[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CGSYHEHDCCWJDP07JX37BT":3,"skill-same:01M6CGSYHEHDCCWJDP07JX37BT":123},{"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},"01M6CGSYHEHDCCWJDP07JX37BT","forecasting-time-series-data",null,"Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1991,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\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],"time series","data","series","time","Forecasting Time Series Data","forecasting","user","when","with","relevant","trigger","io","patterns","use","process","assistant","context","edit","write","read","cmd","grep","Bash","ai assistant","forecast","2026-11-10 10:12:30",false,"---\nname: forecasting-time-series-data\ndescription: |\n  Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.\nallowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)\nversion: 1.0.0\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\nlicense: MIT\n---\n# Time Series Forecaster\n\nThis skill provides automated assistance for time series forecaster tasks.\n\n## Overview\n\n\nThis skill provides automated assistance for time series forecaster tasks.\nThis skill empowers Claude to perform time series forecasting, providing insights into future trends and patterns. It automates the process of data analysis, model selection, and prediction generation, delivering valuable information for decision-making.\n\n## How It Works\n\n1. **Data Analysis**: Claude analyzes the provided time series data, identifying key characteristics such as trends, seasonality, and autocorrelation.\n2. **Model Selection**: Based on the data characteristics, Claude selects an appropriate forecasting model (e.g., ARIMA, Prophet).\n3. **Prediction Generation**: The selected model is trained on the historical data, and future values are predicted along with confidence intervals.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Forecast future sales based on past sales data.\n- Predict website traffic for the next month.\n- Analyze trends in stock prices over the past year.\n\n## Examples\n\n### Example 1: Forecasting Sales\n\nUser request: \"Forecast sales for the next quarter based on the past 3 years of monthly sales data.\"\n\nThe skill will:\n1. Analyze the historical sales data to identify trends and seasonality.\n2. Select and train a suitable forecasting model (e.g., ARIMA or Prophet).\n3. Generate a forecast of sales for the next quarter, including confidence intervals.\n\n### Example 2: Predicting Website Traffic\n\nUser request: \"Predict weekly website traffic for the next month based on the last 6 months of data.\"\n\nThe skill will:\n1. Analyze the website traffic data to identify patterns and seasonality.\n2. Choose an appropriate time series forecasting model.\n3. Generate a forecast of weekly website traffic for the next month.\n\n## Best Practices\n\n- **Data Quality**: Ensure the time series data is clean, complete, and accurate for optimal forecasting results.\n- **Model Selection**: Choose a forecasting model appropriate for the characteristics of the data (e.g., ARIMA for stationary data, Prophet for data with strong seasonality).\n- **Evaluation**: Evaluate the performance of the forecasting model using appropriate metrics (e.g., Mean Absolute Error, Root Mean Squared Error).\n\n## Integration\n\nThis skill can be integrated with other data analysis and visualization tools within the Claude Code ecosystem to provide a comprehensive solution for time series analysis and forecasting.\n\n## Prerequisites\n\n- Appropriate file access permissions\n- Required dependencies installed\n\n## Instructions\n\n1. Invoke this skill when the trigger conditions are met\n2. Provide necessary context and parameters\n3. Review the generated output\n4. Apply modifications as needed\n\n## Output\n\nThe skill produces structured output relevant to the task.\n\n## Error Handling\n\n- Invalid input: Prompts for correction\n- Missing dependencies: Lists required components\n- Permission errors: Suggests remediation steps\n\n## Resources\n\n- Project documentation\n- Related skills and commands","# Time Series Forecaster\n\nThis skill provides automated assistance for time series forecaster tasks.\n\n## Overview\n\n\nThis skill provides automated assistance for time series forecaster tasks.\nThis skill empowers Claude to perform time series forecasting, providing insights into future trends and patterns. It automates the process of data analysis, model selection, and prediction generation, delivering valuable information for decision-making.\n\n## How It Works\n\n1. **Data Analysis**: Claude analyzes the provided time series data, identifying key characteristics such as trends, seasonality, and autocorrelation.\n2. **Model Selection**: Based on the data characteristics, Claude selects an appropriate forecasting model (e.g., ARIMA, Prophet).\n3. **Prediction Generation**: The selected model is trained on the historical data, and future values are predicted along with confidence intervals.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Forecast future sales based on past sales data.\n- Predict website traffic for the next month.\n- Analyze trends in stock prices over the past year.\n\n## Examples\n\n### Example 1: Forecasting Sales\n\nUser request: \"Forecast sales for the next quarter based on the past 3 years of monthly sales data.\"\n\nThe skill will:\n1. Analyze the historical sales data to identify trends and seasonality.\n2. Select and train a suitable forecasting model (e.g., ARIMA or Prophet).\n3. Generate a forecast of sales for the next quarter, including confidence intervals.\n\n### Example 2: Predicting Website Traffic\n\nUser request: \"Predict weekly website traffic for the next month based on the last 6 months of data.\"\n\nThe skill will:\n1. Analyze the website traffic data to identify patterns and seasonality.\n2. Choose an appropriate time series forecasting model.\n3. Generate a forecast of weekly website traffic for the next month.\n\n## Best Practices\n\n- **Data Quality**: Ensure the time series data is clean, complete, and accurate for optimal forecasting results.\n- **Model Selection**: Choose a forecasting model appropriate for the characteristics of the data (e.g., ARIMA for stationary data, Prophet for data with strong seasonality).