[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADY5G9Y75QZ4EZP7YS7CF5":3,"skill-same:01M6ADY5G9Y75QZ4EZP7YS7CF5":73},{"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":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6ADY5G9Y75QZ4EZP7YS7CF5","prompt-engineering",null,"Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering\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],"prompt engineering","engineering","prompt","when","techniques","prompting","strategies","patterns","expert","use","guide","behavior","improve","prompt engineering patterns","Optimization","best practices","learn","engineering patterns","debug","debug agent","2026-11-10 10:12:30",false,"---\nname: prompt-engineering\ndescription: Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.\n---\n\n# Prompt Engineering Patterns\n\nAdvanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.\n\n## Core Capabilities\n\n### 1. Few-Shot Learning\n\nTeach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity.\n\n**Example:**\n\n```markdown\nExtract key information from support tickets:\n\nInput: \"My login doesn't work and I keep getting error 403\"\nOutput: {\"issue\": \"authentication\", \"error_code\": \"403\", \"priority\": \"high\"}\n\nInput: \"Feature request: add dark mode to settings\"\nOutput: {\"issue\": \"feature_request\", \"error_code\": null, \"priority\": \"low\"}\n\nNow process: \"Can't upload files larger than 10MB, getting timeout\"\n```\n\n### 2. Chain-of-Thought Prompting\n\nRequest step-by-step reasoning before the final answer. Add \"Let's think step by step\" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%.\n\n**Example:**\n\n```markdown\nAnalyze this bug report and determine root cause.\n\nThink step by step:\n\n1. What is the expected behavior?\n2. What is the actual behavior?\n3. What changed recently that could cause this?\n4. What components are involved?\n5. What is the most likely root cause?\n\nBug: \"Users can't save drafts after the cache update deployed yesterday\"\n```\n\n### 3. Prompt Optimization\n\nSystematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A\u002FB testing to compare variations. Critical for production prompts where consistency and cost matter.\n\n**Example:**\n\n```markdown\nVersion 1 (Simple): \"Summarize this article\"\n→ Result: Inconsistent length, misses key points\n\nVersion 2 (Add constraints): \"Summarize in 3 bullet points\"\n→ Result: Better structure, but still misses nuance\n\nVersion 3 (Add reasoning): \"Identify the 3 main findings, then summarize each\"\n→ Result: Consistent, accurate, captures key information\n```\n\n### 4. Template Systems\n\nBuild reusable prompt structures with variables, conditional sections, and modular components. Use for multi-turn conversations, role-based interactions, or when the same pattern applies to different inputs. Reduces duplication and ensures consistency across similar tasks.\n\n**Example:**\n\n```python\n# Reusable code review template\ntemplate = \"\"\"\nReview this {language} code for {focus_area}.\n\nCode:\n{code_block}\n\nProvide feedback on:\n{checklist}\n\"\"\"\n\n# Usage\nprompt = template.format(\n    language=\"Python\",\n    focus_area=\"security vulnerabilities\",\n    code_block=user_code,\n    checklist=\"1. SQL injection\\n2. XSS risks\\n3. Authentication\"\n)\n```\n\n### 5. System Prompt Design\n\nSet global behavior and constraints that persist across the conversation. Define the model's role, expertise level, output format, and safety guidelines. Use system prompts for stable instructions that shouldn't change turn-to-turn, freeing up user message tokens for variable content.\n\n**Example:**\n\n```markdown\nSystem: You are a senior backend engineer specializing in API design.\n\nRules:\n\n- Always consider scalability and performance\n- Suggest RESTful patterns by default\n- Flag security concerns immediately\n- Provide code examples in Python\n- Use early return pattern\n\nFormat responses as:\n\n1. Analysis\n2. Recommendation\n3. Code example\n4. Trade-offs\n```\n\n## Key Patterns\n\n### Progressive Disclosure\n\nStart with simple prompts, add complexity only when needed:\n\n1. **Level 1**: Direct instruction\n\n   - \"Summarize this article\"\n\n2. **Level 2**: Add constraints\n\n   - \"Summarize this article in 3 bullet points, focusing on key findings\"\n\n3. **Level 3**: Add reasoning\n\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```\n[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]\n```\n\n### Error Recovery\n\nBuild prompts that gracefully handle failures:\n\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","# Prompt Engineering Patterns\n\nAdvanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.\n\n## Core Capabilities\n\n### 1. Few-Shot Learning\n\nTeach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity.\n\n**Example:**\n\n```markdown\nExtract key information from support tickets:\n\nInput: \"My login doesn't work and I keep getting error 403\"\nOutput: {\"issue\": \"authentication\", \"error_code\": \"403\", \"priority\": \"high\"}\n\nInput: \"Feature request: add dark mode to settings\"\nOutput: {\"issue\": \"feature_request\", \"error_code\": null, \"priority\": \"low\"}\n\nNow process: \"Can't upload files larger than 10MB, getting timeout\"\n```\n\n### 2. Chain-of-Thought Prompting\n\nRequest step-by-step reasoning before the final answer. Add \"Let's think step by step\" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%.\n\n**Example:**\n\n```markdown\nAnalyze this bug report and determine root cause.