[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M70GG5FD1PQDFYENHX95T40J":3,"skill-same:01M70GG5FD1PQDFYENHX95T40J":110},{"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},"01M70GG5FD1PQDFYENHX95T40J","prompt-engineering-patterns",null,"Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.","shengdabai\u002FTony-Claude-Code-Skills","shengdabai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F116417419?v=4","01M70F6D2WY5PQV5A92BKYJQEW","Tony Sheng","01M70F6D3GAAAYZ5H4CC1G8VXM",3,"MIT","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\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],"patterns","engineering patterns","prompt","prompt engineering","engineering","prompt engineering patterns","prompts","Production","templates","Optimizing Prompts","prompt templates","use","outputs","techniques","master","when","performance","advanced","reliability","2026-11-10 10:12:30",false,"---\nname: prompt-engineering-patterns\ndescription: Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.\n---\n\n# Prompt Engineering Patterns\n\nMaster advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.\n\n## When to Use This Skill\n\n- Designing complex prompts for production LLM applications\n- Optimizing prompt performance and consistency\n- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)\n- Building few-shot learning systems with dynamic example selection\n- Creating reusable prompt templates with variable interpolation\n- Debugging and refining prompts that produce inconsistent outputs\n- Implementing system prompts for specialized AI assistants\n\n## Core Capabilities\n\n### 1. Few-Shot Learning\n- Example selection strategies (semantic similarity, diversity sampling)\n- Balancing example count with context window constraints\n- Constructing effective demonstrations with input-output pairs\n- Dynamic example retrieval from knowledge bases\n- Handling edge cases through strategic example selection\n\n### 2. Chain-of-Thought Prompting\n- Step-by-step reasoning elicitation\n- Zero-shot CoT with \"Let's think step by step\"\n- Few-shot CoT with reasoning traces\n- Self-consistency techniques (sampling multiple reasoning paths)\n- Verification and validation steps\n\n### 3. Prompt Optimization\n- Iterative refinement workflows\n- A\u002FB testing prompt variations\n- Measuring prompt performance metrics (accuracy, consistency, latency)\n- Reducing token usage while maintaining quality\n- Handling edge cases and failure modes\n\n### 4. Template Systems\n- Variable interpolation and formatting\n- Conditional prompt sections\n- Multi-turn conversation templates\n- Role-based prompt composition\n- Modular prompt components\n\n### 5. System Prompt Design\n- Setting model behavior and constraints\n- Defining output formats and structure\n- Establishing role and expertise\n- Safety guidelines and content policies\n- Context setting and background information\n\n## Quick Start\n\n```python\nfrom prompt_optimizer import PromptTemplate, FewShotSelector\n\n# Define a structured prompt template\ntemplate = PromptTemplate(\n    system=\"You are an expert SQL developer. Generate efficient, secure SQL queries.\",\n    instruction=\"Convert the following natural language query to SQL:\\n{query}\",\n    few_shot_examples=True,\n    output_format=\"SQL code block with explanatory comments\"\n)\n\n# Configure few-shot learning\nselector = FewShotSelector(\n    examples_db=\"sql_examples.jsonl\",\n    selection_strategy=\"semantic_similarity\",\n    max_examples=3\n)\n\n# Generate optimized prompt\nprompt = template.render(\n    query=\"Find all users who registered in the last 30 days\",\n    examples=selector.select(query=\"user registration date filter\")\n)\n```\n\n## Key Patterns\n\n### Progressive Disclosure\nStart with simple prompts, add complexity only when needed:\n\n1. **Level 1**: Direct instruction\n   - \"Summarize this article\"\n\n2. **Level 2**: Add constraints\n   - \"Summarize this article in 3 bullet points, focusing on key findings\"\n\n3. **Level 3**: Add reasoning\n   - \"Read this article, identify the main findings, then summarize in 3 bullet points\"\n\n4. **Level 4**: Add examples\n   - Include 2-3 example summaries with input-output pairs\n\n### Instruction Hierarchy\n```\n[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]\n```\n\n### Error Recovery\nBuild prompts that gracefully handle failures:\n- Include fallback instructions\n- Request confidence scores\n- Ask for alternative interpretations when uncertain\n- Specify how to indicate missing information\n\n## Best Practices\n\n1. **Be Specific**: