[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CSA24AMSBTSA1HNTXD8N3F":3,"skill-same:01M6CSA24AMSBTSA1HNTXD8N3F":85},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":61,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6CSA24AMSBTSA1HNTXD8N3F","bdistill-behavioral-xray",null,"X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fbdistill-behavioral-xray\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"bdistill behavioral xray","behavioral","Ray","model-evaluation","community","cursor","model","team","api","analysis","testing","behavioral-analysis","Evaluation","compliance","red-team","reasoning","copilot","codex","safe","MCP","2026-11-10 10:12:30",false,"---\nname: bdistill-behavioral-xray\ndescription: \"X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.\"\ncategory: ai-testing\nrisk: safe\nsource: community\ndate_added: \"2026-03-20\"\nauthor: FrancyJGLisboa\ntags: [ai, testing, behavioral-analysis, model-evaluation, red-team, compliance, mcp]\ntools: [claude, cursor, codex, copilot]\n---\n\n# Behavioral X-Ray\n\nSystematically probe an AI model's behavioral patterns and generate a visual report. The AI agent probes *itself* — no API key or external setup needed.\n\n## Overview\n\nbdistill's Behavioral X-Ray runs 30 carefully designed probe questions across 6 dimensions, auto-tags each response with behavioral metadata, and compiles results into a styled HTML report with radar charts and actionable insights.\n\nUse it to understand your model before building with it, compare models for task selection, or track behavioral drift over time.\n\n## When to Use This Skill\n\n- Use when you want to understand how your AI model actually behaves (not how it claims to)\n- Use when choosing between models for a specific task\n- Use when debugging unexpected refusals, hallucinations, or formatting issues\n- Use for compliance auditing — documenting model behavior at deployment boundaries\n- Use for red team assessments — systematic boundary mapping across safety dimensions\n\n## How It Works\n\n### Step 1: Install\n\n```bash\npip install bdistill\nclaude mcp add bdistill -- bdistill-mcp   # Claude Code\n```\n\nFor other tools, add bdistill-mcp as an MCP server in your project config.\n\n### Step 2: Run the probe\n\nIn Claude Code:\n```\n\u002Fxray                          # Full behavioral probe (30 questions)\n\u002Fxray --dimensions refusal     # Probe just one dimension\n\u002Fxray-report                   # Generate report from completed probe\n```\n\nIn any tool with MCP:\n```\n\"X-ray your behavioral patterns\"\n\"Test your refusal boundaries\"\n\"Generate a behavioral report\"\n```\n\n## Probe Dimensions\n\n| Dimension | What it measures |\n|-----------|-----------------|\n| **tool_use** | When does it call tools vs. answer from knowledge? |\n| **refusal** | Where does it draw safety boundaries? Does it over-refuse? |\n| **formatting** | Lists vs. prose? Code blocks? Length calibration? |\n| **reasoning** | Does it show chain-of-thought? Handle trick questions? |\n| **persona** | Identity, tone matching, composure under hostility |\n| **grounding** | Hallucination resistance, fabrication traps, knowledge limits |\n\n## Output\n\nA styled HTML report showing:\n- Refusal rate, hedge rate, chain-of-thought usage\n- Per-dimension breakdown with bar charts\n- Notable response examples with behavioral tags\n- Actionable insights (e.g., \"you already show CoT 85% of the time, no need to prompt for it\")\n\n## Best Practices\n\n- Answer probe questions honestly — the value is in authentic behavioral data\n- Run probes on the same model periodically to track behavioral drift\n- Compare reports across models to make informed selection decisions\n- Use adversarial knowledge extraction (`\u002Fdistill --adversarial`) alongside behavioral probes for complete model profiling\n\n## Related Skills\n\n- `@bdistill-knowledge-extraction` - Extract structured domain knowledge from any AI model\n","# Behavioral X-Ray\n\nSystematically probe an AI model's behavioral patterns and generate a visual report. The AI agent probes *itself* — no API key or external setup needed.\n\n## Overview\n\nbdistill's Behavioral X-Ray runs 30 carefully designed probe questions across 6 dimensions, auto-tags each response with behavioral metadata, and compiles results into a styled HTML report with radar charts and actionable insights.\n\nUse it to understand your model before building with it, compare models for task selection, or track behavioral drift over time.