61},{"categoryNo":184,"categoryName":185,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000036","内容创作者",[75,187,188,161,189,190,191,192,193,194,195,107,196,197,198,199,200,201,49,202],"venue templates","research","NeurIPS","ICML","LaTeX","science","Bash","access","read","edit","proposals","Academic","requirements","formatting","journal","chi",{"skillNo":204,"name":205,"nameZh":19,"description":206,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":207,"categories":208,"keywords":212,"publishedAt":50},"01M6EKZFQYMA6F057QPWHPFGEW","vaex","Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV\u002FHDF5\u002FArrow\u002FParquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvaex\u002FSKILL.md",[209],{"categoryNo":210,"categoryName":211,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000013","分析与可视化",[205,213,214,215,216,217,218,219,101,74,220,221,222,223,224,225,226,227,228,229],"memory","machine learning","core","create","visualization","dataframe","Evaluation","in memory","visualizations","learning","work","files","do","statistics","fit","processing","ML Pipelines",{"skillNo":231,"name":232,"nameZh":19,"description":233,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":234,"categories":235,"keywords":237,"publishedAt":50},"01M6EKYGWR4MZ13TM3HADZ69S9","usfiscaldata","Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fusfiscaldata\u002FSKILL.md",[236],{"categoryNo":58,"categoryName":59,"taxonomyType":60,"taxonomyTypeDesc":61},[232,238,74,76,194,239,79,240,241,242,243,226,244,245],"savings","tracking","daily","datasets","query","exchange","monthly","revenue",{"skillNo":247,"name":248,"nameZh":19,"description":249,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":250,"categories":251,"keywords":258,"publishedAt":50},"01M6EKRQ04HP7G25NK69G7JF7T","treatment-plans","Generate concise (3-4 page), focused medical treatment plans in LaTeX\u002FPDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Ftreatment-plans\u002FSKILL.md",[252,255],{"categoryNo":253,"categoryName":254,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":256,"categoryName":257,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000037","律师与法务",[259,260,261,262,263,193,264,265,195,107,196,266,267,268,269,270,200,271,272,273],"treatment plans","plans","PDF","generate","Citations","text","page","evidence","goal","general","frameworks","mental health","medical","clinical","professional",{"skillNo":275,"name":276,"nameZh":19,"description":277,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":278,"categories":279,"keywords":280,"publishedAt":50},"01M6EKQQY7XQDXB6SHVXXY3PTZ","torch-geometric","Graph Neural Networks (PyG). Node\u002Fgraph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Ftorch-geometric\u002FSKILL.md",[],[281,282,283,284,285,286,222,287,288,289,290,291,292,293],"torch geometric","deep learning","neural networks","graph","networks","classification","node","Neural","deep","graphs","molecular","gat","link",{"skillNo":295,"name":296,"nameZh":19,"description":297,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":298,"categories":299,"keywords":300,"publishedAt":50},"01M6EKNX9FHR02BFHR82FYYY9E","tiledbvcf","Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF\u002FBCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Ftiledbvcf\u002FSKILL.md",[],[296,37,301,74,43,302,303,304,305,306,307,308,309],"capabilities","export","ingestion","queries","sample","genomics","vcf","retrieval","Scalable",{"skillNo":311,"name":312,"nameZh":19,"description":313,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":314,"categories":315,"keywords":316,"publishedAt":50},"01M6EKE5JWDNSWDNMQR1E1WY3A","strategic-alignment","Cascades strategy from boardroom to individual contributor. Detects and fixes misalignment between company goals and team execution. Covers strategy articulation, cascade mapping, orphan goal detection, silo identification, communication gap analysis, and realignment protocols. Use when teams are pulling in different directions, OKRs don't connect, departments optimize locally at company expense, or when user mentions alignment, strategy cascade, silo, conflicting OKRs, or strategy communication.