Vague prompts produce inconsistent results\n2. **Show, Don't Tell**: Examples are more effective than descriptions\n3. **Test Extensively**: Evaluate on diverse, representative inputs\n4. **Iterate Rapidly**: Small changes can have large impacts\n5. **Monitor Performance**: Track metrics in production\n6. **Version Control**: Treat prompts as code with proper versioning\n7. **Document Intent**: Explain why prompts are structured as they are\n\n## Common Pitfalls\n\n- **Over-engineering**: Starting with complex prompts before trying simple ones\n- **Example pollution**: Using examples that don't match the target task\n- **Context overflow**: Exceeding token limits with excessive examples\n- **Ambiguous instructions**: Leaving room for multiple interpretations\n- **Ignoring edge cases**: Not testing on unusual or boundary inputs\n\n## Integration Patterns\n\n### With RAG Systems\n```python\n# Combine retrieved context with prompt engineering\nprompt = f\"\"\"Given the following context:\n{retrieved_context}\n\n{few_shot_examples}\n\nQuestion: {user_question}\n\nProvide a detailed answer based solely on the context above. If the context doesn't contain enough information, explicitly state what's missing.\"\"\"\n```\n\n### With Validation\n```python\n# Add self-verification step\nprompt = f\"\"\"{main_task_prompt}\n\nAfter generating your response, verify it meets these criteria:\n1. Answers the question directly\n2. Uses only information from provided context\n3. Cites specific sources\n4. Acknowledges any uncertainty\n\nIf verification fails, revise your response.\"\"\"\n```\n\n## Performance Optimization\n\n### Token Efficiency\n- Remove redundant words and phrases\n- Use abbreviations consistently after first definition\n- Consolidate similar instructions\n- Move stable content to system prompts\n\n### Latency Reduction\n- Minimize prompt length without sacrificing quality\n- Use streaming for long-form outputs\n- Cache common prompt prefixes\n- Batch similar requests when possible\n\n## Resources\n\n- **references\u002Ffew-shot-learning.md**: Deep dive on example selection and construction\n- **references\u002Fchain-of-thought.md**: Advanced reasoning elicitation techniques\n- **references\u002Fprompt-optimization.md**: Systematic refinement workflows\n- **references\u002Fprompt-templates.md**: Reusable template patterns\n- **references\u002Fsystem-prompts.md**: System-level prompt design\n- **assets\u002Fprompt-template-library.md**: Battle-tested prompt templates\n- **assets\u002Ffew-shot-examples.json**: Curated example datasets\n- **scripts\u002Foptimize-prompt.py**: Automated prompt optimization tool\n\n## Success Metrics\n\nTrack these KPIs for your prompts:\n- **Accuracy**: Correctness of outputs\n- **Consistency**: Reproducibility across similar inputs\n- **Latency**: Response time (P50, P95, P99)\n- **Token Usage**: Average tokens per request\n- **Success Rate**: Percentage of valid outputs\n- **User Satisfaction**: Ratings and feedback\n\n## Next Steps\n\n1. Review the prompt template library for common patterns\n2. Experiment with few-shot learning for your specific use case\n3. Implement prompt versioning and A\u002FB testing\n4. Set up automated evaluation pipelines\n5. Document your prompt engineering decisions and learnings\n","# Prompt Engineering Patterns\n\nMaster advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.\n\n## When to Use This Skill\n\n- Designing complex prompts for production LLM applications\n- Optimizing prompt performance and consistency\n- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)\n- Building few-shot learning systems with dynamic example selection\n- Creating reusable prompt templates with variable interpolation\n- Debugging and refining prompts that produce inconsistent outputs\n- Implementing system prompts for specialized AI assistants\n\n## Core Capabilities\n\n### 1. Few-Shot Learning\n- Example selection strategies (semantic similarity, diversity sampling)\n- Balancing example count with context window constraints\n- Constructing effective demonstrations with input-output pairs\n- Dynamic example retrieval from knowledge bases\n- Handling edge cases through strategic example selection\n\n### 2. Chain-of-Thought Prompting\n- Step-by-step reasoning elicitation\n- Zero-shot CoT with \"Let's think step by step\"\n- Few-shot CoT with reasoning traces\n- Self-consistency techniques (sampling multiple reasoning paths)\n- Verification and validation steps\n\n### 3. Prompt Optimization\n- Iterative refinement workflows\n- A\u002FB testing prompt variations\n- Measuring