[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQ56XYC4B4MA0P9YD7WQXJ":3,"skill-same:01M6KQ56XYC4B4MA0P9YD7WQXJ":113},{"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":34,"publishedAt":55,"linkOnly":56,"markdownContent":57,"markdownBody":58,"frontMatter":59,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":14,"repositoryHtmlUrl":67,"repositoryHomepageUrl":6,"ownerHtmlUrl":68,"ownerBlogUrl":6,"ownerDescription":69,"updatedAt":70,"install":71,"resources":84},"01M6KQ56XYC4B4MA0P9YD7WQXJ","prompt-engineer-toolkit",null,"Analyzes and rewrites prompts for better AI output, creates reusable prompt templates for marketing use cases (ad copy, email campaigns, social media), and structures end-to-end AI content workflows. Use when the user wants to improve prompts for AI-assisted marketing, build prompt templates, or optimize AI content workflows. Also use when the user mentions 'prompt engineering,' 'improve my prompts,' 'AI writing quality,' 'prompt templates,' or 'AI content workflow.'","Boboegg\u002Fai-resources","Boboegg","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F171472260?v=4","01M6KMJYEA4SVHDTXRTH3HZANA","曾柏儒","01M6KMJYJ4231XB9W048VZ8RBB",1,"MIT","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FSKILL.md",[18,23,26,31],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",{"categoryNo":32,"categoryName":33,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"toolkit","prompt engineer","prompt","prompt engineer toolkit","engineer","for","writing","my","end","campaigns","social","ai content workflow","optimize","social media","marketing","improve","Media","email","workflows","workflow","2026-11-10 10:12:30",false,"---\nname: \"prompt-engineer-toolkit\"\ndescription: \"Analyzes and rewrites prompts for better AI output, creates reusable prompt templates for marketing use cases (ad copy, email campaigns, social media), and structures end-to-end AI content workflows. Use when the user wants to improve prompts for AI-assisted marketing, build prompt templates, or optimize AI content workflows. Also use when the user mentions 'prompt engineering,' 'improve my prompts,' 'AI writing quality,' 'prompt templates,' or 'AI content workflow.'\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: marketing\n  updated: 2026-03-06\n---\n\n# Prompt Engineer Toolkit\n\n## Overview\n\nUse this skill to move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. It emphasizes measurable quality over intuition. Apply it when launching a new LLM feature that needs reliable outputs, when prompt quality degrades after model or instruction changes, when multiple team members edit prompts and need history\u002Fdiffs, when you need evidence-based prompt choice for production rollout, or when you want consistent prompt governance across environments.\n\n## Core Capabilities\n\n- A\u002FB prompt evaluation against structured test cases\n- Quantitative scoring for adherence, relevance, and safety checks\n- Prompt version tracking with immutable history and changelog\n- Prompt diffs to review behavior-impacting edits\n- Reusable prompt templates and selection guidance\n- Regression-friendly workflows for model\u002Fprompt updates\n\n## Key Workflows\n\n### 1. Run Prompt A\u002FB Test\n\nPrepare JSON test cases and run:\n\n```bash\npython3 scripts\u002Fprompt_tester.py \\\n  --prompt-a-file prompts\u002Fa.txt \\\n  --prompt-b-file prompts\u002Fb.txt \\\n  --cases-file testcases.json \\\n  --runner-cmd 'my-llm-cli --prompt {prompt} --input {input}' \\\n  --format text\n```\n\nInput can also come from stdin\u002F`--input` JSON payload.\n\n### 2. Choose Winner With Evidence\n\nThe tester scores outputs per case and aggregates:\n\n- expected content coverage\n- forbidden content violations\n- regex\u002Fformat compliance\n- output length sanity\n\nUse the higher-scoring prompt as candidate baseline, then run regression suite.\n\n### 3. Version Prompts\n\n```bash\n# Add version\npython3 scripts\u002Fprompt_versioner.py add \\\n  --name support_classifier \\\n  --prompt-file prompts\u002Fsupport_v3.txt \\\n  --author alice\n\n# Diff versions\npython3 scripts\u002Fprompt_versioner.py diff --name support_classifier --from-version 2 --to-version 3\n\n# Changelog\npython3 scripts\u002Fprompt_versioner.py changelog --name support_classifier\n```\n\n### 4. Regression Loop\n\n1. Store baseline version.\n2. Propose prompt edits.\n3. Re-run A\u002FB test.\n4. Promote only if score and safety constraints improve.\n\n## Script Interfaces\n\n- `python3 scripts\u002Fprompt_tester.py --help`\n  - Reads prompts\u002Fcases from stdin or `--input`\n  - Optional external runner command\n  - Emits text or JSON metrics\n- `python3 scripts\u002Fprompt_versioner.py --help`\n  - Manages prompt history (`add`, `list`, `diff`, `changelog`)\n  - Stores metadata and content snapshots locally\n\n## Pitfalls, Best Practices & Review Checklist\n\n**Avoid these mistakes:**\n1. Picking prompts from single-case outputs — use a realistic, edge-case-rich test suite.