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fstrategic-alignment\u002FSKILL.md",[],[317,318,319,320,321,322,323,324,130,325,326,327,79,267,328,108,329,330,331,332],"strategic alignment","alignment","teams","connect","communication","protocols","playbook","execution","team","expense","strategy","goals","orphan","gap","gap analysis","identification",{"skillNo":334,"name":335,"nameZh":19,"description":336,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":337,"categories":338,"keywords":342,"publishedAt":50},"01M6EKD52VJCTB8FZ4BPPHARRY","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fstatistical-analysis\u002FSKILL.md",[339],{"categoryNo":340,"categoryName":341,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000008","测试与质量",[130,343,188,125,344,345,74,346,347,79,348,349,350,351,198,352],"statistical analysis","help","reporting","statsmodels","results","power","models","guidance","selection","tests",{"skillNo":354,"name":355,"nameZh":19,"description":356,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":357,"categories":358,"keywords":359,"publishedAt":50},"01M6EKCSREQ3Y3AZXS7BRK57YV","stable-baselines3","Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fstable-baselines3\u002FSKILL.md",[],[360,158,361,362,363,364,365,366,76,222,43,79,367,368,369,370,371,372,373,141],"stable baselines3","training","learn","reinforcement learning","vectorized","PPO","pufferlib","ready","rl","scikit-learn","reinforcement","systems","high-performance","prototyping",{"skillNo":375,"name":376,"nameZh":19,"description":377,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":378,"categories":379,"keywords":387,"publishedAt":50},"01M6EK992VNZVJXR86P0A9P45R","social-media-manager","When the user wants to develop social media strategy, plan content calendars, manage community engagement, or grow their social presence across platforms. Also use when the user mentions 'social media strategy,' 'social calendar,' 'community management,' 'social media plan,' 'grow followers,' 'engagement rate,' 'social media audit,' or 'which platforms should I use.' For writing individual social posts, see social-content. For analyzing social performance data, see social-media-analyzer.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fsocial-media-manager\u002FSKILL.md",[380,383,386],{"categoryNo":381,"categoryName":382,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":384,"categoryName":385,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000035","市场营销",{"categoryNo":184,"categoryName":185,"taxonomyType":68,"taxonomyTypeDesc":69},[388,389,390,109,157,158,391,101,74,392,102,103,327,79,393,394,395,396,114,397],"Media","social media","social media manager","audit","social-media","calendar","manage","community","across","social media audit",{"skillNo":399,"name":400,"nameZh":19,"description":401,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":402,"categories":403,"keywords":405,"publishedAt":50},"01M6EK63RFB2716VFN5NSGBD2E","simpy","Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fsimpy\u002FSKILL.md",[404],{"categoryNo":63,"categoryName":64,"taxonomyType":60,"taxonomyTypeDesc":61},[400,35,406,407,408,77,78,409,79,410,411,412,371,413,86,414,415,416],"framework","network","simulation","process","time","shared","service","as","resources","manufacturing","processes",{"skillNo":418,"name":419,"nameZh":19,"description":420,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":31,"licenseFileUrl":19,"sourceHtmlUrl":421,"categories":422,"keywords":424,"publishedAt":50},"01M6EK3H9J7Z2P4VYFP8RRMBT8","shap","Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fshap\u002FSKILL.md",[423],{"categoryNo":63,"categoryName":64,"taxonomyType":60,"taxonomyTypeDesc":61},[419,425,214,282,426,427,428,429,101,222,430,79,289,431,432,433,434,435,436,437],"tensorflow","pytorch","lightgbm","debugging","linear","model","bias","feature","black","random forest","predictions","machine","Interpretability",{"skillNo":439,"name":440,"nameZh":19,"description":441,"descriptionZh":19,"repositoryFullName":29,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":30,"starsCount":15,"licenseSpdxId":442,"licenseFileUrl":19,"sourceHtmlUrl":443,"categories":444,"keywords":445,"publishedAt":50},"01M6EJW6YBE67RJASPVZ4H0BS6","scvelo","RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced\u002Fspliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy\u002FscVI-tools for trajectory inference.","BSD-3-Clause","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fscvelo\u002FSKILL.md",[],[440,446,447,74,448,130,449,450,451,410,452,453,454,455,456,457,458,459,460,461],"inference","state","Tools","scanpy","velocity","compute","transitions","RNA","cell","infer","single-cell","trajectory","scvi","dynamics","driver","rna-seq",18]