[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6G1WPCSWYXAFCDAP5CNAY47":3,"skill-same:01M6G1WPCSWYXAFCDAP5CNAY47":115},{"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":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":6,"ownerHtmlUrl":69,"ownerBlogUrl":70,"ownerDescription":71,"updatedAt":72,"install":73,"resources":86},"01M6G1WPCSWYXAFCDAP5CNAY47","prompt-engineer-toolkit",null,"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.'","oabdelmaksoud\u002FAgention","oabdelmaksoud","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F50699149?v=4","01M6D5CVVN778ZCDP1WHS816SP","Omar Abdelmaksoud","01M6D8A4TE76V3DCSH3D94VG1V",2,"MIT","https:\u002F\u002Fgithub.com\u002Foabdelmaksoud\u002FAgention\u002Fblob\u002Fmain\u002Fupstream-sync\u002Fwork\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","engineer","prompt engineer toolkit","prompt","quality","ai content","ai content workflow","optimize","build","prompts","use","writing","marketing","improve","templates","prompt templates","workflows","workflow","prompt engineering","2026-11-10 10:12:30",false,"---\nname: prompt-engineer-toolkit\ndescription: \"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**Tier:** POWERFUL  \n**Category:** Marketing Skill \u002F AI Operations  \n**Domain:** Prompt Engineering, LLM Optimization, AI Workflows\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.\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## When to Use\n\n- You are launching a new LLM feature and need reliable outputs\n- Prompt quality degrades after model or instruction changes\n- Multiple team members edit prompts and need history\u002Fdiffs\n- You need evidence-based prompt choice for production rollout\n- You want consistent prompt governance across environments\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## Common Pitfalls\n\n1. Picking prompts by anecdotal single-case outputs\n2. Changing prompt + model simultaneously without control group\n3. Missing forbidden-content checks in evaluation criteria\n4. Editing prompts without version metadata or rationale\n5. Failing to diff semantic changes before deploy\n\n## Best Practices\n\n1. Keep test cases realistic and edge-case rich.\n2. Always include negative checks (`must_not_contain`).\n3. Store prompt versions with author and change reason.\n4. Run A\u002FB tests before and after major model upgrades.\n5. Separate reusable templates from production prompt instances.\n6. Maintain a small golden regression suite for every critical prompt.\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## Prompt Review Checklist\n\n1. Task intent is explicit and unambiguous.\n2. Output schema\u002Fformat is explicit.\n3. Safety and exclusion constraints are explicit.\n4. Prompt avoids contradictory instructions.\n5. Prompt avoids unnecessary verbosity tokens.\n\n## Common Operational Risks\n\n- Evaluating with too few test cases (false confidence)\n- Optimizing for one benchmark while harming edge cases\n- Missing audit trail for prompt edits in multi-author teams\n- Model swap without rerunning baseline A\u002FB suite\n\n## Proactive Triggers\n\n- **AI output sounds generic** → Prompts lack brand voice context. Include voice guidelines.\n- **Inconsistent output quality** → Prompts too vague. Add specific examples and constraints.\n- **No quality checks on AI content** → AI output needs human review. Never publish without editing.\n- **Same prompt style for all tasks** → Different tasks need different prompt structures.\n\n## Output Artifacts\n\n| When you ask for... | You get... |\n|---------------------|------------|\n| \"Improve my prompts\" | Prompt audit with specific rewrites for better output |\n| \"Prompt templates\" | Task-specific prompt templates for marketing use cases |\n| \"AI content workflow\" | End-to-end AI-assisted content production workflow |\n\n## Communication\n\nAll output passes quality verification:\n- Self-verify: source attribution, assumption audit, confidence scoring\n- Output format: Bottom Line → What (with confidence) → Why → How to Act\n- Results only. Every finding tagged: ? verified, ? medium, ? assumed.\n\n## Related Skills\n\n- **content-production**: For the full content pipeline. Prompt engineering supports AI-assisted writing.\n- **ad-creative**: For generating ad variations using prompt techniques.