\n2. Changing prompt and model simultaneously — always isolate variables.\n3. Missing `must_not_contain` (forbidden-content) checks in evaluation criteria.\n4. Editing prompts without version metadata, author, or change rationale.\n5. Skipping semantic diffs before deploying a new prompt version.\n6. Optimizing one benchmark while harming edge cases — track the full suite.\n7. Model swap without rerunning the baseline A\u002FB suite.\n\n**Before promoting any prompt, confirm:**\n- [ ] Task intent is explicit and unambiguous.\n- [ ] Output schema\u002Fformat is explicit.\n- [ ] Safety and exclusion constraints are explicit.\n- [ ] No contradictory instructions.\n- [ ] No unnecessary verbosity tokens.\n- [ ] A\u002FB score improves and violation count stays at zero.\n\n## References\n\n- [references\u002Fprompt-templates.md](references\u002Fprompt-templates.md)\n- [references\u002Ftechnique-guide.md](references\u002Ftechnique-guide.md)\n- [references\u002Fevaluation-rubric.md](references\u002Fevaluation-rubric.md)\n- [README.md](README.md)\n\n## Evaluation Design\n\nEach test case should define:\n\n- `input`: realistic production-like input\n- `expected_contains`: required markers\u002Fcontent\n- `forbidden_contains`: disallowed phrases or unsafe content\n- `expected_regex`: required structural patterns\n\nThis enables deterministic grading across prompt variants.\n\n## Versioning Policy\n\n- Use semantic prompt identifiers per feature (`support_classifier`, `ad_copy_shortform`).\n- Record author + change note for every revision.\n- Never overwrite historical versions.\n- Diff before promoting a new prompt to production.\n\n## Rollout Strategy\n\n1. Create baseline prompt version.\n2. Propose candidate prompt.\n3. Run A\u002FB suite against same cases.\n4. Promote only if winner improves average and keeps violation count at zero.\n5. Track post-release feedback and feed new failure cases back into test suite.\n","# Prompt Engineer Toolkit\n\n## Overview\n\nUse this skill to move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. It emphasizes measurable quality over intuition. Apply it when launching a new LLM feature that needs reliable outputs, when prompt quality degrades after model or instruction changes, when multiple team members edit prompts and need history\u002Fdiffs, when you need evidence-based prompt choice for production rollout, or when you want consistent prompt governance across environments.\n\n## Core Capabilities\n\n- A\u002FB prompt evaluation against structured test cases\n- Quantitative scoring for adherence, relevance, and safety checks\n- Prompt version tracking with immutable history and changelog\n- Prompt diffs to review behavior-impacting edits\n- Reusable prompt templates and selection guidance\n- Regression-friendly workflows for model\u002Fprompt updates\n\n## Key Workflows\n\n### 1. Run Prompt A\u002FB Test\n\nPrepare JSON test cases and run:\n\n```bash\npython3 scripts\u002Fprompt_tester.py \\\n  --prompt-a-file prompts\u002Fa.txt \\\n  --prompt-b-file prompts\u002Fb.txt \\\n  --cases-file testcases.json \\\n  --runner-cmd 'my-llm-cli --prompt {prompt} --input {input}' \\\n  --format text\n```\n\nInput can also come from stdin\u002F`--input` JSON payload.\n\n### 2. Choose Winner With Evidence\n\nThe tester scores outputs per case and aggregates:\n\n- expected content coverage\n- forbidden content violations\n- regex\u002Fformat compliance\n- output length sanity\n\nUse the higher-scoring prompt as candidate baseline, then run regression suite.\n\n### 3. Version Prompts\n\n```bash\n# Add version\npython3 scripts\u002Fprompt_versioner.py add \\\n  --name support_classifier \\\n  --prompt-file prompts\u002Fsupport_v3.txt \\\n  --author alice\n\n# Diff versions\npython3 scripts\u002Fprompt_versioner.py diff --name support_classifier --from-version 2 --to-version 3\n\n# Changelog\npython3 scripts\u002Fprompt_versioner.py changelog --name support_classifier\n```\n\n### 4. Regression Loop\n\n1. Store baseline version.\n2. Propose prompt edits.\n3. Re-run A\u002FB test.\n4. Promote only if score and safety constraints improve.\n\n## Script Interfaces\n\n- `python3 scripts\u002Fprompt_tester.py --help`\n  - Reads prompts\u002Fcases from stdin or `--input`\n  - Optional external runner command\n  - Emits text or JSON metrics\n- `python3 scripts\u002Fprompt_versioner.py --help`\n  - Manages prompt history (`add`, `list`, `diff`, `changelog`)\n  - Stores metadata and content snapshots locally\n\n## Pitfalls, Best Practices & Review Checklist\n\n**Avoid these mistakes:**\n1. Picking prompts from single-case outputs — use a realistic, edge-case-rich test suite.