[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71SADZ5RQ0FQR8SZP3P07HV":3,"skill-same:01M71SADZ5RQ0FQR8SZP3P07HV":203},{"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":18,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":14,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M71SADZ5RQ0FQR8SZP3P07HV","phoenix-evals",null,"Build and run evaluators for AI\u002FLLM applications using Phoenix.","TorchiaHub\u002FSkills","TorchiaHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F125223386?v=4","01M71Q3SNKHMKJYN4HGMSQ2CEY","Mattia Torchia","01M71Q3SP6T1Z9NBADBCFP7GM5",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"evals","phoenix evals","Phoenix","server","packages","build","for","run","applications","python","typescript","client","openai","oss","LLM Applications","Arize","2026-11-10 13:02:02",false,"---\nname: phoenix-evals\ndescription: Build and run evaluators for AI\u002FLLM applications using Phoenix.\nlicense: Apache-2.0\ncompatibility: Requires Phoenix server. Python skills need phoenix and openai packages; TypeScript skills need @arizeai\u002Fphoenix-client.\nmetadata:\n  author: oss@arize.com\n  version: \"1.0.0\"\n  languages: \"Python, TypeScript\"\n---\n\n# Phoenix Evals\n\nBuild evaluators for AI\u002FLLM applications. Code first, LLM for nuance, validate against humans.\n\n## Quick Reference\n\n| Task | Files |\n| ---- | ----- |\n| Setup | [setup-python](references\u002Fsetup-python.md), [setup-typescript](references\u002Fsetup-typescript.md) |\n| Decide what to evaluate | [evaluators-overview](references\u002Fevaluators-overview.md) |\n| Choose a judge model | [fundamentals-model-selection](references\u002Ffundamentals-model-selection.md) |\n| Use pre-built evaluators | [evaluators-pre-built](references\u002Fevaluators-pre-built.md) |\n| Build code evaluator | [evaluators-code-python](references\u002Fevaluators-code-python.md), [evaluators-code-typescript](references\u002Fevaluators-code-typescript.md) |\n| Build LLM evaluator | [evaluators-llm-python](references\u002Fevaluators-llm-python.md), [evaluators-llm-typescript](references\u002Fevaluators-llm-typescript.md), [evaluators-custom-templates](references\u002Fevaluators-custom-templates.md) |\n| Batch evaluate DataFrame | [evaluate-dataframe-python](references\u002Fevaluate-dataframe-python.md) |\n| Run experiment | [experiments-running-python](references\u002Fexperiments-running-python.md), [experiments-running-typescript](references\u002Fexperiments-running-typescript.md) |\n| Create dataset | [experiments-datasets-python](references\u002Fexperiments-datasets-python.md), [experiments-datasets-typescript](references\u002Fexperiments-datasets-typescript.md) |\n| Generate synthetic data | [experiments-synthetic-python](references\u002Fexperiments-synthetic-python.md), [experiments-synthetic-typescript](references\u002Fexperiments-synthetic-typescript.md) |\n| Validate evaluator accuracy | [validation](references\u002Fvalidation.md), [validation-evaluators-python](references\u002Fvalidation-evaluators-python.md), [validation-evaluators-typescript](references\u002Fvalidation-evaluators-typescript.md) |\n| Sample traces for review | [observe-sampling-python](references\u002Fobserve-sampling-python.md), [observe-sampling-typescript](references\u002Fobserve-sampling-typescript.md) |\n| Analyze errors | [error-analysis](references\u002Ferror-analysis.md), [error-analysis-multi-turn](references\u002Ferror-analysis-multi-turn.md), [axial-coding](references\u002Faxial-coding.md) |\n| RAG evals | [evaluators-rag](references\u002Fevaluators-rag.md) |\n| Avoid common mistakes | [common-mistakes-python](references\u002Fcommon-mistakes-python.md), [fundamentals-anti-patterns](references\u002Ffundamentals-anti-patterns.md) |\n| Production | [production-overview](references\u002Fproduction-overview.md), [production-guardrails](references\u002Fproduction-guardrails.md), [production-continuous](references\u002Fproduction-continuous.md) |\n\n## Workflows\n\n**Starting Fresh:**\n[observe-tracing-setup](references\u002Fobserve-tracing-setup.md) → [error-analysis](references\u002Ferror-analysis.md) → [axial-coding](references\u002Faxial-coding.md) → [evaluators-overview](references\u002Fevaluators-overview.md)\n\n**Building Evaluator:**\n[fundamentals](references\u002Ffundamentals.md) → [common-mistakes-python](references\u002Fcommon-mistakes-python.md) → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}\n\n**RAG Systems:**\n[evaluators-rag](references\u002Fevaluators-rag.md) → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)\n\n**Production:**\n[production-overview](references\u002Fproduction-overview.md) → [production-guardrails](references\u002Fproduction-guardrails.md) → [production-continuous](references\u002Fproduction-continuous.md)\n\n## Reference