\n\nThink step by step:\n\n1. What is the expected behavior?\n2. What is the actual behavior?\n3. What changed recently that could cause this?\n4. What components are involved?\n5. What is the most likely root cause?\n\nBug: \"Users can't save drafts after the cache update deployed yesterday\"\n```\n\n### 3. Prompt Optimization\n\nSystematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A\u002FB testing to compare variations. Critical for production prompts where consistency and cost matter.\n\n**Example:**\n\n```markdown\nVersion 1 (Simple): \"Summarize this article\"\n→ Result: Inconsistent length, misses key points\n\nVersion 2 (Add constraints): \"Summarize in 3 bullet points\"\n→ Result: Better structure, but still misses nuance\n\nVersion 3 (Add reasoning): \"Identify the 3 main findings, then summarize each\"\n→ Result: Consistent, accurate, captures key information\n```\n\n### 4. Template Systems\n\nBuild reusable prompt structures with variables, conditional sections, and modular components. Use for multi-turn conversations, role-based interactions, or when the same pattern applies to different inputs. Reduces duplication and ensures consistency across similar tasks.\n\n**Example:**\n\n```python\n# Reusable code review template\ntemplate = \"\"\"\nReview this {language} code for {focus_area}.\n\nCode:\n{code_block}\n\nProvide feedback on:\n{checklist}\n\"\"\"\n\n# Usage\nprompt = template.format(\n    language=\"Python\",\n    focus_area=\"security vulnerabilities\",\n    code_block=user_code,\n    checklist=\"1. SQL injection\\n2. XSS risks\\n3. Authentication\"\n)\n```\n\n### 5. System Prompt Design\n\nSet global behavior and constraints that persist across the conversation. Define the model's role, expertise level, output format, and safety guidelines. Use system prompts for stable instructions that shouldn't change turn-to-turn, freeing up user message tokens for variable content.\n\n**Example:**\n\n```markdown\nSystem: You are a senior backend engineer specializing in API design.\n\nRules:\n\n- Always consider scalability and performance\n- Suggest RESTful patterns by default\n- Flag security concerns immediately\n- Provide code examples in Python\n- Use early return pattern\n\nFormat responses as:\n\n1. Analysis\n2. Recommendation\n3. Code example\n4. Trade-offs\n```\n\n## Key Patterns\n\n### Progressive Disclosure\n\nStart with simple prompts, add complexity only when needed:\n\n1. **Level 1**: Direct instruction\n\n   - \"Summarize this article\"\n\n2. **Level 2**: Add constraints\n\n   - \"Summarize this article in 3 bullet points, focusing on key findings\"\n\n3. **Level 3**: Add reasoning\n\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```\n[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]\n```\n\n### Error Recovery\n\nBuild prompts that gracefully handle failures:\n\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",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering",{"tool":65,"command":66},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering",{"tool":68,"command":69},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering\u002FSKILL.md",true,[],[74,100,127,160,195,218],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":81,"publishedAt":44},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[80],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[82,83,84,85,86,87,88,89,33,27,90,91,92,93,94,95,96,97,98,99],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":107,"publishedAt":44},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[106],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[108,109,110,111,112,113,114,115,116,117,118,27,119,120,121,122,123,124,125,126],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":142,"publishedAt":44},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[133,136,137],{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":138,"categoryName":139,"taxonomyType":140,"taxonomyTypeDesc":141},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[143,144,145,146,147,148,149,87,150,151,152,153,154,155,156,33,157,158,27,159],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"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":164,"licenseFileUrl":165,"sourceHtmlUrl":166,"categories":167,"keywords":171,"publishedAt":44},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[168],{"categoryNo":169,"categoryName":170,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,87,194],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":164,"licenseFileUrl":199,"sourceHtmlUrl":200,"categories":201,"keywords":206,"publishedAt":44},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[202,205],{"categoryNo":203,"categoryName":204,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":169,"categoryName":170,"taxonomyType":21,"taxonomyTypeDesc":22},[207,208,209,210,211,173,212,182,183,213,214,215,216,87,217,91,35],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local",{"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":164,"licenseFileUrl":222,"sourceHtmlUrl":223,"categories":224,"keywords":232,"publishedAt":44},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[225,228,229],{"categoryNo":226,"categoryName":227,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":230,"categoryName":231,"taxonomyType":140,"taxonomyTypeDesc":141},"01JBSEEDCAT000000000000036","内容创作者",[233,234,235,236,237,238,239,240,175,241,185,119,242,243,244,245,246,155,247,27,248,249,250,251,252,97,253],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]