[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CGPV4R2TTYSQ0GRDZJ9RHT":3,"skill-same:01M6CGPV4R2TTYSQ0GRDZJ9RHT":134},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6CGPV4R2TTYSQ0GRDZJ9RHT","detecting-data-anomalies",null,"Process identify anomalies and outliers in datasets using machine learning algorithms.\nUse when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior.\nTrigger with phrases like \"detect anomalies\", \"find outliers\", or \"identify unusual patterns\".","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1986,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"data","detecting","Detecting Data Anomalies","datasets","when","normal","analyzing data","for","with","machine","detect","trigger","io","patterns","python","use","process","behavior","read","find","grep","learning","Bash","analyzing","machine learning","2026-11-10 10:12:30",false,"---\nname: detecting-data-anomalies\ndescription: |\n  Process identify anomalies and outliers in datasets using machine learning algorithms.\n  Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior.\n  Trigger with phrases like \"detect anomalies\", \"find outliers\", or \"identify unusual patterns\".\n  \nallowed-tools: Read, Bash(python:*), Grep, Glob\nversion: 1.0.0\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\nlicense: MIT\n---\n\n# Detecting Data Anomalies\n\n## Overview\n\nThis skill provides automated assistance for the described functionality.\n\n## Prerequisites\n\nBefore using this skill, ensure you have:\n- Dataset in accessible format (CSV, JSON, or database)\n- Python environment with scikit-learn or similar ML libraries\n- Understanding of data distribution and expected patterns\n- Sufficient data volume for statistical significance\n- Knowledge of domain-specific normal behavior\n- Data preprocessing capabilities for cleaning and scaling\n\n## Instructions\n\n1. Load dataset using Read tool\n2. Inspect data structure and identify relevant features\n3. Clean data by handling missing values and inconsistencies\n4. Normalize or scale features as appropriate for algorithm\n5. Split temporal data if time-series analysis is needed\n1. Apply selected algorithm using Bash tool\n2. Generate anomaly scores for each data point\n3. Classify points as normal or anomalous based on threshold\n4. Extract characteristics of identified anomalies\n\n\nSee `{baseDir}\u002Freferences\u002Fimplementation.md` for detailed implementation guide.\n\n## Output\n\n- Total data points analyzed\n- Number of anomalies detected\n- Contamination rate (percentage of anomalies)\n- Algorithm used and configuration parameters\n- Confidence scores for detected anomalies\n- Record identifier and timestamp (if applicable)\n\n## Error Handling\n\nSee `{baseDir}\u002Freferences\u002Ferrors.md` for comprehensive error handling.\n\n## Examples\n\nSee `{baseDir}\u002Freferences\u002Fexamples.md` for detailed examples.\n\n## Resources\n\n- Isolation Forest documentation and implementation examples\n- One-Class SVM for novelty detection\n- Local Outlier Factor (LOF) for density-based detection\n- Autoencoder-based anomaly detection for deep learning approaches\n- scikit-learn anomaly detection module\n","# Detecting Data Anomalies\n\n## Overview\n\nThis skill provides automated assistance for the described functionality.\n\n## Prerequisites\n\nBefore using this skill, ensure you have:\n- Dataset in accessible format (CSV, JSON, or database)\n- Python environment with scikit-learn or similar ML libraries\n- Understanding of data distribution and expected patterns\n- Sufficient data volume for statistical significance\n- Knowledge of domain-specific normal behavior\n- Data preprocessing capabilities for cleaning and scaling\n\n## Instructions\n\n1. Load dataset using Read tool\n2. Inspect data structure and identify relevant features\n3. Clean data by handling missing values and inconsistencies\n4. Normalize or scale features as appropriate for algorithm\n5. Split temporal data if time-series analysis is needed\n1. Apply selected algorithm using Bash tool\n2. Generate anomaly scores for each data point\n3. Classify points as normal or anomalous based on threshold\n4. Extract characteristics of identified anomalies\n\n\nSee `{baseDir}\u002Freferences\u002Fimplementation.md` for detailed implementation guide.\n\n## Output\n\n- Total data points analyzed\n- Number of anomalies detected\n- Contamination rate (percentage of anomalies)\n- Algorithm used and configuration parameters\n- Confidence scores for detected anomalies\n- Record identifier and timestamp (if applicable)\n\n## Error Handling\n\nSee `{baseDir}\u002Freferences\u002Ferrors.md` for comprehensive error handling.\n\n## Examples\n\nSee `{baseDir}\u002Freferences\u002Fexamples.md` for detailed examples.\n\n## Resources\n\n- Isolation Forest documentation and implementation examples\n- One-Class SVM for novelty detection\n- Local Outlier Factor (LOF) for density-based detection\n- Autoencoder-based anomaly detection for deep learning approaches\n- scikit-learn anomaly detection module\n",{"name":5,"author":54,"license":15,"version":55,"description":56,"allowed-tools":57},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Process identify anomalies and outliers in datasets using machine learning algorithms.\nUse when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior.\nTrigger with phrases like \"detect anomalies\", \"find outliers\", or \"identify unusual patterns\".