[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-clickhouse-chdb-datastore-zh-CN":3,"guides-for-clickhouse-chdb-datastore":454,"similar-k17dqqr24esgpd8m5va5s9gd9h86nrz9-zh-CN":455},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":241,"isFallback":231,"parentExtension":245,"providers":301,"relations":305,"repo":307,"tags":451,"workflow":452},1778684241900.214,"k17dqqr24esgpd8m5va5s9gd9h86nrz9",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"与 ClickHouse 性能兼容的即插即用 pandas 替代品。使用 `import chdb.datastore as pd`（或 `from datastore import DataStore`）并编写标准的 pandas 代码 — API 相同，在大数据集上速度提升 10-100 倍。支持 16 种以上数据源（MySQL、PostgreSQL、S3、MongoDB、ClickHouse、Iceberg、Delta Lake 等）和 10 种以上文件格式（Parquet、CSV、JSON、Arrow、ORC 等）以及跨源连接。当用户希望使用 pandas 风格的语法分析数据、加速缓慢的 pandas 代码、将远程数据库或云存储作为 DataFrame 查询，或连接不同来源的数据时，请使用此技能 — 即使他们没有明确提及 chdb 或 DataStore。请勿用于原始 SQL 查询、ClickHouse 服务器管理或非 Python 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Always read relevant rule files and cite specific rules in responses.",{"claudeCode":12},"SKILL.md frontmatter at skills/clickhouse-best-practices/SKILL.md",[412,413,414],{"path":356,"priority":319},{"path":321,"priority":324},{"path":397,"priority":336},{"basePath":416,"description":417,"displayName":418,"installMethods":419,"rationale":420,"selectedPaths":421,"source":325,"sourceLanguage":255,"type":244},"skills/clickhouse-client-js/clickhouse-js-node-troubleshooting","Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically — including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like \"my inserts keep failing\" or \"connection drops randomly\" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.\n","clickhouse-js-node-troubleshooting",{"claudeCode":12},"SKILL.md frontmatter at skills/clickhouse-client-js/clickhouse-js-node-troubleshooting/SKILL.md",[422],{"path":356,"priority":319},{"basePath":424,"description":425,"displayName":426,"installMethods":427,"rationale":428,"selectedPaths":429,"source":325,"sourceLanguage":255,"type":244},"skills/clickhousectl-cloud-deploy","Use when a user wants to deploy ClickHouse to the cloud, go to production, use ClickHouse Cloud, host a managed ClickHouse service, or migrate from a local ClickHouse setup to ClickHouse Cloud.","clickhousectl-cloud-deploy",{"claudeCode":12},"SKILL.md frontmatter at skills/clickhousectl-cloud-deploy/SKILL.md",[430],{"path":356,"priority":319},{"basePath":432,"description":433,"displayName":434,"installMethods":435,"rationale":436,"selectedPaths":437,"source":325,"sourceLanguage":255,"type":244},"skills/clickhousectl-local-dev","Use when a user wants to build an application with ClickHouse, set up a local ClickHouse development environment, install ClickHouse, create a local server, create tables, or start developing with ClickHouse. Covers the full flow from zero to a working local ClickHouse setup.","clickhousectl-local-dev",{"claudeCode":12},"SKILL.md frontmatter at skills/clickhousectl-local-dev/SKILL.md",[438],{"path":356,"priority":319},{"sources":440},[441],"manual",{"closedIssues90d":8,"description":443,"forks":230,"homepage":444,"license":237,"openIssues90d":233,"pushedAt":234,"readmeSize":228,"stars":235,"topics":445},"The official Agent Skills for ClickHouse and ClickHouse Cloud","https://clickhouse.ai",[446,211],"agents",{"classifiedAt":448,"discoverAt":449,"extractAt":450,"githubAt":450,"updatedAt":448},1778683910082,1778683905800,1778683908184,[211,209,215,213,214,210,212],{"evaluatedAt":453,"extractAt":284,"updatedAt":240},1778684010861,[],[456,485,513,544,565,594],{"_creationTime":457,"_id":458,"community":459,"display":460,"identity":466,"providers":471,"relations":479,"tags":481,"workflow":482},1778691799740.4863,"k17de5wxjp7msakczxjbt8e7sh86n1c7",{"reviewCount":8},{"description":461,"installMethods":462,"name":464,"sourceUrl":465},"Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.",{"claudeCode":463},"K-Dense-AI/claude-scientific-skills","Polars","https://github.com/K-Dense-AI/claude-scientific-skills",{"basePath":467,"githubOwner":468,"githubRepo":469,"locale":255,"slug":470,"type":244},"scientific-skills/polars","K-Dense-AI","claude-scientific-skills","polars",{"evaluate":472,"extract":477},{"promptVersionExtension":202,"promptVersionScoring":203,"score":473,"tags":474,"targetMarket":273,"tier":216},99,[213,475,476,290,214],"data-processing","performance",{"commitSha":275,"license":478},"MIT",{"repoId":480},"kd79rphh5gexy91xmpxc05h5mh86mm9r",[475,213,214,476,290],{"evaluatedAt":483,"extractAt":484,"updatedAt":483},1778693624979,1778691799740,{"_creationTime":486,"_id":487,"community":488,"display":489,"identity":495,"providers":500,"relations":507,"tags":509,"workflow":510},1778675145461.8557,"k1782c1rcbne9pgyx4be8ww93x86nbs2",{"reviewCount":8},{"description":490,"installMethods":491,"name":493,"sourceUrl":494},"Part of the AlterLab Academic Skills suite. Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.",{"claudeCode":492},"AlterLab-IEU/AlterLab-Academic-Skills","AlterLab Polars","https://github.com/AlterLab-IEU/AlterLab-Academic-Skills",{"basePath":496,"githubOwner":497,"githubRepo":498,"locale":255,"slug":499,"type":244},"skills/data-science/alterlab-polars","AlterLab-IEU","AlterLab-Academic-Skills","alterlab-polars",{"evaluate":501,"extract":506},{"promptVersionExtension":202,"promptVersionScoring":203,"score":502,"tags":503,"targetMarket":273,"tier":505},78,[504,213,292,214,290,476],"data-science","community",{"commitSha":275,"license":478},{"repoId":508},"kd7fqvj70pvyn4r3q9kctpnd7d86mfqd",[292,504,213,214,476,290],{"evaluatedAt":511,"extractAt":512,"updatedAt":511},1778676483564,1778675145461,{"_creationTime":514,"_id":515,"community":516,"display":517,"identity":523,"providers":527,"relations":537,"tags":540,"workflow":541},1778695548458.4036,"k171cqe6hd4yd3ktqnf3qy9z5186mmff",{"reviewCount":8},{"description":518,"installMethods":519,"name":521,"sourceUrl":522},"Design and execute insect population surveys covering survey design, sampling methods, field execution, specimen identification, diversity index calculation including Shannon-Wiener and Simpson indices, statistical analysis, and reporting. Covers defining survey objectives, selecting study sites, determining sampling intensity and replication, choosing sampling methods appropriate to target taxa, standardizing collection effort, recording environmental covariates, identifying specimens to the lowest practical taxonomic level, calculating species richness, Shannon-Wiener diversity (H'), Simpson diversity (1-D), evenness, rarefaction curves, multivariate ordination, and producing survey reports with species lists and conservation implications. Use when conducting baseline biodiversity assessments, monitoring insect populations over time, comparing insect communities across habitats or treatments, assessing environmental impact, or supporting conservation planning with quantitative ecological data.\n",{"claudeCode":520},"pjt222/agent-almanac","survey-insect-population","https://github.com/pjt222/agent-almanac",{"basePath":524,"githubOwner":525,"githubRepo":526,"locale":255,"slug":521,"type":244},"skills/survey-insect-population","pjt222","agent-almanac",{"evaluate":528,"extract":536},{"promptVersionExtension":202,"promptVersionScoring":203,"score":529,"tags":530,"targetMarket":273,"tier":216},100,[531,532,533,534,535,209],"entomology","insects","ecology","biodiversity","survey",{"commitSha":275},{"parentExtensionId":538,"repoId":539},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[534,209,533,531,532,535],{"evaluatedAt":542,"extractAt":543,"updatedAt":542},1778701822946,1778695548458,{"_creationTime":545,"_id":546,"community":547,"display":548,"identity":552,"providers":554,"relations":561,"tags":562,"workflow":563},1778695548458.3613,"k17dx6tyy2yb3z5pp1vgmg46ad86nm18",{"reviewCount":8},{"description":549,"installMethods":550,"name":551,"sourceUrl":522},"Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.\n",{"claudeCode":520},"fit-drift-diffusion-model",{"basePath":553,"githubOwner":525,"githubRepo":526,"locale":255,"slug":551,"type":244},"skills/fit-drift-diffusion-model",{"evaluate":555,"extract":560},{"promptVersionExtension":202,"promptVersionScoring":203,"score":529,"tags":556,"targetMarket":273,"tier":216},[557,558,559,290,209],"cognitive-science","modeling","statistics",{"commitSha":275},{"parentExtensionId":538,"repoId":539},[557,209,558,290,559],{"evaluatedAt":564,"extractAt":543,"updatedAt":564},1778698191612,{"_creationTime":566,"_id":567,"community":568,"display":569,"identity":575,"providers":579,"relations":587,"tags":590,"workflow":591},1778695720086.7703,"k176r34g5a5fjn1z1a4gq6v88186nje0",{"reviewCount":8},{"description":570,"installMethods":571,"name":573,"sourceUrl":574},"Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.",{"claudeCode":572},"product-on-purpose/pm-skills","measure-experiment-design","https://github.com/product-on-purpose/pm-skills",{"basePath":576,"githubOwner":577,"githubRepo":578,"locale":255,"slug":573,"type":244},"skills/measure-experiment-design","product-on-purpose","pm-skills",{"evaluate":580,"extract":586},{"promptVersionExtension":202,"promptVersionScoring":203,"score":529,"tags":581,"targetMarket":273,"tier":216},[582,583,584,585,209],"ab-testing","experimentation","product-management","a-b-testing",{"commitSha":275},{"parentExtensionId":588,"repoId":589},"k1721116hsfj7zg78w03432n8986n6y8","kd78ksv1wjj826ds5j1sh2kqnx86mhqf",[585,582,209,583,584],{"evaluatedAt":592,"extractAt":593,"updatedAt":592},1778696438706,1778695720086,{"_creationTime":595,"_id":596,"community":597,"display":598,"identity":602,"providers":605,"relations":613,"tags":614,"workflow":615},1778691799740.488,"k1707r3f2j67714pvq6wk0r6y186m2zd",{"reviewCount":8},{"description":599,"installMethods":600,"name":601,"sourceUrl":465},"Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.",{"claudeCode":463},"PyDESeq2",{"basePath":603,"githubOwner":468,"githubRepo":469,"locale":255,"slug":604,"type":244},"scientific-skills/pydeseq2","pydeseq2",{"evaluate":606,"extract":612},{"promptVersionExtension":202,"promptVersionScoring":203,"score":529,"tags":607,"targetMarket":273,"tier":216},[608,609,610,611,290,209],"bioinformatics","genomics","rna-seq","deseq2",{"commitSha":275,"license":478},{"repoId":480},[608,209,611,609,290,610],{"evaluatedAt":616,"extractAt":484,"updatedAt":616},1778693766611]