\n2. Changing prompt and model simultaneously — always isolate variables.\n3. Missing `must_not_contain` (forbidden-content) checks in evaluation criteria.\n4. Editing prompts without version metadata, author, or change rationale.\n5. Skipping semantic diffs before deploying a new prompt version.\n6. Optimizing one benchmark while harming edge cases — track the full suite.\n7. Model swap without rerunning the baseline A\u002FB suite.\n\n**Before promoting any prompt, confirm:**\n- [ ] Task intent is explicit and unambiguous.\n- [ ] Output schema\u002Fformat is explicit.\n- [ ] Safety and exclusion constraints are explicit.\n- [ ] No contradictory instructions.\n- [ ] No unnecessary verbosity tokens.\n- [ ] A\u002FB score improves and violation count stays at zero.\n\n## References\n\n- [references\u002Fprompt-templates.md](references\u002Fprompt-templates.md)\n- [references\u002Ftechnique-guide.md](references\u002Ftechnique-guide.md)\n- [references\u002Fevaluation-rubric.md](references\u002Fevaluation-rubric.md)\n- [README.md](README.md)\n\n## Evaluation Design\n\nEach test case should define:\n\n- `input`: realistic production-like input\n- `expected_contains`: required markers\u002Fcontent\n- `forbidden_contains`: disallowed phrases or unsafe content\n- `expected_regex`: required structural patterns\n\nThis enables deterministic grading across prompt variants.\n\n## Versioning Policy\n\n- Use semantic prompt identifiers per feature (`support_classifier`, `ad_copy_shortform`).\n- Record author + change note for every revision.\n- Never overwrite historical versions.\n- Diff before promoting a new prompt to production.\n\n## Rollout Strategy\n\n1. Create baseline prompt version.\n2. Propose candidate prompt.\n3. Run A\u002FB suite against same cases.\n4. Promote only if winner improves average and keeps violation count at zero.\n5. Track post-release feedback and feed new failure cases back into test suite.\n",{"name":5,"license":15,"metadata":60,"description":7},{"author":61,"updated":62,"version":63,"category":49},"Alireza Rezvani",1772755200000,"1.0.0","main","cfe69e68b9b427ce0b098a2306c9898096ef0ac2","claude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources","https:\u002F\u002Fgithub.com\u002FBoboegg","National Chung Cheng University Student","2026-04-03 12:18:40",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add Boboegg\u002Fai-resources --skill prompt-engineer-toolkit",{"tool":77,"command":78},"bunx","bunx skills add Boboegg\u002Fai-resources --skill prompt-engineer-toolkit",{"tool":80,"command":81},"pnpm","pnpm dlx skills add Boboegg\u002Fai-resources --skill prompt-engineer-toolkit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FSKILL.md",true,[85,91,95,99,103,109],{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"README.md","REFERENCE","参考文档",1244,"https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FREADME.md",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fevaluation-rubric.md",328,"https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Fevaluation-rubric.md",{"path":96,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fprompt-templates.md",1532,"https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Fprompt-templates.md",{"path":100,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Ftechnique-guide.md",675,"https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Ftechnique-guide.md",{"path":104,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":107,"sourceHtmlUrl":108},"scripts\u002Fprompt_tester.py","SCRIPT","脚本",7793,"https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_tester.py",{"path":110,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":111,"sourceHtmlUrl":112},"scripts\u002Fprompt_versioner.py",8111,"https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_versioner.py",[114,143,170,191,215,247],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":123,"publishedAt":55},"01M6KQBV2F4EG8YV2KET152Y5C","ra-qm-skills","12 regulatory & QM agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. ISO 13485 QMS, MDR 2017\u002F745, FDA 510(k)\u002FPMA, ISO 27001 ISMS, GDPR\u002FDSGVO, risk management (ISO 14971), CAPA, document control, auditing. Python tools (stdlib-only).","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fra-qm-team\u002FSKILL.md",[120],{"categoryNo":121,"categoryName":122,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师",[124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,40],"ra qm skills","qm","cli","gemini","python","codex","code","gdpr","risk","quality","cursor","regulatory","only","document","control","iso","iso 14971","ISO 13485","codex-cli",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":149,"publishedAt":55},"01M6KQB7AQ2V5E25R7MNWW8QEW","scrum-master","Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fproject-management\u002Fscrum-master\u002FSKILL.md",[],[150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169],"Scrum