\n","Read, Bash(python:*), Grep, Glob","main","7439377908fdee98c9bb5863391212b95453de95","tests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies",233,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust","https:\u002F\u002Fgithub.com\u002FDicklesworthstone","https:\u002F\u002Fwww.jeffreyemanuel.com\u002F","Building in NY","2026-11-13 03:33:43",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add Dicklesworthstone\u002Fpi_agent_rust --skill 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detecting-data-anomalies","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002FSKILL.md",true,[81,87,93,97,101,105,109,113,119,123,127,131],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"assets\u002FREADME.md","ASSET","资产文件",404,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Fassets\u002FREADME.md",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fbreaking_changes_patterns.md","REFERENCE","参考文档",3871,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-820-dependency-optimization-coordinator\u002Freferences\u002Fbreaking_changes_patterns.md",{"path":94,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Ferrors.md",1001,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Freferences\u002Ferrors.md",{"path":98,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fexamples.md",67,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Freferences\u002Fexamples.md",{"path":102,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fimplementation.md",1771,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Freferences\u002Fimplementation.md",{"path":106,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002FREADME.md",683,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Freferences\u002FREADME.md",{"path":110,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fsecurity_audit_guide.md",2717,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-820-dependency-optimization-coordinator\u002Freferences\u002Fsecurity_audit_guide.md",{"path":114,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":117,"sourceHtmlUrl":118},"scripts\u002Falgorithm_selector.py","SCRIPT","脚本",2974,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Fscripts\u002Falgorithm_selector.py",{"path":120,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002Fanomaly_visualizer.py",2940,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Fscripts\u002Fanomaly_visualizer.py",{"path":124,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":125,"sourceHtmlUrl":126},"scripts\u002Fdata_loader.py",2952,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Fscripts\u002Fdata_loader.py",{"path":128,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":129,"sourceHtmlUrl":130},"scripts\u002FREADME.md",639,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Fscripts\u002FREADME.md",{"path":132,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":125,"sourceHtmlUrl":133},"scripts\u002Freport_generator.py","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Fanomaly-detection-system\u002Fskills\u002Fdetecting-data-anomalies\u002Fscripts\u002Freport_generator.py",[135,174,197,222,249,271],{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":139,"licenseFileUrl":140,"sourceHtmlUrl":141,"categories":142,"keywords":148,"publishedAt":49},"01M6FEGPPK0ZTKCCM0R3E3KS33","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[143],{"categoryNo":144,"categoryName":145,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,39,167,168,169,170,171,31,172,173],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","component","routing","state management","state","shadcn","file","ui","Tools","Tailwind","html","single-file","simple","ui components","multi","jsx","suite",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":139,"licenseFileUrl":178,"sourceHtmlUrl":179,"categories":180,"keywords":185,"publishedAt":49},"01M6FEE9VTK1W4GY21AKREJXRG","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[181,184],{"categoryNo":182,"categoryName":183,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":144,"categoryName":145,"taxonomyType":146,"taxonomyTypeDesc":147},[186,187,188,154,189,190,150,191,192,193,164,194,195,41,196,32,31],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","applications","local",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":201,"sourceHtmlUrl":202,"categories":203,"keywords":204,"publishedAt":49},"01M6FE20F7ZTQBFWKF4MPJ2SBX","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.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FSKILL.md",[],[205,206,207,208,24,209,210,211,39,212,213,214,215,216,217,218,32,219,220,221,31,28],"analysis","neuropixels analysis","spike","metrics","open","quality","user","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":139,"licenseFileUrl":226,"sourceHtmlUrl":227,"categories":228,"keywords":232,"publishedAt":49},"01M6FDP0PDK6F1GGX395C7625Z","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[229],{"categoryNo":230,"categoryName":231,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000023","Skill与智能体开发",[233,234,235,236,237,238,239,240,241,242,243,244,245,246,32,31,247,28,248],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":250,"name":251,"nameZh":6,"description":252,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":253,"sourceHtmlUrl":254,"categories":255,"keywords":257,"publishedAt":49},"01M6FDM8Y7VK7NYG9SM7QPA3PW","notebooklm","Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FSKILL.md",[256],{"categoryNo":230,"categoryName":231,"taxonomyType":146,"taxonomyTypeDesc":147},[251,258,259,260,261,262,263,193,264,265,39,266,267,268,269,31,270],"google","browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation","source",{"skillNo":272,"name":273,"nameZh":6,"description":274,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":139,"licenseFileUrl":275,"sourceHtmlUrl":276,"categories":277,"keywords":284,"publishedAt":49},"01M6FDHPXE10J3XGTCH5HBHS1M","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[278,281],{"categoryNo":279,"categoryName":280,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":282,"categoryName":283,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000016","图像与多媒体",[285,286,287,234,288,165,289,290,239,39,291,246,292,293,294,31,295,28],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]