prompt performance metrics (accuracy, consistency, latency)\n- Reducing token usage while maintaining quality\n- Handling edge cases and failure modes\n\n### 4. Template Systems\n- Variable interpolation and formatting\n- Conditional prompt sections\n- Multi-turn conversation templates\n- Role-based prompt composition\n- Modular prompt components\n\n### 5. System Prompt Design\n- Setting model behavior and constraints\n- Defining output formats and structure\n- Establishing role and expertise\n- Safety guidelines and content policies\n- Context setting and background information\n\n## Quick Start\n\n```python\nfrom prompt_optimizer import PromptTemplate, FewShotSelector\n\n# Define a structured prompt template\ntemplate = PromptTemplate(\n    system=\"You are an expert SQL developer. Generate efficient, secure SQL queries.\",\n    instruction=\"Convert the following natural language query to SQL:\\n{query}\",\n    few_shot_examples=True,\n    output_format=\"SQL code block with explanatory comments\"\n)\n\n# Configure few-shot learning\nselector = FewShotSelector(\n    examples_db=\"sql_examples.jsonl\",\n    selection_strategy=\"semantic_similarity\",\n    max_examples=3\n)\n\n# Generate optimized prompt\nprompt = template.render(\n    query=\"Find all users who registered in the last 30 days\",\n    examples=selector.select(query=\"user registration date filter\")\n)\n```\n\n## Key Patterns\n\n### Progressive Disclosure\nStart with simple prompts, add complexity only when needed:\n\n1. **Level 1**: Direct instruction\n   - \"Summarize this article\"\n\n2. **Level 2**: Add constraints\n   - \"Summarize this article in 3 bullet points, focusing on key findings\"\n\n3. **Level 3**: Add reasoning\n   - \"Read this article, identify the main findings, then summarize in 3 bullet points\"\n\n4. **Level 4**: Add examples\n   - Include 2-3 example summaries with input-output pairs\n\n### Instruction Hierarchy\n```\n[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]\n```\n\n### Error Recovery\nBuild prompts that gracefully handle failures:\n- Include fallback instructions\n- Request confidence scores\n- Ask for alternative interpretations when uncertain\n- Specify how to indicate missing information\n\n## Best Practices\n\n1. **Be Specific**: Vague prompts produce inconsistent results\n2. **Show, Don't Tell**: Examples are more effective than descriptions\n3. **Test Extensively**: Evaluate on diverse, representative inputs\n4. **Iterate Rapidly**: Small changes can have large impacts\n5. **Monitor Performance**: Track metrics in production\n6. **Version Control**: Treat prompts as code with proper versioning\n7. **Document Intent**: Explain why prompts are structured as they are\n\n## Common Pitfalls\n\n- **Over-engineering**: Starting with complex prompts before trying simple ones\n- **Example pollution**: Using examples that don't match the target task\n- **Context overflow**: Exceeding token limits with excessive examples\n- **Ambiguous instructions**: Leaving room for multiple interpretations\n- **Ignoring edge cases**: Not testing on unusual or boundary inputs\n\n## Integration Patterns\n\n### With RAG Systems\n```python\n# Combine retrieved context with prompt engineering\nprompt = f\"\"\"Given the following context:\n{retrieved_context}\n\n{few_shot_examples}\n\nQuestion: {user_question}\n\nProvide a detailed answer based solely on the context above. If the context doesn't contain enough information, explicitly state what's missing.\"\"\"\n```\n\n### With Validation\n```python\n# Add self-verification step\nprompt = f\"\"\"{main_task_prompt}\n\nAfter generating your response, verify it meets these criteria:\n1. Answers the question directly\n2. Uses only information from provided context\n3. Cites specific sources\n4. Acknowledges any uncertainty\n\nIf verification fails, revise your response.\"\"\"\n```\n\n## Performance Optimization\n\n### Token Efficiency\n- Remove redundant words and phrases\n- Use abbreviations consistently after first definition\n- Consolidate similar instructions\n- Move stable content to system prompts\n\n### Latency Reduction\n- Minimize prompt length without sacrificing quality\n- Use streaming for long-form outputs\n- Cache common prompt prefixes\n- Batch similar requests when possible\n\n## Resources\n\n- **references\u002Ffew-shot-learning.md**: Deep dive on example selection and construction\n- **references\u002Fchain-of-thought.md**: Advanced reasoning elicitation techniques\n- **references\u002Fprompt-optimization.md**: Systematic refinement workflows\n- **references\u002Fprompt-templates.md**: Reusable template patterns\n- **references\u002Fsystem-prompts.md**: System-level prompt design\n- **assets\u002Fprompt-template-library.md**: Battle-tested prompt templates\n- **assets\u002Ffew-shot-examples.json**: Curated example datasets\n- **scripts\u002Foptimize-prompt.py**: Automated prompt optimization tool\n\n## Success Metrics\n\nTrack these KPIs for your prompts:\n- **Accuracy**: Correctness of outputs\n- **Consistency**: Reproducibility across similar inputs\n- **Latency**: Response time (P50, P95, P99)\n- **Token Usage**: Average tokens per request\n- **Success Rate**: Percentage of valid outputs\n- **User Satisfaction**: Ratings and feedback\n\n## Next Steps\n\n1. Review the prompt template library for common patterns\n2. Experiment with few-shot learning for your specific use case\n3. Implement prompt versioning and A\u002FB testing\n4. Set up automated evaluation pipelines\n5. Document your prompt engineering decisions and learnings\n",{"name":5,"description":7},"main","7850740bd837adb32f0a109ab2af82482196c6f2","skills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns",1,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills","https:\u002F\u002Fzturnsgo.com","https:\u002F\u002Fgithub.com\u002Fshengdabai","https:\u002F\u002Fzturnsgo.com\u002F","AI × education creator — producer, not consumer. Taught 6,000+ learners; shipping learning tools & daily essays with Claude Code, practicing @xiaolai's way 🥋","2026-11-01 21:28:23",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add shengdabai\u002FTony-Claude-Code-Skills --skill prompt-engineering-patterns",{"tool":64,"command":65},"bunx","bunx skills add shengdabai\u002FTony-Claude-Code-Skills --skill prompt-engineering-patterns",{"tool":67,"command":68},"pnpm","pnpm dlx skills add shengdabai\u002FTony-Claude-Code-Skills --skill prompt-engineering-patterns","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002FSKILL.md",true,[72,78,82,88,92,96,100,104],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"assets\u002Ffew-shot-examples.json","ASSET","资产文件",4174,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Fassets\u002Ffew-shot-examples.json",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"assets\u002Fprompt-template-library.md",3273,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Fassets\u002Fprompt-template-library.md",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fchain-of-thought.md","REFERENCE","参考文档",9386,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Freferences\u002Fchain-of-thought.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Ffew-shot-learning.md",11171,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Freferences\u002Ffew-shot-learning.md",{"path":93,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fprompt-optimization.md",12778,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Freferences\u002Fprompt-optimization.md",{"path":97,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Fprompt-templates.md",11395,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Freferences\u002Fprompt-templates.md",{"path":101,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fsystem-prompts.md",5438,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Freferences\u002Fsystem-prompts.md",{"path":105,"resourceType":106,"resourceTypeDesc":107,"sizeBytes":108,"sourceHtmlUrl":109},"scripts\u002Foptimize-prompt.py","SCRIPT","脚本",9159,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002F_collections\u002Fplugins\u002Fllm-application-dev\u002Fskills\u002Fprompt-engineering-patterns\u002Fscripts\u002Foptimize-prompt.py",[111,128,160,185,207,227],{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":120,"publishedAt":43},"01M70P17DAD4C1C47NPH6A96EM","xiaolai-methodology","以李笑来(Li Xiaolai)的思考方式、方法论与表达风格,为用户做全方位 1-on-1 教练指导,覆盖创业、赚钱、投资、学习、自学、读书写作、用 AI、以及人生心态与自我管理。当用户提到 \"李笑来\"、\"笑来\"、\"笑来老师\"、\"扮演李笑来\"、\"找笑来聊聊\"、\"笑来怎么看\"、\"xiaolai\"、\"把时间当作朋友\"、\"通往财富自由之路\"、\"定投改变命运\"、\"韭菜的自我修养\"、\"自学是门手艺\"、\"让时间陪你慢慢变富\"、\"七年就是一辈子\"、\"元认知\"、\"活在未来\"、\"复利\u002F长期主义\"、\"践行\"、\"痴迷改进\"、\"个人商业模式\u002F出售时间\"、\"睡后收入\"、\"定投\"、\"财富自由\"、\"操作系统升级\"、\"最少必要知识\"、\"北极星提示词\"、\"用 AI 该怎么学\u002F思考\" 等任一触发词,或当用户带着关于创业\u002F赚钱\u002F投资\u002F学习\u002F职业\u002F重大人生决策的困惑、希望被人用笑来式的「逻辑 + 大白话 + 类比 + 重新定义 + 践行导向」来点醒和指导时,自动进入李笑来教练模式。","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fxiaolai-methodology\u002FSKILL.md",[117],{"categoryNo":118,"categoryName":119,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",[121,122,123,124,125,126,127],"methodology","xiaolai","xiaolai methodology","朋友","投资","writing","收入",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":140,"publishedAt":43},"01M70P13NB9YP7ACJR1S861CDS","zread","Produce and consume a wiki-style knowledge base for a code repository via the `zread` CLI and its on-disk output under `.