Master","scrum","master","jira","agile","data","analysis","Charts","team","project-management","reports","scoring","velocity","project","data-analysis","scripts","sprint","advanced","action","data-driven",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":178,"publishedAt":55},"01M6KQA3FMM7WRABSEDFVTFV1G","pm-skills","6 project management agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Senior PM, scrum master, Jira expert (JQL), Confluence expert, Atlassian admin, template creator. MCP integration for live Jira\u002FConfluence automation.","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fproject-management\u002FSKILL.md",[176,177],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":121,"categoryName":122,"taxonomyType":29,"taxonomyTypeDesc":30},[179,180,181,182,126,183,184,153,130,154,185,186,159,187,151,152,188,163,189,190],"pm skills","pm","MCP","openclaw","gemini cli","template creator","project management","template","atlassian","expert","live","jql",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":201,"publishedAt":55},"01M6KQ95X5FVDMW0CS2CDC9Y8S","research-summarizer","Structured research summarization agent skill for non-dev users. Handles academic papers, web articles, reports, and documentation. Extracts key findings, generates comparative analyses, and produces properly formatted citations. Use when: user wants to summarize a research paper, compare multiple sources, extract citations from documents, or create structured research briefs. Plugin for Claude Code, Codex, Gemini CLI, and OpenClaw.","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fproduct-team\u002Fresearch-summarizer\u002FSKILL.md",[197,200],{"categoryNo":198,"categoryName":199,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[202,203,204,182,126,127,183,205,206,207,130,208,209,210,160,211,212,213,214,40],"research","research summarizer","summarizer","documentation","documents","create","dev","product","Citations","agent skill","sources","papers","briefs",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":231,"publishedAt":55},"01M6KQ7P10V92ZA3WAP0KMG5ZW","code-to-prd","Reverse-engineer any codebase into a complete Product Requirements Document (PRD).\nAnalyzes routes, components, state management, API integrations, and user interactions to produce\nbusiness-readable documentation detailed enough for engineers or AI agents to fully reconstruct\nevery page and endpoint. Works with frontend frameworks (React, Vue, Angular, Svelte, Next.js, Nuxt),\nbackend frameworks (NestJS, Django, Express, FastAPI), and fullstack applications.\n\nTrigger when users mention: generate PRD, reverse-engineer requirements, code to documentation,\nextract product specs from code, document page logic, analyze page fields and interactions,\ncreate a functional inventory, write requirements from an existing codebase, document API endpoints,\nor analyze backend routes.","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fproduct-team\u002Fcode-to-prd\u002FSKILL.md",[221,224,227,228],{"categoryNo":222,"categoryName":223,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":225,"categoryName":226,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":121,"categoryName":122,"taxonomyType":29,"taxonomyTypeDesc":30},{"categoryNo":229,"categoryName":230,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000031","产品经理",[232,130,233,234,235,236,237,207,238,239,240,241,242,39,137,243,244,245,40,246],"to prd","prd","code to prd","frontend","components","react","generate","vue","applications","analyze","write","frameworks","express","functional","Existing",{"skillNo":248,"name":249,"nameZh":6,"description":250,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":251,"categories":252,"keywords":258,"publishedAt":55},"01M6KQ7A5WBZMQAM7Z37F77B4X","product-skills","10 product agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. PM toolkit (RICE), agile PO, product strategist (OKR), UX researcher, UI design system, competitive teardown, landing page generator, SaaS scaffolder, research summarizer. Python tools (stdlib-only).","https:\u002F\u002Fgithub.com\u002FBoboegg\u002Fai-resources\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fproduct-team\u002FSKILL.md",[253,254,255],{"categoryNo":121,"categoryName":122,"taxonomyType":29,"taxonomyTypeDesc":30},{"categoryNo":229,"categoryName":230,"taxonomyType":29,"taxonomyTypeDesc":30},{"categoryNo":256,"categoryName":257,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000032","设计师",[209,259,260,182,126,183,261,130,154,262,263,264,265,203,266,136,180,267,204,268],"product skills","design","design system","ux","ui","product strategist","Tools","rice","teardown","competitive"]