Categories\n\n| Prefix | Description |\n| ------ | ----------- |\n| `fundamentals-*` | Types, scores, anti-patterns |\n| `observe-*` | Tracing, sampling |\n| `error-analysis-*` | Finding failures |\n| `axial-coding-*` | Categorizing failures |\n| `evaluators-*` | Code, LLM, RAG evaluators |\n| `experiments-*` | Datasets, running experiments |\n| `validation-*` | Validating evaluator accuracy against human labels |\n| `production-*` | CI\u002FCD, monitoring |\n\n## Key Principles\n\n| Principle | Action |\n| --------- | ------ |\n| Error analysis first | Can't automate what you haven't observed |\n| Custom > generic | Build from your failures |\n| Code first | Deterministic before LLM |\n| Validate judges | >80% TPR\u002FTNR |\n| Binary > Likert | Pass\u002Ffail, not 1-5 |\n","# Phoenix Evals\n\nBuild evaluators for AI\u002FLLM applications. Code first, LLM for nuance, validate against humans.\n\n## Quick Reference\n\n| Task | Files |\n| ---- | ----- |\n| Setup | [setup-python](references\u002Fsetup-python.md), [setup-typescript](references\u002Fsetup-typescript.md) |\n| Decide what to evaluate | [evaluators-overview](references\u002Fevaluators-overview.md) |\n| Choose a judge model | [fundamentals-model-selection](references\u002Ffundamentals-model-selection.md) |\n| Use pre-built evaluators | [evaluators-pre-built](references\u002Fevaluators-pre-built.md) |\n| Build code evaluator | [evaluators-code-python](references\u002Fevaluators-code-python.md), [evaluators-code-typescript](references\u002Fevaluators-code-typescript.md) |\n| Build LLM evaluator | [evaluators-llm-python](references\u002Fevaluators-llm-python.md), [evaluators-llm-typescript](references\u002Fevaluators-llm-typescript.md), [evaluators-custom-templates](references\u002Fevaluators-custom-templates.md) |\n| Batch evaluate DataFrame | [evaluate-dataframe-python](references\u002Fevaluate-dataframe-python.md) |\n| Run experiment | [experiments-running-python](references\u002Fexperiments-running-python.md), [experiments-running-typescript](references\u002Fexperiments-running-typescript.md) |\n| Create dataset | [experiments-datasets-python](references\u002Fexperiments-datasets-python.md), [experiments-datasets-typescript](references\u002Fexperiments-datasets-typescript.md) |\n| Generate synthetic data | [experiments-synthetic-python](references\u002Fexperiments-synthetic-python.md), [experiments-synthetic-typescript](references\u002Fexperiments-synthetic-typescript.md) |\n| Validate evaluator accuracy | [validation](references\u002Fvalidation.md), [validation-evaluators-python](references\u002Fvalidation-evaluators-python.md), [validation-evaluators-typescript](references\u002Fvalidation-evaluators-typescript.md) |\n| Sample traces for review | [observe-sampling-python](references\u002Fobserve-sampling-python.md), [observe-sampling-typescript](references\u002Fobserve-sampling-typescript.md) |\n| Analyze errors | [error-analysis](references\u002Ferror-analysis.md), [error-analysis-multi-turn](references\u002Ferror-analysis-multi-turn.md), [axial-coding](references\u002Faxial-coding.md) |\n| RAG evals | [evaluators-rag](references\u002Fevaluators-rag.md) |\n| Avoid common mistakes | [common-mistakes-python](references\u002Fcommon-mistakes-python.md), [fundamentals-anti-patterns](references\u002Ffundamentals-anti-patterns.md) |\n| Production | [production-overview](references\u002Fproduction-overview.md), [production-guardrails](references\u002Fproduction-guardrails.md), [production-continuous](references\u002Fproduction-continuous.md) |\n\n## Workflows\n\n**Starting Fresh:**\n[observe-tracing-setup](references\u002Fobserve-tracing-setup.md) → [error-analysis](references\u002Ferror-analysis.md) → [axial-coding](references\u002Faxial-coding.md) → [evaluators-overview](references\u002Fevaluators-overview.md)\n\n**Building Evaluator:**\n[fundamentals](references\u002Ffundamentals.md) → [common-mistakes-python](references\u002Fcommon-mistakes-python.md) → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}\n\n**RAG Systems:**\n[evaluators-rag](references\u002Fevaluators-rag.md) → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)\n\n**Production:**\n[production-overview](references\u002Fproduction-overview.md) → [production-guardrails](references\u002Fproduction-guardrails.md) → [production-continuous](references\u002Fproduction-continuous.md)\n\n## Reference Categories\n\n| Prefix | Description |\n| ------ | ----------- |\n| `fundamentals-*` | Types, scores, anti-patterns |\n| `observe-*` | Tracing, sampling |\n| `error-analysis-*` | Finding failures |\n| `axial-coding-*` | Categorizing failures |\n| `evaluators-*` | Code, LLM, RAG evaluators |\n| `experiments-*` | Datasets, running experiments |\n| `validation-*` | Validating evaluator accuracy against human labels |\n| `production-*` | CI\u002FCD, monitoring |\n\n## Key Principles\n\n| Principle | Action |\n| --------- | ------ |\n| Error analysis first | Can't automate what you haven't observed |\n| Custom > generic | Build from your failures |\n| Code first | Deterministic before LLM |\n| Validate judges | >80% TPR\u002FTNR |\n| Binary > Likert | Pass\u002Ffail, not 1-5 |\n",{"name":5,"license":15,"metadata":40,"description":7,"compatibility":44},{"author":41,"version":42,"languages":43},"oss@arize.com","1.0.0","Python, TypeScript","Requires Phoenix server. Python skills need phoenix and openai packages; TypeScript skills need @arizeai\u002Fphoenix-client.","master","e3489bc4e504668e9a2ff8fb73dc086da71328ab","01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills","https:\u002F\u002Fgithub.com\u002FTorchiaHub","2026-06-21 20:40:46",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add TorchiaHub\u002FSkills --skill phoenix-evals",{"tool":57,"command":58},"bunx","bunx skills add TorchiaHub\u002FSkills --skill phoenix-evals",{"tool":60,"command":61},"pnpm","pnpm dlx skills add TorchiaHub\u002FSkills --skill phoenix-evals","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002FSKILL.md",true,[65,71,75,79,83,87,91,95,99,103,107,111,115,119,123,127,131,135,139,143,147,151,155,159,163,167,171,175,179,183,187,191,195,199],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"references\u002Faxial-coding.md","REFERENCE","参考文档",2373,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Faxial-coding.md",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fcommon-mistakes-python.md",7156,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fcommon-mistakes-python.md",{"path":76,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Ferror-analysis-multi-turn.md",1371,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Ferror-analysis-multi-turn.md",{"path":80,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Ferror-analysis.md",4316,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Ferror-analysis.md",{"path":84,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fevaluate-dataframe-python.md",4464,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluate-dataframe-python.md",{"path":88,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fevaluators-code-python.md",2668,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-code-python.md",{"path":92,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fevaluators-code-typescript.md",1207,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-code-typescript.md",{"path":96,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fevaluators-custom-templates.md",1179,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-custom-templates.md",{"path":100,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fevaluators-llm-python.md",2615,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-llm-python.md",{"path":104,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fevaluators-llm-typescript.md",1420,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-llm-typescript.md",{"path":108,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":109,"sourceHtmlUrl":110},"references\u002Fevaluators-overview.md",1132,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-overview.md",{"path":112,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":113,"sourceHtmlUrl":114},"references\u002Fevaluators-pre-built.md",2455,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-pre-built.md",{"path":116,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":117,"sourceHtmlUrl":118},"references\u002Fevaluators-rag.md",3222,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fevaluators-rag.md",{"path":120,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":121,"sourceHtmlUrl":122},"references\u002Fexperiments-datasets-python.md",3578,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fexperiments-datasets-python.md",{"path":124,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":125,"sourceHtmlUrl":126},"references\u002Fexperiments-datasets-typescript.md",1668,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fexperiments-datasets-typescript.md",{"path":128,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":129,"sourceHtmlUrl":130},"references\u002Fexperiments-overview.md",1286,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fexperiments-overview.md",{"path":132,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":133,"sourceHtmlUrl":134},"references\u002Fexperiments-running-python.md",1664,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fexperiments-running-python.md",{"path":136,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":137,"sourceHtmlUrl":138},"references\u002Fexperiments-running-typescript.md",1868,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fexperiments-running-typescript.md",{"path":140,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":141,"sourceHtmlUrl":142},"references\u002Fexperiments-synthetic-python.md",1816,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fexperiments-synthetic-python.md",{"path":144,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":145,"sourceHtmlUrl":146},"references\u002Fexperiments-synthetic-typescript.md",2266,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fexperiments-synthetic-typescript.md",{"path":148,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":149,"sourceHtmlUrl":150},"references\u002Ffundamentals-anti-patterns.md",1246,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Ffundamentals-anti-patterns.md",{"path":152,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":153,"sourceHtmlUrl":154},"references\u002Ffundamentals-model-selection.md",1276,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Ffundamentals-model-selection.md",{"path":156,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":157,"sourceHtmlUrl":158},"references\u002Ffundamentals.md",1835,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Ffundamentals.md",{"path":160,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":161,"sourceHtmlUrl":162},"references\u002Fobserve-sampling-python.md",2415,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fobserve-sampling-python.md",{"path":164,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":165,"sourceHtmlUrl":166},"references\u002Fobserve-sampling-typescript.md",3954,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fobserve-sampling-typescript.md",{"path":168,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":169,"sourceHtmlUrl":170},"references\u002Fobserve-tracing-setup.md",3396,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fobserve-tracing-setup.md",{"path":172,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":173,"sourceHtmlUrl":174},"references\u002Fproduction-continuous.md",3642,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fproduction-continuous.md",{"path":176,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":177,"sourceHtmlUrl":178},"references\u002Fproduction-guardrails.md",1363,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fproduction-guardrails.md",{"path":180,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":181,"sourceHtmlUrl":182},"references\u002Fproduction-overview.md",2255,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fproduction-overview.md",{"path":184,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":185,"sourceHtmlUrl":186},"references\u002Fsetup-python.md",1971,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fsetup-python.md",{"path":188,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":189,"sourceHtmlUrl":190},"references\u002Fsetup-typescript.md",981,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fsetup-typescript.md",{"path":192,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":193,"sourceHtmlUrl":194},"references\u002Fvalidation-evaluators-python.md",1067,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fvalidation-evaluators-python.md",{"path":196,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":197,"sourceHtmlUrl":198},"references\u002Fvalidation-evaluators-typescript.md",4551,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fvalidation-evaluators-typescript.md",{"path":200,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":201,"sourceHtmlUrl":202},"references\u002Fvalidation.md",1764,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T115304Z\u002Fsource\u002Fskills\u002Fphoenix-evals\u002Freferences\u002Fvalidation.md",[204,240,271,301,329,353],{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":208,"sourceHtmlUrl":209,"categories":210,"keywords":219,"publishedAt":35},"01M71VC09WZH7EYAA8YDKJQ0E1","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","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[211,216],{"categoryNo":212,"categoryName":213,"taxonomyType":214,"taxonomyTypeDesc":215},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":217,"categoryName":218,"taxonomyType":214,"taxonomyTypeDesc":215},"01JBSEEDCAT000000000000016","图像与多媒体",[220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239],"resemble detect","detect","images","Video","SET","app","sources","Media","Audio","detect ai","local","generated","ai api","api","analyze","text","paths","Safety","intelligence","deepfake detection",{"skillNo":241,"name":242,"nameZh":6,"description":243,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":244,"licenseFileUrl":6,"sourceHtmlUrl":245,"categories":246,"keywords":253,"publishedAt":35},"01M71VBN3ZPGZT29DK7N7VCT4P","refactor","Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.","MIT","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Frefactor\u002FSKILL.md",[247,250],{"categoryNo":248,"categoryName":249,"taxonomyType":214,"taxonomyTypeDesc":215},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":251,"categoryName":252,"taxonomyType":214,"taxonomyTypeDesc":215},"01JBSEEDCAT000000000000015","视觉设计",[242,254,255,256,257,258,259,260,261,237,262,263,264,265,25,266,267,268,269,270],"design