[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PEFA3AXFDCXNEJ77K1H2BC":3,"skill-same:01M6PEFA3AXFDCXNEJ77K1H2BC":91},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6PEFA3AXFDCXNEJ77K1H2BC","analyzing-text-sentiment",null,"This skill enables Claude to analyze the sentiment of text data. It identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. Use this skill when a user requests sentiment analysis, opinion mining, or emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include \"sentiment analysis\", \"analyze sentiment\", \"opinion mining\", \"emotion detection\", and \"polarity\".","sandraschi\u002Fdocumentation-mcp","sandraschi","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F34750307?v=4","01M6C42H9WE5EC9CK3N5RGZP8S","Sandra Schipal","01M6PE1X2FZP8KR5GANPPFTHA8",2,"MIT","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fsentiment-analysis-tool\u002Fskills\u002Fsentiment-analysis-tool\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"sentiment","Analyzing Text Sentiment","text","analyzing","social","mining","requests","posts","reviews","emotion","trigger","as","tone","analysis","customer","survey","use","social media","analyze","Media","2026-11-10 10:12:30",false,"---\nname: analyzing-text-sentiment\ndescription: |\n  This skill enables Claude to analyze the sentiment of text data. It identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. Use this skill when a user requests sentiment analysis, opinion mining, or emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include \"sentiment analysis\", \"analyze sentiment\", \"opinion mining\", \"emotion detection\", and \"polarity\".\n---\n\n## Overview\n\nThis skill empowers Claude to perform sentiment analysis on text, providing insights into the emotional content and polarity of the provided data. By leveraging AI\u002FML techniques, it helps understand public opinion, customer feedback, and overall emotional tone in written communication.\n\n## How It Works\n\n1. **Text Input**: The skill receives text data as input from the user.\n2. **Sentiment Analysis**: The skill processes the text using a pre-trained sentiment analysis model to determine the sentiment polarity (positive, negative, or neutral).\n3. **Result Output**: The skill provides a sentiment score and classification, indicating the overall sentiment expressed in the text.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Determine the overall sentiment of customer reviews.\n- Analyze the emotional tone of social media posts.\n- Gauge public opinion on a particular topic.\n- Identify positive and negative feedback in survey responses.\n\n## Examples\n\n### Example 1: Analyzing Customer Reviews\n\nUser request: \"Analyze the sentiment of these customer reviews: 'The product is amazing!', 'The service was terrible.', 'It was okay.'\"\n\nThe skill will:\n1. Process the provided customer reviews.\n2. Classify each review as positive, negative, or neutral and provide sentiment scores.\n\n### Example 2: Monitoring Social Media Sentiment\n\nUser request: \"Perform sentiment analysis on the following tweet: 'I love this new feature!'\"\n\nThe skill will:\n1. Analyze the provided tweet.\n2. Identify the sentiment as positive and provide a corresponding sentiment score.\n\n## Best Practices\n\n- **Data Quality**: Ensure the input text is clear and free from ambiguous language for accurate sentiment analysis.\n- **Context Awareness**: Consider the context of the text when interpreting sentiment scores, as sarcasm or irony can affect results.\n- **Model Selection**: Use appropriate sentiment analysis models based on the type of text being analyzed (e.g., social media, customer reviews).\n\n## Integration\n\nThis skill can be integrated with other Claude Code plugins to automate workflows, such as summarizing feedback alongside sentiment scores or triggering actions based on sentiment polarity (e.g., escalating negative feedback).\n","## Overview\n\nThis skill empowers Claude to perform sentiment analysis on text, providing insights into the emotional content and polarity of the provided data. By leveraging AI\u002FML techniques, it helps understand public opinion, customer feedback, and overall emotional tone in written communication.\n\n## How It Works\n\n1. **Text Input**: The skill receives text data as input from the user.\n2. **Sentiment Analysis**: The skill processes the text using a pre-trained sentiment analysis model to determine the sentiment polarity (positive, negative, or neutral).\n3. **Result Output**: The skill provides a sentiment score and classification, indicating the overall sentiment expressed in the text.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Determine the overall sentiment of customer reviews.\n- Analyze the emotional tone of social media posts.\n- Gauge public opinion on a particular topic.\n- Identify positive and negative feedback in survey responses.\n\n## Examples\n\n### Example 1: Analyzing Customer Reviews\n\nUser request: \"Analyze the sentiment of these customer reviews: 'The product is amazing!', 'The service was terrible.', 'It was okay.'\"\n\nThe skill will:\n1. Process the provided customer reviews.\n2. Classify each review as positive, negative, or neutral and provide sentiment scores.\n\n### Example 2: Monitoring Social Media Sentiment\n\nUser request: \"Perform sentiment analysis on the following tweet: 'I love this new feature!'\"\n\nThe skill will:\n1. Analyze the provided tweet.\n2. Identify the sentiment as positive and provide a corresponding sentiment score.\n\n## Best Practices\n\n- **Data Quality**: Ensure the input text is clear and free from ambiguous language for accurate sentiment analysis.\n- **Context Awareness**: Consider the context of the text when interpreting sentiment scores, as sarcasm or irony can affect results.\n- **Model Selection**: Use appropriate sentiment analysis models based on the type of text being analyzed (e.g., social media, customer reviews).\n\n## Integration\n\nThis skill can be integrated with other Claude Code plugins to automate workflows, such as summarizing feedback alongside sentiment scores or triggering actions based on sentiment polarity (e.g., escalating negative feedback).