[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6R2WH7RC6FN2XS8HRSPZCQ0":3,"skill-same:01M6R2WH7RC6FN2XS8HRSPZCQ0":94},{"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":29,"publishedAt":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":61,"install":62,"resources":75},"01M6R2WH7RC6FN2XS8HRSPZCQ0","denario",null,"Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"agent orchestration","Publication","when","for","papers","with","scientific","literature","end","computational","ideas","data analysis","ready","system","multiagent","analysis","data","experiments","workflows","orchestration","2026-11-10 10:12:30",false,"---\nname: denario\ndescription: Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.\n---\n\n# Denario\n\n## Overview\n\nDenario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing.\n\n## When to Use This Skill\n\nUse this skill when:\n- Analyzing datasets to generate novel research hypotheses\n- Developing structured research methodologies\n- Executing computational experiments and generating visualizations\n- Conducting literature searches for research context\n- Writing journal-formatted LaTeX papers from research results\n- Automating the complete research pipeline from data to publication\n\n## Installation\n\nInstall denario using uv (recommended):\n\n```bash\nuv init\nuv add \"denario[app]\"\n```\n\nOr using pip:\n\n```bash\nuv pip install \"denario[app]\"\n```\n\nFor Docker deployment or building from source, see `references\u002Finstallation.md`.\n\n## LLM API Configuration\n\nDenario requires API keys from supported LLM providers. Supported providers include:\n- Google Vertex AI\n- OpenAI\n- Other LLM services compatible with AG2\u002FLangGraph\n\nStore API keys securely using environment variables or `.env` files. For detailed configuration instructions including Vertex AI setup, see `references\u002Fllm_configuration.md`.\n\n## Core Research Workflow\n\nDenario follows a structured four-stage research pipeline:\n\n### 1. Data Description\n\nDefine the research context by specifying available data and tools:\n\n```python\nfrom denario import Denario\n\nden = Denario(project_dir=\".\u002Fmy_research\")\nden.set_data_description(\"\"\"\nAvailable datasets: time-series data on X and Y\nTools: pandas, sklearn, matplotlib\nResearch domain: [specify domain]\n\"\"\")\n```\n\n### 2. Idea Generation\n\nGenerate research hypotheses from the data description:\n\n```python\nden.get_idea()\n```\n\nThis produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea:\n\n```python\nden.set_idea(\"Custom research hypothesis\")\n```\n\n### 3. Methodology Development\n\nDevelop the research methodology:\n\n```python\nden.get_method()\n```\n\nThis creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:\n\n```python\nden.set_method(\"path\u002Fto\u002Fmethodology.md\")\n```\n\n### 4. Results Generation\n\nExecute computational experiments and generate analysis:\n\n```python\nden.get_results()\n```\n\nThis runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:\n\n```python\nden.set_results(\"path\u002Fto\u002Fresults.md\")\n```\n\n### 5. Paper Generation\n\nCreate a publication-ready LaTeX paper:\n\n```python\nfrom denario import Journal\n\nden.get_paper(journal=Journal.APS)\n```\n\nThe generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.\n\n## Available Journals\n\nDenario supports multiple journal formatting styles:\n- `Journal.APS` - American Physical Society format\n- Additional journals may be available; check `references\u002Fresearch_pipeline.md` for the complete list\n\n## Launching the GUI\n\nRun the graphical user interface:\n\n```bash\ndenario run\n```\n\nThis launches a web-based interface for interactive research workflow management.\n\n## Common Workflows\n\n### End-to-End Research Pipeline\n\n```python\nfrom denario import Denario, Journal\n\n# Initialize project\nden = Denario(project_dir=\".\u002Fresearch_project\")\n\n# Define research context\nden.set_data_description(\"\"\"\nDataset: Time-series measurements of [phenomenon]\nAvailable tools: pandas, sklearn, scipy\nResearch goal: Investigate [research question]\n\"\"\")\n\n# Generate research idea\nden.get_idea()\n\n# Develop methodology\nden.get_method()\n\n# Execute analysis\nden.get_results()\n\n# Create publication\nden.get_paper(journal=Journal.APS)\n```\n\n### Hybrid Workflow (Custom + Automated)\n\n```python\n# Provide custom research idea\nden.set_idea(\"Investigate the correlation between X and Y using time-series analysis\")\n\n# Auto-generate methodology\nden.get_method()\n\n# Auto-generate results\nden.get_results()\n\n# Generate paper\nden.get_paper(journal=Journal.APS)\n```\n\n### Literature Search Integration\n\nFor literature search functionality and additional workflow examples, see `references\u002Fexamples.md`.