[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADG0029ZH6GNNVGSKNNTG6":3,"skill-same:01M6ADG0029ZH6GNNVGSKNNTG6":78},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6ADG0029ZH6GNNVGSKNNTG6","diagnose",null,"Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41162,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fdiagnose\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"prompt","fitness","diagnostic","with","systematic","efficiency","across","dimensions","scan","AI workflow","workflow","quality","tool","health","Safety","remediation","actions","report","architecture","2026-11-10 10:12:30",false,"---\nname: diagnose\ndescription: \"Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.\"\n---\n\n# AI Workflow Diagnostics\n\nYou are a systematic AI workflow auditor. Perform a diagnostic scan across 5 dimensions. For each dimension, score 1–5 and provide specific findings.\n\n## Dimension 1: Prompt Quality (1–5)\n\nEvaluate:\n\n- Structure (role, context, instructions, output zones)\n- Output schema definition (explicit vs. implicit)\n- Instruction clarity (specific vs. vague)\n- Edge case handling (addressed vs. ignored)\n- Anti-patterns (wall of text, contradictions, implicit format)\n\n## Dimension 2: Context Efficiency (1–5)\n\nEvaluate:\n\n- Context budget allocation (planned vs. ad-hoc)\n- Attention gradient awareness (critical info at start\u002Fend)\n- Context window utilization (efficient vs. wasteful)\n- State management (explicit vs. implicit)\n- Memory strategy (appropriate for conversation length)\n\n## Dimension 3: Tool Health (1–5)\n\nEvaluate:\n\n- Tool count (3–7 ideal, 13+ problematic)\n- Description quality (specific vs. vague)\n- Error handling (graceful vs. none)\n- Schema completeness (input\u002Foutput\u002Ferror defined)\n- Idempotency (safe to retry vs. side-effect prone)\n- **Scope attribution**: Distinguish project-configured tools (custom scripts, project MCP servers) from agent-level tools (built-in IDE tools, global MCP servers). Only flag tool overhead for tools the project can actually control.\n\n## Dimension 4: Architecture Fitness (1–5)\n\nEvaluate:\n\n- Topology appropriateness (single vs. multi-agent justified)\n- Agent boundaries (clear vs. overlapping)\n- Handoff protocols (structured vs. ad-hoc)\n- Observability (decisions logged vs. black box)\n- Cost awareness (budgeted vs. unbounded)\n\n## Dimension 5: Safety & Reliability (1–5)\n\nEvaluate:\n\n- Input validation (present vs. absent)\n- Output filtering (PII, content policy) — scope contextually: data between a user's own frontend and backend is lower risk than data exposed to external services\n- Cost controls (ceilings set vs. unbounded)\n- Error recovery (fallbacks vs. crash)\n- Evaluation strategy (golden tests vs. \"it seems to work\")\n\n## Diagnostic Report Format\n\n```text\n╔══════════════════════════════════════╗\n║          WORKFLOW DIAGNOSTIC        ║\n╠══════════════════════════════════════╣\n║ Prompt Quality      ████░  4\u002F5      ║\n║ Context Efficiency   ███░░  3\u002F5      ║\n║ Tool Health          ██░░░  2\u002F5      ║\n║ Architecture         ████░  4\u002F5      ║\n║ Safety & Reliability ██░░░  2\u002F5      ║\n╠══════════════════════════════════════╣\n║ Overall Score:       15\u002F25           ║\n╚══════════════════════════════════════╝\n\nCRITICAL FINDINGS:\n1. [Most severe issue — immediate action needed]\n2. [Second most severe]\n3. [Third]\n\nRECOMMENDED ACTIONS:\n1. [Specific remediation for finding #1]\n2. [Specific remediation for finding #2]\n3. [Specific remediation for finding #3]\n```\n\n## Scoring Guide\n\n| Score | Meaning                | Recommended Action                        |\n|-------|------------------------|-------------------------------------------|\n| 5     | Production-excellent   | No action needed                          |\n| 4     | Good with minor gaps   | Polish prompt clarity or output schema    |\n| 3     | Functional but risky   | Add error handling or reduce complexity   |\n| 2     | Significant issues     | Immediate attention — add retries\u002Fguards  |\n| 1     | Broken or missing      | Rebuild from scratch with clear structure |\n\n## Usage\n\nInvoke this skill when you want to:\n\n- Find hidden problems before a workflow goes to production\n- Audit an existing agent for quality and reliability\n- Get a prioritized remediation plan with concrete next steps\n- Health-check a workflow after significant changes\n\nProvide the workflow description, prompt text, tool list, or agent configuration as context. The more detail you provide, the more precise the findings.\n","# AI Workflow Diagnostics\n\nYou are a systematic AI workflow auditor. Perform a diagnostic scan across 5 dimensions. For each dimension, score 1–5 and provide specific findings.\n\n## Dimension 1: Prompt Quality (1–5)\n\nEvaluate:\n\n- Structure (role, context, instructions, output zones)\n- Output schema definition (explicit vs. implicit)\n- Instruction clarity (specific vs. vague)\n- Edge case handling (addressed vs. ignored)\n- Anti-patterns (wall of text, contradictions, implicit format)\n\n## Dimension 2: Context Efficiency (1–5)\n\nEvaluate:\n\n- Context budget allocation (planned vs. ad-hoc)\n- Attention gradient awareness (critical info at start\u002Fend)\n- Context window utilization (efficient vs. wasteful)\n- State management (explicit vs. implicit)\n- Memory strategy (appropriate for conversation length)\n\n## Dimension 3: Tool Health (1–5)\n\nEvaluate:\n\n- Tool count (3–7 ideal, 13+ problematic)\n- Description quality (specific vs. vague)\n- Error handling (graceful vs. none)\n- Schema completeness (input\u002Foutput\u002Ferror defined)\n- Idempotency (safe to retry vs. side-effect prone)\n- **Scope attribution**: Distinguish project-configured tools (custom scripts, project MCP servers) from agent-level tools (built-in IDE tools, global MCP servers). Only flag tool overhead for tools the project can actually control.\n\n## Dimension 4: Architecture Fitness (1–5)\n\nEvaluate:\n\n- Topology appropriateness (single vs. multi-agent justified)\n- Agent boundaries (clear vs. overlapping)\n- Handoff protocols (structured vs. ad-hoc)\n- Observability (decisions logged vs. black box)\n- Cost awareness (budgeted vs. unbounded)\n\n## Dimension 5: Safety & Reliability (1–5)\n\nEvaluate:\n\n- Input validation (present vs. absent)\n- Output filtering (PII, content policy) — scope contextually: data between a user's own frontend and backend is lower risk than data exposed to external services\n- Cost controls (ceilings set vs. unbounded)\n- Error recovery (fallbacks vs. crash)\n- Evaluation strategy (golden tests vs. \"it seems to work\")\n\n## Diagnostic Report Format\n\n```text\n╔══════════════════════════════════════╗\n║          WORKFLOW DIAGNOSTIC        ║\n╠══════════════════════════════════════╣\n║ Prompt Quality      ████░  4\u002F5      ║\n║ Context Efficiency   ███░░  3\u002F5      ║\n║ Tool Health          ██░░░  2\u002F5      ║\n║ Architecture         ████░  4\u002F5      ║\n║ Safety & Reliability ██░░░  2\u002F5      ║\n╠══════════════════════════════════════╣\n║ Overall Score:       15\u002F25           ║\n╚══════════════════════════════════════╝\n\nCRITICAL FINDINGS:\n1. [Most severe issue — immediate action needed]\n2. [Second most severe]\n3. [Third]\n\nRECOMMENDED ACTIONS:\n1. [Specific remediation for finding #1]\n2. [Specific remediation for finding #2]\n3. [Specific remediation for finding #3]\n```\n\n## Scoring Guide\n\n| Score | Meaning                | Recommended Action                        |\n|-------|------------------------|-------------------------------------------|\n| 5     | Production-excellent   | No action needed                          |\n| 4     | Good with minor gaps   | Polish prompt clarity or output schema    |\n| 3     | Functional but risky   | Add error handling or reduce complexity   |\n| 2     | Significant issues     | Immediate attention — add retries\u002Fguards  |\n| 1     | Broken or missing      | Rebuild from scratch with clear structure |\n\n## Usage\n\nInvoke this skill when you want to:\n\n- Find hidden problems before a workflow goes to production\n- Audit an existing agent for quality and reliability\n- Get a prioritized remediation plan with concrete next steps\n- Health-check a workflow after significant changes\n\nProvide the workflow description, prompt text, tool list, or agent configuration as context. The more detail you provide, the more precise the findings.\n",{"name":5,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fdiagnose",5360,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-15 04:42:30",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add github\u002Fawesome-copilot --skill diagnose",{"tool":64,"command":65},"bunx","bunx skills add github\u002Fawesome-copilot --skill diagnose",{"tool":67,"command":68},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill diagnose","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fdiagnose\u002FSKILL.md",true,[72],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"agents\u002Fopenai.yaml","REFERENCE","参考文档",107,"https:\u002F\u002Fgithub.com\u002Ftdortman\u002Fdotfiles\u002Fblob\u002Fmain\u002Fhome\u002Fdot_agents\u002Fskills\u002Frequest-refactor-plan\u002Fagents\u002Fopenai.yaml",[79,109,137,167,201,218],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":89,"publishedAt":43},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[85,88],{"categoryNo":86,"categoryName":87,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,27],"supabase","rls","supabase rls","supabase rls policies","project","when","review","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":116,"publishedAt":43},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[115],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"distribution","skill distribution audit","audit","SET","deterministic","installer","for","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":141,"sourceHtmlUrl":142,"categories":143,"keywords":149,"publishedAt":43},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[144],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[150,151,152,153,154,155,123,94,156,157,158,159,160,161,162,106,163,164,165,166],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":171,"sourceHtmlUrl":172,"categories":173,"keywords":178,"publishedAt":43},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[174,177],{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},[179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,95,194,195,196,197,198,123,199,200],"winmd api search","search","api","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"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":205,"licenseFileUrl":206,"sourceHtmlUrl":207,"categories":208,"keywords":214,"publishedAt":43},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[209,210,213],{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":211,"categoryName":212,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[215,120,188,216,217],"resemble detect","https","identity",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":205,"licenseFileUrl":222,"sourceHtmlUrl":223,"categories":224,"keywords":229,"publishedAt":43},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[225,228],{"categoryNo":226,"categoryName":227,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[230,231,35,232,151,96,9,119,233,234,216,41,235,236,237,238,239,240,241,242,243,244,104,245,246,247,248],"playbook","quality playbook","code review","tdd","run","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]