\n",{"name":5,"description":7},"master","be156301b65e301887f327d08190ed4f17ba08d2","docs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fsentiment-analysis-tool\u002Fskills\u002Fsentiment-analysis-tool",0,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp","https:\u002F\u002Fgithub.com\u002Fsandraschi","2026-11-11 11:02:01",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add sandraschi\u002Fdocumentation-mcp --skill analyzing-text-sentiment",{"tool":65,"command":66},"bunx","bunx skills add sandraschi\u002Fdocumentation-mcp --skill analyzing-text-sentiment",{"tool":68,"command":69},"pnpm","pnpm dlx skills add sandraschi\u002Fdocumentation-mcp --skill analyzing-text-sentiment","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fsentiment-analysis-tool\u002Fskills\u002Fsentiment-analysis-tool\u002FSKILL.md",true,[73,79,85],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"assets\u002FREADME.md","ASSET","资产文件",382,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fsentiment-analysis-tool\u002Fskills\u002Fsentiment-analysis-tool\u002Fassets\u002FREADME.md",{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002FREADME.md","REFERENCE","参考文档",566,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fsentiment-analysis-tool\u002Fskills\u002Fsentiment-analysis-tool\u002Freferences\u002FREADME.md",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002FREADME.md","SCRIPT","脚本",344,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fsentiment-analysis-tool\u002Fskills\u002Fsentiment-analysis-tool\u002Fscripts\u002FREADME.md",[92,114,141,151,177,202],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":102,"publishedAt":47},"01M6PFAWAHEJJCKKVNXEWNK87W","Docs Research","Use MCP Central Docs server to search, ask, and retrieve documentation (FastMCP, standards, packaging).","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fsrc\u002Fdocs_mcp\u002Fskills\u002Fdocs-research\u002FSKILL.md",[98,101],{"categoryNo":99,"categoryName":100,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[103,104,105,106,43,107,108,109,110,111,112,113],"docs research","docs","research","standards","packaging","ask","server","search","fastmcp","documentation","MCP",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":47},"01M6PFAN4DAENMY574Q7Q3CB3H","wrappee-drift","Read-only, single-repo audit of a repo's tool coverage against the software it wraps (its current real API\u002Fdocs, not training-data memory) and against comparable\u002Fcompeting wrappers. Trigger when the user says \"wrappee drift\" followed by a repo name, \"capability audit\" followed by a repo name, or asks whether a repo is missing features the wrapped software or a competitor now has. Always scoped to one named repo per invocation -- never trigger this fleet-wide or unprompted; that is explicitly against the SOP. Do NOT trigger for internal fleet-standard compliance (that is drift-scan) or strategic worth\u002Farchive decisions (that is quality-check) -- this is specifically about the gap between the repo's tool list and the wrapped software's\u002Fcompetitors' actual current capabilities.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwrappee-drift\u002FSKILL.md",[],[121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140],"drift","wrappee drift","api","data","audit","capabilities","training","memory","features","repo audit","competitors","fleet","now","software","tool list","only","never","coverage","list","read",{"skillNo":142,"name":143,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":150,"publishedAt":47},"01M6PFADA3X031SK3W3T6X0N6H","modules","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwindsurf-mcp-standards\u002Fmodules\u002FSKILL.md",[146,149],{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[143],{"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":6,"sourceHtmlUrl":155,"categories":156,"keywords":161,"publishedAt":47},"01M6PFA8YA029N68TW0X31FB1W","mcp-standards","Use when developing MCP servers to access comprehensive standards for scaffolding, packaging, sampling, documentation, testing, error handling, monitoring, and CI\u002FCD workflows.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwindsurf-mcp-standards\u002FSKILL.md",[157,160],{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[162,113,106,43,163,164,165,166,167,168,169,170,171,172,173,174,112,175,176,107],"mcp standards","monitoring","sampling","scaffolding","servers","cd","error","for","CI\u002FCD","access","ci","mcp servers","error handling","workflows","testing",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":185,"publishedAt":47},"01M6PFA1XX344WX57B0DJZY94Q","webapp-cua-test","Run the pre-Tauri CUA click-through test (real browser, dev stack, OCR \"Connected\" wait, sidebar nav walk) for a fleet MCP server repo under D:\\Dev\\repos. Trigger when the user says \"cua webapp test\" or \"webapp clickthrough\" followed by a repo name, asks for an e2e click-through of a webapp, or asks whether a webapp boots and connects in a real browser. Not for the installed NSIS app (that is the nsis-build skill \u002F cua-nsis-test) and not for DOM assertions (Playwright), and not for hunting fake data or dead buttons (fakefind).","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwebapp-cua-test\u002FSKILL.md",[183,184],{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[186,187,188,189,190,113,191,192,193,169,37,194,195,196,197,198,199,200,109,201],"webapp","test","webapp cua test","playwright","mcp server","dev","dom","assertions","click","app","tauri","repo","stack","e2e","ocr","browser",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":209,"publishedAt":47},"01M6PF9WYF6NWJR7WVN38TT0WH","ship","Final release gate for a fleet MCP server repo under D:\\Dev\\repos -- run all quality gates, build the release artifact, verify it, commit, push, and cut a GitHub release. Trigger this whenever the user says \"ship it\", \"ship\" followed by a repo name, \"release\" followed by a repo name, or asks to finalize and publish work that's already been done in a repo. This is the terminal step after assfix\u002Fnsis-build, not a substitute for them -- if the repo hasn't been assessed recently, say so before shipping rather than shipping blind.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fship\u002FSKILL.md",[208],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[204,197,169,37,210,211,212,213,214,215,216,109,217,191,218,219,220,190,221,113],"gates","ship it","artifact","done","say","work","publish","push","shipping","quality gates","commit","github"]