\n\n## When to Use This Skill\n\n- Use when you want to understand how your AI model actually behaves (not how it claims to)\n- Use when choosing between models for a specific task\n- Use when debugging unexpected refusals, hallucinations, or formatting issues\n- Use for compliance auditing — documenting model behavior at deployment boundaries\n- Use for red team assessments — systematic boundary mapping across safety dimensions\n\n## How It Works\n\n### Step 1: Install\n\n```bash\npip install bdistill\nclaude mcp add bdistill -- bdistill-mcp   # Claude Code\n```\n\nFor other tools, add bdistill-mcp as an MCP server in your project config.\n\n### Step 2: Run the probe\n\nIn Claude Code:\n```\n\u002Fxray                          # Full behavioral probe (30 questions)\n\u002Fxray --dimensions refusal     # Probe just one dimension\n\u002Fxray-report                   # Generate report from completed probe\n```\n\nIn any tool with MCP:\n```\n\"X-ray your behavioral patterns\"\n\"Test your refusal boundaries\"\n\"Generate a behavioral report\"\n```\n\n## Probe Dimensions\n\n| Dimension | What it measures |\n|-----------|-----------------|\n| **tool_use** | When does it call tools vs. answer from knowledge? |\n| **refusal** | Where does it draw safety boundaries? Does it over-refuse? |\n| **formatting** | Lists vs. prose? Code blocks? Length calibration? |\n| **reasoning** | Does it show chain-of-thought? Handle trick questions? |\n| **persona** | Identity, tone matching, composure under hostility |\n| **grounding** | Hallucination resistance, fabrication traps, knowledge limits |\n\n## Output\n\nA styled HTML report showing:\n- Refusal rate, hedge rate, chain-of-thought usage\n- Per-dimension breakdown with bar charts\n- Notable response examples with behavioral tags\n- Actionable insights (e.g., \"you already show CoT 85% of the time, no need to prompt for it\")\n\n## Best Practices\n\n- Answer probe questions honestly — the value is in authentic behavioral data\n- Run probes on the same model periodically to track behavioral drift\n- Compare reports across models to make informed selection decisions\n- Use adversarial knowledge extraction (`\u002Fdistill --adversarial`) alongside behavioral probes for complete model profiling\n\n## Related Skills\n\n- `@bdistill-knowledge-extraction` - Extract structured domain knowledge from any AI model\n",{"name":5,"risk":42,"tags":49,"tools":52,"author":54,"source":28,"category":55,"date_added":56,"description":7},[50,34,35,27,38,37,51],"ai","mcp",[53,29,41,40],"claude","FrancyJGLisboa","ai-testing","2026-03-20","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fbdistill-behavioral-xray",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill bdistill-behavioral-xray",{"tool":71,"command":72},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill bdistill-behavioral-xray",{"tool":74,"command":75},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill bdistill-behavioral-xray","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fbdistill-behavioral-xray\u002FSKILL.md",true,[79],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"evals\u002Ftriggers.json","REFERENCE","参考文档",3154,"https:\u002F\u002Fgithub.com\u002Fevent4u-app\u002Fagent-config\u002Fblob\u002Fmain\u002Fsrc\u002Fskills\u002Ffinishing-a-development-branch\u002Fevals\u002Ftriggers.json",[86,97,113,137,156,182],{"skillNo":87,"name":88,"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":89,"categories":90,"keywords":94,"publishedAt":44},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[95,96],"Test Skill","test",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":44},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[104,105,106,107,108,109,110,28,111,112],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":120,"publishedAt":44},"01M6GDRKEA0WE670SPFWX5GKCN","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). 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Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoho-crm-automation\u002FSKILL.md",[143],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[145,121,146,147,148,149,150,28,126,151,133,152,130,129,125,153,111,154,43,155],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":165,"publishedAt":44},"01M6GDRCKQBAVTGHX5XBH26KPZ","zod-validation-expert","Expert in Zod — TypeScript-first schema validation. 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