\n- **content-humanizer**: For refining AI-generated output to sound natural.\n- **marketing-context**: Provides brand context that improves prompt outputs.\n","# Prompt Engineer Toolkit\n\n**Tier:** POWERFUL  \n**Category:** Marketing Skill \u002F AI Operations  \n**Domain:** Prompt Engineering, LLM Optimization, AI Workflows\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.\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## When to Use\n\n- You are launching a new LLM feature and need reliable outputs\n- Prompt quality degrades after model or instruction changes\n- Multiple team members edit prompts and need history\u002Fdiffs\n- You need evidence-based prompt choice for production rollout\n- You want consistent prompt governance across environments\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## Common Pitfalls\n\n1. Picking prompts by anecdotal single-case outputs\n2. Changing prompt + model simultaneously without control group\n3. Missing forbidden-content checks in evaluation criteria\n4. Editing prompts without version metadata or rationale\n5. Failing to diff semantic changes before deploy\n\n## Best Practices\n\n1. Keep test cases realistic and edge-case rich.\n2. Always include negative checks (`must_not_contain`).\n3. Store prompt versions with author and change reason.\n4. Run A\u002FB tests before and after major model upgrades.\n5. Separate reusable templates from production prompt instances.\n6. Maintain a small golden regression suite for every critical prompt.\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## Prompt Review Checklist\n\n1. Task intent is explicit and unambiguous.\n2. Output schema\u002Fformat is explicit.\n3. Safety and exclusion constraints are explicit.\n4. Prompt avoids contradictory instructions.\n5. Prompt avoids unnecessary verbosity tokens.\n\n## Common Operational Risks\n\n- Evaluating with too few test cases (false confidence)\n- Optimizing for one benchmark while harming edge cases\n- Missing audit trail for prompt edits in multi-author teams\n- Model swap without rerunning baseline A\u002FB suite\n\n## Proactive Triggers\n\n- **AI output sounds generic** → Prompts lack brand voice context. Include voice guidelines.\n- **Inconsistent output quality** → Prompts too vague. Add specific examples and constraints.\n- **No quality checks on AI content** → AI output needs human review. Never publish without editing.\n- **Same prompt style for all tasks** → Different tasks need different prompt structures.\n\n## Output Artifacts\n\n| When you ask for... | You get... |\n|---------------------|------------|\n| \"Improve my prompts\" | Prompt audit with specific rewrites for better output |\n| \"Prompt templates\" | Task-specific prompt templates for marketing use cases |\n| \"AI content workflow\" | End-to-end AI-assisted content production workflow |\n\n## Communication\n\nAll output passes quality verification:\n- Self-verify: source attribution, assumption audit, confidence scoring\n- Output format: Bottom Line → What (with confidence) → Why → How to Act\n- Results only. Every finding tagged: ? verified, ? medium, ? assumed.\n\n## Related Skills\n\n- **content-production**: For the full content pipeline. Prompt engineering supports AI-assisted writing.\n- **ad-creative**: For generating ad variations using prompt techniques.\n- **content-humanizer**: For refining AI-generated output to sound natural.\n- **marketing-context**: Provides brand context that improves prompt outputs.\n",{"name":5,"license":15,"metadata":60,"description":7},{"author":61,"updated":62,"version":63,"category":48},"Alireza Rezvani",1772755200000,"1.0.0","main","a318f48de3c5718e8ea2e9f6fae7790e8a759e85","upstream-sync\u002Fwork\u002Fclaude-skills\u002Fmarketing-skill\u002Fprompt-engineer-toolkit",0,"https:\u002F\u002Fgithub.com\u002Foabdelmaksoud\u002FAgention","https:\u002F\u002Fgithub.com\u002Foabdelmaksoud","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fo-abdelmaksoud\u002F","Full-Stack Engineer | AI Systems Architect | Building autonomous multi-agent systems","2026-03-08 21:23:03",{"source":8,"skillName":5,"commands":74,"prompt":84,"zipAvailable":85},[75,78,81],{"tool":76,"command":77},"npx","npx skills add 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