[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C1YGME2KC04MD4GTTHCWRT":3,"skill-same:01M6C1YGME2KC04MD4GTTHCWRT":108},{"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":60,"commitSha":61,"skillDirectory":62,"forksCount":13,"repositoryHtmlUrl":63,"repositoryHomepageUrl":6,"ownerHtmlUrl":64,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":65,"install":66,"resources":79},"01M6C1YGME2KC04MD4GTTHCWRT","prompt-engineer-toolkit",null,"Turns marketing prompts into tested, versioned production assets: A\u002FB prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'","mne20114-hash\u002Fgest35bi","mne20114-hash","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F224168109?v=4","01M6C0JCR7334CZ39YWTZXX2NG","01M6C0JCWN0NBWMQTW1CPG2Z4G",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\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],"prompt engineer toolkit","prompt","toolkit","prompt engineer","playbook","discipline","generated","history","versioning","marketing","improve","team","review","Meta","Evaluation","email","ai governance","workflow","safe","diffs","2026-11-10 10:12:30",false,"---\nname: \"prompt-engineer-toolkit\"\ndescription: \"Turns marketing prompts into tested, versioned production assets: A\u002FB prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'\"\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) — 6 production marketing templates (ad copy, email sequence, social repurposing, landing sections, SEO meta, brand-voice rewrite) plus generic building blocks; each written to be graded by `prompt_tester.py`\n- [references\u002Ftechnique-guide.md](references\u002Ftechnique-guide.md) — technique-selection table for marketing tasks + the LLM-governance stack for marketing teams (claim discipline, disclosure rules, data boundaries, human-review gates)\n- [references\u002Fevaluation-rubric.md](references\u002Fevaluation-rubric.md) — mechanical scoring weights, acceptance gates, marketing quality dimensions, test-suite design, and eval anti-patterns\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) — 6 production marketing templates (ad copy, email sequence, social repurposing, landing sections, SEO meta, brand-voice rewrite) plus generic building blocks; each written to be graded by `prompt_tester.py`\n- [references\u002Ftechnique-guide.md](references\u002Ftechnique-guide.md) — technique-selection table for marketing tasks + the LLM-governance stack for marketing teams (claim discipline, disclosure rules, data boundaries, human-review gates)\n- [references\u002Fevaluation-rubric.md](references\u002Fevaluation-rubric.md) — mechanical scoring weights, acceptance gates, marketing quality dimensions, test-suite design, and eval anti-patterns\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":14,"metadata":56,"description":7},{"author":57,"updated":58,"version":59,"category":40},"Alireza Rezvani",1772755200000,"1.0.0","main","510dd2917e051cc953dc55f56fd6ef442aa01b38","claude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi","https:\u002F\u002Fgithub.com\u002Fmne20114-hash","2026-10-28 07:50:31",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add mne20114-hash\u002Fgest35bi --skill prompt-engineer-toolkit",{"tool":72,"command":73},"bunx","bunx skills add mne20114-hash\u002Fgest35bi --skill prompt-engineer-toolkit",{"tool":75,"command":76},"pnpm","pnpm dlx skills add mne20114-hash\u002Fgest35bi --skill prompt-engineer-toolkit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit\u002FSKILL.md",true,[80,86,90,94,98,104],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"README.md","REFERENCE","参考文档",1244,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit\u002FREADME.md",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fevaluation-rubric.md",4752,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit\u002Freferences\u002Fevaluation-rubric.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fprompt-templates.md",6097,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit\u002Freferences\u002Fprompt-templates.md",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Ftechnique-guide.md",5983,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit\u002Freferences\u002Ftechnique-guide.md",{"path":99,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":102,"sourceHtmlUrl":103},"scripts\u002Fprompt_tester.py","SCRIPT","脚本",7793,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_tester.py",{"path":105,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":106,"sourceHtmlUrl":107},"scripts\u002Fprompt_versioner.py",8111,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_versioner.py",[109,142,164,187,212,231],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":122,"publishedAt":51},"01M6C27N3H0F9RGG2KASPASH04","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[115,118,119],{"categoryNo":116,"categoryName":117,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":120,"categoryName":121,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000034","行政与办公人员",[111,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141],"docx","pr","create","intake","search","audit","integrity","checkpoint","learning","js","format","direct","Rules","find","consensus","list","extract","use","outcomes",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":152,"publishedAt":51},"01M6C27DMGZ4D6ERNHSVA8H8RJ","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[148,151],{"categoryNo":149,"categoryName":150,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[144,153,154,124,155,126,128,156,157,129,158,159,160,161,134,135,162,136,163,140],"research","web","sentiment analysis","sentiment","Citations","analysis","discovery","open","tool","twitter","user",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":175,"publishedAt":51},"01M6C279M59X635VPD6Y9R616J","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[170,171,172],{"categoryNo":116,"categoryName":117,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":173,"categoryName":174,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000037","律师与法务",[166,123,124,176,177,178,126,127,128,158,179,180,181,135,182,183,184,185,140,186],"competitive landscape","routing","product","commits","Word","text","acquisition","decisions","strategy","coverage","advice",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":197,"publishedAt":51},"01M6C275RZWNRB3CCB14QQM7ES","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[193,196],{"categoryNo":194,"categoryName":195,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[189,198,199,153,200,201,202,203,204,205,206,207,160,181,134,208,209,210,211,140],"google","browser automation","automation","generate","browser","fast","SET","doc","Video","Audio","check","ask","environment","overview",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":222,"publishedAt":51},"01M6C2717B0QDJ3Y4PJP77BMW5","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[218,219,220,221],{"categoryNo":116,"categoryName":117,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":149,"categoryName":150,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":120,"categoryName":121,"taxonomyType":28,"taxonomyTypeDesc":29},[214,123,223,224,43,124,126,127,225,226,227,130,228,180,229,135,137,230,185,140],"MCP","writing","lane","keyless","literature review","plan","researcher","question",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":235,"categories":236,"keywords":241,"publishedAt":51},"01M6C26X5G3WDVWQ40Y16ZS19F","grants","NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance\u002FInnovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget\u002Fscope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fgrants\u002Fskills\u002Fgrants\u002FSKILL.md",[237,238,239,240],{"categoryNo":116,"categoryName":117,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":149,"categoryName":150,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":120,"categoryName":121,"taxonomyType":28,"taxonomyTypeDesc":29},[233,123,153,124,242,126,127,128,243,244,129,158,180,135,136,137,184,210,140,245],"help","data","Budget","innovation"]