[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6Q76HGCW1TWSVRQ0XMVVF0G":3,"skill-same:01M6Q76HGCW1TWSVRQ0XMVVF0G":124},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":30,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":76,"commitSha":77,"skillDirectory":78,"forksCount":79,"repositoryHtmlUrl":80,"repositoryHomepageUrl":6,"ownerHtmlUrl":81,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":82,"install":83,"resources":95},"01M6Q76HGCW1TWSVRQ0XMVVF0G","prompt-engineer-toolkit",null,"Manage — 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","thiagofernandes1987-create\u002FAPEX","thiagofernandes1987-create","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235012278?v=4","01M6P9ZPHGVWK0SDZAHH74GN45","01M6P9ZPP1XQV60N6JES70PT75",2,"MIT","https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FSKILL.md",[17,22,25],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"toolkit","prompt engineer","prompt","prompt engineer toolkit","engineer","result","documentation","hybrid","for","true","STRING","false","end","campaigns","social","manage","marketing","Media","email","code","2026-11-10 10:12:30",false,"---\nname: \"prompt-engineer-toolkit\"\ndescription: \"Manage — 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\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: marketing\n  updated: 2026-03-06\nexecutor: HYBRID\nskill_id: business.marketing-skill.prompt-engineer-toolkit\nstatus: ADOPTED\nsecurity: {level: standard, pii: true, approval_required: false}\nanchors:\n  - business\n  - marketing\n  - documentation\ntier: 2\ninput_schema:\n  - name: code_or_task\n    type: string\n    description: \"Code snippet, script, or task description to process\"\n    required: true\noutput_schema:\n  - name: result\n    type: string\n    description: \"Primary output from prompt engineer toolkit\"\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\n---\n\n## Why This Skill Exists\n\nManage — 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\n\n\u003C!-- SR_40: auto-generated from frontmatter `purpose`\u002F`description` (OPP-Phase3). Expand with domain-specific rationale. -->\n\n## When to Use\n\nUse this skill when the task requires prompt engineer toolkit capabilities.\n\n\u003C!-- SR_40: auto-generated from frontmatter `when`\u002F`description` (OPP-Phase3). -->\n\n## What If Fails\n\nIf this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.\n\n\u003C!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->\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\n---\n\n## Why This Skill Exists\n\nManage — 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\n\n\u003C!-- SR_40: auto-generated from frontmatter `purpose`\u002F`description` (OPP-Phase3). Expand with domain-specific rationale. -->\n\n## When to Use\n\nUse this skill when the task requires prompt engineer toolkit capabilities.\n\n\u003C!-- SR_40: auto-generated from frontmatter `when`\u002F`description` (OPP-Phase3). -->\n\n## What If Fails\n\nIf this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.\n\n\u003C!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->\n",{"name":5,"tier":13,"status":56,"anchors":57,"license":14,"executor":59,"metadata":60,"security":64,"skill_id":67,"description":7,"input_schema":68,"output_schema":73},"ADOPTED",[58,47,37],"business","HYBRID",{"author":61,"updated":62,"version":63,"category":47},"Alireza Rezvani",1772755200000,"1.0.0",{"pii":65,"level":66,"approval_required":52},true,"standard","business.marketing-skill.prompt-engineer-toolkit",[69],{"name":70,"type":71,"required":65,"description":72},"code_or_task","string","Code snippet, script, or task description to process",[74],{"name":36,"type":71,"description":75},"Primary output from prompt engineer toolkit","main","19b1e9f092771d7e208f036df866ef2350da1fc8","skills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit",0,"https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX","https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create","2026-09-19 19:19:54",{"source":8,"skillName":5,"commands":84,"prompt":94,"zipAvailable":65},[85,88,91],{"tool":86,"command":87},"npx","npx skills add thiagofernandes1987-create\u002FAPEX --skill prompt-engineer-toolkit",{"tool":89,"command":90},"bunx","bunx skills add thiagofernandes1987-create\u002FAPEX --skill prompt-engineer-toolkit",{"tool":92,"command":93},"pnpm","pnpm dlx skills add thiagofernandes1987-create\u002FAPEX --skill prompt-engineer-toolkit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FSKILL.md",[96,102,106,110,114,120],{"path":97,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":100,"sourceHtmlUrl":101},"README.md","REFERENCE","参考文档",1244,"https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FREADME.md",{"path":103,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fevaluation-rubric.md",328,"https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Fevaluation-rubric.md",{"path":107,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fprompt-templates.md",1532,"https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Fprompt-templates.md",{"path":111,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":112,"sourceHtmlUrl":113},"references\u002Ftechnique-guide.md",675,"https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Ftechnique-guide.md",{"path":115,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":118,"sourceHtmlUrl":119},"scripts\u002Fprompt_tester.py","SCRIPT","脚本",8224,"https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_tester.py",{"path":121,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":122,"sourceHtmlUrl":123},"scripts\u002Fprompt_versioner.py",8545,"https:\u002F\u002Fgithub.com\u002Fthiagofernandes1987-create\u002FAPEX\u002Fblob\u002Fmain\u002Fskills\u002Fbusiness\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_versioner.py",[125,156,180,218,241,269],{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":135,"publishedAt":51},"01M6QGYATP9FFBSH598CZGB9GX","vercel-react-view-transitions","Guide for implementing smooth, native-feeling animations using React's View Transition API (`\u003CViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). 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