\n\n## Advanced Features\n\n- **Multiagent orchestration**: AG2 and LangGraph coordinate specialized agents for different research tasks\n- **Reproducible research**: All stages produce structured outputs that can be version-controlled\n- **Journal integration**: Automatic formatting for target publication venues\n- **Flexible input**: Manual or automated at each pipeline stage\n- **Docker deployment**: Containerized environment with LaTeX and all dependencies\n\n## Detailed References\n\nFor comprehensive documentation:\n- **Installation options**: `references\u002Finstallation.md`\n- **LLM configuration**: `references\u002Fllm_configuration.md`\n- **Complete API reference**: `references\u002Fresearch_pipeline.md`\n- **Example workflows**: `references\u002Fexamples.md`\n\n## Troubleshooting\n\nCommon issues and solutions:\n- **API key errors**: Ensure environment variables are set correctly (see `references\u002Fllm_configuration.md`)\n- **LaTeX compilation**: Install TeX distribution or use Docker image with pre-installed LaTeX\n- **Package conflicts**: Use virtual environments or Docker for isolation\n- **Python version**: Requires Python 3.12 or higher\n\n","# Denario\n\n## Overview\n\nDenario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing.\n\n## When to Use This Skill\n\nUse this skill when:\n- Analyzing datasets to generate novel research hypotheses\n- Developing structured research methodologies\n- Executing computational experiments and generating visualizations\n- Conducting literature searches for research context\n- Writing journal-formatted LaTeX papers from research results\n- Automating the complete research pipeline from data to publication\n\n## Installation\n\nInstall denario using uv (recommended):\n\n```bash\nuv init\nuv add \"denario[app]\"\n```\n\nOr using pip:\n\n```bash\nuv pip install \"denario[app]\"\n```\n\nFor Docker deployment or building from source, see `references\u002Finstallation.md`.\n\n## LLM API Configuration\n\nDenario requires API keys from supported LLM providers. Supported providers include:\n- Google Vertex AI\n- OpenAI\n- Other LLM services compatible with AG2\u002FLangGraph\n\nStore API keys securely using environment variables or `.env` files. For detailed configuration instructions including Vertex AI setup, see `references\u002Fllm_configuration.md`.\n\n## Core Research Workflow\n\nDenario follows a structured four-stage research pipeline:\n\n### 1. Data Description\n\nDefine the research context by specifying available data and tools:\n\n```python\nfrom denario import Denario\n\nden = Denario(project_dir=\".\u002Fmy_research\")\nden.set_data_description(\"\"\"\nAvailable datasets: time-series data on X and Y\nTools: pandas, sklearn, matplotlib\nResearch domain: [specify domain]\n\"\"\")\n```\n\n### 2. Idea Generation\n\nGenerate research hypotheses from the data description:\n\n```python\nden.get_idea()\n```\n\nThis produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea:\n\n```python\nden.set_idea(\"Custom research hypothesis\")\n```\n\n### 3. Methodology Development\n\nDevelop the research methodology:\n\n```python\nden.get_method()\n```\n\nThis creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:\n\n```python\nden.set_method(\"path\u002Fto\u002Fmethodology.md\")\n```\n\n### 4. Results Generation\n\nExecute computational experiments and generate analysis:\n\n```python\nden.get_results()\n```\n\nThis runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:\n\n```python\nden.set_results(\"path\u002Fto\u002Fresults.md\")\n```\n\n### 5. Paper Generation\n\nCreate a publication-ready LaTeX paper:\n\n```python\nfrom denario import Journal\n\nden.get_paper(journal=Journal.APS)\n```\n\nThe generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.\n\n## Available Journals\n\nDenario supports multiple journal formatting styles:\n- `Journal.APS` - American Physical Society format\n- Additional journals may be available; check `references\u002Fresearch_pipeline.md` for the complete list\n\n## Launching the GUI\n\nRun the graphical user interface:\n\n```bash\ndenario run\n```\n\nThis launches a web-based interface for interactive research workflow management.\n\n## Common Workflows\n\n### End-to-End Research Pipeline\n\n```python\nfrom denario import Denario, Journal\n\n# Initialize project\nden = Denario(project_dir=\".\u002Fresearch_project\")\n\n# Define research context\nden.set_data_description(\"\"\"\nDataset: Time-series measurements of [phenomenon]\nAvailable tools: pandas, sklearn, scipy\nResearch goal: Investigate [research question]\n\"\"\")\n\n# Generate research idea\nden.get_idea()\n\n# Develop methodology\nden.get_method()\n\n# Execute analysis\nden.get_results()\n\n# Create publication\nden.get_paper(journal=Journal.APS)\n```\n\n### Hybrid Workflow (Custom + Automated)\n\n```python\n# Provide custom research idea\nden.set_idea(\"Investigate the correlation between X and Y using time-series analysis\")\n\n# Auto-generate methodology\nden.get_method()\n\n# Auto-generate results\nden.get_results()\n\n# Generate paper\nden.get_paper(journal=Journal.APS)\n```\n\n### Literature Search Integration\n\nFor literature search functionality and additional workflow examples, see `references\u002Fexamples.md`.