\n- **Evaluation**: Evaluate the performance of the forecasting model using appropriate metrics (e.g., Mean Absolute Error, Root Mean Squared Error).\n\n## Integration\n\nThis skill can be integrated with other data analysis and visualization tools within the Claude Code ecosystem to provide a comprehensive solution for time series analysis and forecasting.\n\n## Prerequisites\n\n- Appropriate file access permissions\n- Required dependencies installed\n\n## Instructions\n\n1. Invoke this skill when the trigger conditions are met\n2. Provide necessary context and parameters\n3. Review the generated output\n4. Apply modifications as needed\n\n## Output\n\nThe skill produces structured output relevant to the task.\n\n## Error Handling\n\n- Invalid input: Prompts for correction\n- Missing dependencies: Lists required components\n- Permission errors: Suggests remediation steps\n\n## Resources\n\n- Project documentation\n- Related skills and commands",{"name":5,"author":54,"license":15,"version":55,"description":56,"allowed-tools":57},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.\n","Read, Write, Edit, Grep, Glob, Bash(cmd:*)","main","7439377908fdee98c9bb5863391212b95453de95","tests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data",234,"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 11:50:27",{"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 forecasting-time-series-data",{"tool":73,"command":74},"bunx","bunx skills add Dicklesworthstone\u002Fpi_agent_rust --skill forecasting-time-series-data",{"tool":76,"command":77},"pnpm","pnpm dlx skills add Dicklesworthstone\u002Fpi_agent_rust --skill forecasting-time-series-data","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\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002FSKILL.md",true,[81,87,91,95,101,107,111,115,119],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"assets\u002Fconfiguration_template.json","ASSET","资产文件",6049,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fassets\u002Fconfiguration_template.json",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"assets\u002Fexample_data.csv",907,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fassets\u002Fexample_data.csv",{"path":92,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":93,"sourceHtmlUrl":94},"assets\u002FREADME.md",358,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fassets\u002FREADME.md",{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"assets\u002Fvisualization_template.py","SCRIPT","脚本",9659,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fassets\u002Fvisualization_template.py",{"path":102,"resourceType":103,"resourceTypeDesc":104,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002FREADME.md","REFERENCE","参考文档",556,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Freferences\u002FREADME.md",{"path":108,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":109,"sourceHtmlUrl":110},"scripts\u002Fevaluate.py",2920,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fscripts\u002Fevaluate.py",{"path":112,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fforecast.py",2932,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fscripts\u002Fforecast.py",{"path":116,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":117,"sourceHtmlUrl":118},"scripts\u002Fpreprocess.py",2916,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fscripts\u002Fpreprocess.py",{"path":120,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002FREADME.md",470,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fai-ml\u002Ftime-series-forecaster\u002Fskills\u002Fforecasting-time-series-data\u002Fscripts\u002FREADME.md",[124,164,188,212,239,261],{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":128,"licenseFileUrl":129,"sourceHtmlUrl":130,"categories":131,"keywords":137,"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",[132],{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,37,156,157,158,159,160,161,162,163],"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","for","jsx","suite",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":128,"licenseFileUrl":168,"sourceHtmlUrl":169,"categories":170,"keywords":175,"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",[171,174],{"categoryNo":172,"categoryName":173,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},[176,177,178,143,179,180,139,181,182,183,153,184,185,186,187,32,161],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","applications","behavior","local",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":192,"sourceHtmlUrl":193,"categories":194,"keywords":195,"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",[],[196,197,198,199,25,200,201,30,37,202,203,204,205,206,207,208,32,209,210,211,161,31],"analysis","neuropixels analysis","spike","metrics","open","quality","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":128,"licenseFileUrl":216,"sourceHtmlUrl":217,"categories":218,"keywords":222,"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",[219],{"categoryNo":220,"categoryName":221,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000023","Skill与智能体开发",[223,224,225,226,227,228,229,230,231,232,233,234,235,236,32,161,237,31,238],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":240,"name":241,"nameZh":6,"description":242,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":243,"sourceHtmlUrl":244,"categories":245,"keywords":247,"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",[246],{"categoryNo":220,"categoryName":221,"taxonomyType":135,"taxonomyTypeDesc":136},[241,248,249,250,251,252,253,183,254,255,37,256,257,258,259,161,260],"google","browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation","source",{"skillNo":262,"name":263,"nameZh":6,"description":264,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":128,"licenseFileUrl":265,"sourceHtmlUrl":266,"categories":267,"keywords":274,"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",[268,271],{"categoryNo":269,"categoryName":270,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":272,"categoryName":273,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000016","图像与多媒体",[275,276,277,224,278,154,279,280,229,37,281,236,282,283,284,161,285,31],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]