patterns","extracting","improve","design","Variables","code","refactoring","Type Safety","patterns","surgical","code refactoring","down","repo","use","type","functions","behavior",{"skillNo":272,"name":273,"nameZh":6,"description":274,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":244,"licenseFileUrl":275,"sourceHtmlUrl":276,"categories":277,"keywords":282,"publishedAt":35},"01M71VA75R4MGJXGR0JS727PBV","quality-playbook","Explore any codebase from scratch and generate six quality artifacts: a quality constitution (QUALITY.md), spec-traced functional tests, a code review protocol with regression test generation, an integration testing protocol, a multi-model spec audit (Council of Three), and an AI bootstrap file (AGENTS.md). Includes state machine completeness analysis and missing safeguard detection. Works with any language (Python, Java, Scala, TypeScript, Go, Rust, etc.). Use this skill whenever the user asks to set up a quality playbook, generate functional tests from specifications, create a quality constitution, build testing protocols, audit code against specs, or establish a repeatable quality system for a project. Also trigger when the user mentions 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', 'coverage theater', or wants to go beyond basic test generation to build a full quality system grounded in their actual codebase.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[278,279],{"categoryNo":248,"categoryName":249,"taxonomyType":214,"taxonomyTypeDesc":215},{"categoryNo":280,"categoryName":281,"taxonomyType":214,"taxonomyTypeDesc":215},"01JBSEEDCAT000000000000008","测试与质量",[283,284,285,286,287,288,289,290,291,292,293,294,29,295,296,297,298,299,25,300],"playbook","quality playbook","quality","project","code review","integration testing","explore","testing","create","functional","generate","spec audit","go","state machine","full","multi-model","three","system",{"skillNo":302,"name":303,"nameZh":6,"description":304,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":244,"licenseFileUrl":6,"sourceHtmlUrl":305,"categories":306,"keywords":312,"publishedAt":35},"01M71V7TYJZ1C5XD0RRH2P340K","prd","Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fprd\u002FSKILL.md",[307],{"categoryNo":308,"categoryName":309,"taxonomyType":310,"taxonomyTypeDesc":311},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[303,313,25,314,315,316,317,318,319,320,321,293,322,323,285,324,325,326,327,328],"risk","technical","documents","requirements","analysis","stories","summaries","high","product","specifications","risk analysis","features","systems","software","user stories","Product Requirements",{"skillNo":330,"name":331,"nameZh":6,"description":332,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":244,"licenseFileUrl":6,"sourceHtmlUrl":333,"categories":334,"keywords":335,"publishedAt":35},"01M71V7JFHSJ61847CEY1EM48A","power-platform-architect","Use this skill when the user needs to transform business requirements, use case descriptions, or meeting transcripts into a technical Power Platform solution architecture, including component selection and Mermaid.js diagrams.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fpower-platform-architect\u002FSKILL.md",[],[336,337,338,339,340,341,342,314,267,343,344,316,345,346,347,348,349,350,351,352],"power","platform","power platform","architect","power platform architect","diagrams","selection","business","when","architecture","needs","case","solution architecture","component","solution","mermaid","meeting",{"skillNo":354,"name":355,"nameZh":6,"description":356,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":244,"licenseFileUrl":6,"sourceHtmlUrl":357,"categories":358,"keywords":359,"publishedAt":35},"01M71V65KR84TMS1WARE3R11VZ","plantuml-ascii","Generate ASCII art diagrams using PlantUML text mode. Use when user asks to create ASCII diagrams, text-based diagrams, terminal-friendly diagrams, or mentions plantuml ascii, text diagram, ascii art diagram. Supports: Converting PlantUML diagrams to ASCII art, Creating sequence diagrams, class diagrams, flowcharts in ASCII format, Generating Unicode-enhanced ASCII art with -utxt flag","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fplantuml-ascii\u002FSKILL.md",[],[360,361,362,341,363,364,365,344,366,235,291,367,293,368,369,370,371,372,373,267],"plantuml","ascii","plantuml ascii","Unicode","mode","Art","Bash","diagram","terminal","read","write","sequence","plantuml diagrams","ascii art"]