\n\n## Advanced Features\n\n- **Multiagent orchestration**: AG2 and LangGraph coordinate specialized agents for different research tasks\n- **Reproducible research**: All stages produce structured outputs that can be version-controlled\n- **Journal integration**: Automatic formatting for target publication venues\n- **Flexible input**: Manual or automated at each pipeline stage\n- **Docker deployment**: Containerized environment with LaTeX and all dependencies\n\n## Detailed References\n\nFor comprehensive documentation:\n- **Installation options**: `references\u002Finstallation.md`\n- **LLM configuration**: `references\u002Fllm_configuration.md`\n- **Complete API reference**: `references\u002Fresearch_pipeline.md`\n- **Example workflows**: `references\u002Fexamples.md`\n\n## Troubleshooting\n\nCommon issues and solutions:\n- **API key errors**: Ensure environment variables are set correctly (see `references\u002Fllm_configuration.md`)\n- **LaTeX compilation**: Install TeX distribution or use Docker image with pre-installed LaTeX\n- **Package conflicts**: Use virtual environments or Docker for isolation\n- **Python version**: Requires Python 3.12 or higher\n\n",{"name":5,"description":7},"main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-13 18:15:57",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill denario",{"tool":68,"command":69},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill denario",{"tool":71,"command":72},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill denario","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario\u002FSKILL.md",true,[76,82,86,90],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fexamples.md","REFERENCE","参考文档",12755,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario\u002Freferences\u002Fexamples.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Finstallation.md",3943,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario\u002Freferences\u002Finstallation.md",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fllm_configuration.md",6714,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario\u002Freferences\u002Fllm_configuration.md",{"path":91,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fresearch_pipeline.md",11885,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario\u002Freferences\u002Fresearch_pipeline.md",[95,126,151,177,201,226],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":100,"sourceHtmlUrl":101,"categories":102,"keywords":106,"publishedAt":50},"01M6R4ARN4W2VZRDERDTMN1KP0","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[103],{"categoryNo":104,"categoryName":105,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,33,125],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","routing","state management","state","file","Tailwind","use","single-file","ui components","multi","single",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":130,"sourceHtmlUrl":131,"categories":132,"keywords":137,"publishedAt":50},"01M6R45GXREM5DP8T08MAC788H","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[133,136],{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":104,"categoryName":105,"taxonomyType":21,"taxonomyTypeDesc":22},[138,139,140,112,141,142,108,143,144,145,146,147,148,149,150,35,33],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":155,"sourceHtmlUrl":156,"categories":157,"keywords":158,"publishedAt":50},"01M6R2GYJ470X8EB7R58V0B68K","think-tank","Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural\u002Fdesign\u002Fstrategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[159,160,161,162,163,45,164,165,166,167,168,169,170,171,172,173,174,175,176,32],"think tank","think","design","run","feeds","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":181,"sourceHtmlUrl":182,"categories":183,"keywords":185,"publishedAt":50},"01M6R2FSR55AVRFA9YZ8SC2GZG","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[184],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[186,187,48,188,189,190,191,192,193,194,195,196,197,198,35,33,199,32,200],"skill creator","creator","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":207,"publishedAt":50},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[208,209,210,211,212,213,214,215,121,216,217,218,219,220,221,222,223,224,225,32],"learn","ship learn next","ship","next","framework","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":230,"sourceHtmlUrl":231,"categories":232,"keywords":239,"publishedAt":50},"01M6R2088G1RHDA5MDX16HASRB","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[233,236],{"categoryNo":234,"categoryName":235,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":237,"categoryName":238,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[240,241,242,187,243,244,245,246,191,121,173,198,175,247,248,33,249,32],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]