\u002F.zread\u002Fwiki\u002F`. Use this skill whenever the user wants to understand, onboard onto, explore, summarize, map, or get an overview of an unfamiliar codebase; asks for architecture docs, a project wiki, a repo walkthrough, module\u002Fpackage explanations, or \"what does this repo do\"; wants to generate, regenerate, resume, browse, or serve code documentation locally; or mentions zread \u002F zread.ai directly. Also use it proactively before diving into a large unknown repo — if `.\u002F.zread\u002Fwiki\u002Fcurrent` exists, read the generated pages instead of crawling source file-by-file; if it doesn't, consider offering to run `zread generate`. The trigger is the intent (understand a codebase through generated docs), not the literal word \"zread\".","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fzread\u002FSKILL.md",[134,135],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":136,"categoryName":137,"taxonomyType":138,"taxonomyTypeDesc":139},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[130,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159],"knowledge","wiki","explore","walkthrough","project","get","knowledge base","run","generate","intent","cli","map","documentation","project wiki","disk","architecture","code","unknown","generated",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":170,"publishedAt":43},"01M70P10S4SWMZQ8BJAFBFDRW3","youtube","Launch the YouTube Creator Agent to auto-generate videos.\nUse when: user types \u002Fyoutube, wants to create YouTube videos,\ngenerate video content, or upload to YouTube.\nKeywords: youtube, video, 视频, 创作视频, 发布视频","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube\u002FSKILL.md",[166,167],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":168,"categoryName":169,"taxonomyType":138,"taxonomyTypeDesc":139},"01JBSEEDCAT000000000000036","内容创作者",[162,171,172,173,174,175,176,177,149,178,179,180,39,35,181,182,183,184],"user","content","视频","creator","generate video","videos","launch","video content","create","upload","auto","types","Video","创作",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":192,"publishedAt":43},"01M70P0YE95D8BACZCGV8BK90K","youtube-script-sync","Sync, update, or optimize Tony's GetNote knowledge base `Youtube视频逐字稿` by turning each parent recording or first-person idea note into an idempotent child note containing a directly recordable YouTube long-video or Shorts script. Use when the user says “更新\u002F同步\u002F优化 Youtube视频逐字稿知识库”, or says “更新\u002F同步\u002F优化该知识库” while this knowledge base is the active context.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-script-sync\u002FSKILL.md",[191],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[193,194,162,195,196,183,197,198,199,200,201,202,203,204,147,205,39,35,141,206],"sync","script","youtube script sync","recording","shorts","first","base","person","update","active","child","optimize","idempotent","long",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":217,"publishedAt":43},"01M70P0WMV5XT0EEC0N445PW07","writing-skills","How to write SKILL.md files that trigger reliably and teach effectively. Use when creating, improving, or reviewing Claude Code skills.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fxiaolai\u002Fnlpm\u002Fskills\u002Fnlpm\u002Fwriting-skills\u002FSKILL.md",[213,214,215,216],{"categoryNo":118,"categoryName":119,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":136,"categoryName":137,"taxonomyType":138,"taxonomyTypeDesc":139},{"categoryNo":168,"categoryName":169,"taxonomyType":138,"taxonomyTypeDesc":139},[218,126,219,220,221,222,157,39,35,223,224,225,226],"writing skills","how","teach","write skill","write","claude code skills","files","how to","trigger",{"skillNo":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":231,"categories":232,"keywords":235,"publishedAt":43},"01M70P0SJFR4YVJQBGMHKGSKXT","writing-rules","How to write .claude\u002Frules\u002F files that Claude actually follows. Use when creating, improving, or reviewing project rules.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fxiaolai\u002Fnlpm\u002Fskills\u002Fnlpm\u002Fwriting-rules\u002FSKILL.md",[233,234],{"categoryNo":118,"categoryName":119,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":168,"categoryName":169,"taxonomyType":138,"taxonomyTypeDesc":139},[236,126,237,145,238,224,39,219,222,35,225],"Rules","writing rules","project rules"]