[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JTJ2HGA072NBA3VJWRZC8C":3,"skill-same:01M6JTJ2HGA072NBA3VJWRZC8C":92},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":32,"publishedAt":53,"linkOnly":54,"markdownContent":55,"markdownBody":56,"frontMatter":57,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":69,"ownerHtmlUrl":70,"ownerBlogUrl":71,"ownerDescription":72,"updatedAt":73,"install":74,"resources":86},"01M6JTJ2HGA072NBA3VJWRZC8C","seo-content",null,"Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says \"content quality\", \"E-E-A-T\", \"content analysis\", \"readability check\", \"thin content\", or \"content audit\".","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FBestLemoon\u002Fcodex-seo\u002Fskills\u002Fseo-content\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FBestLemoon\u002Fcodex-seo\u002Fskills\u002Fseo-content\u002FSKILL.md",[19,24,29],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":27,"taxonomyTypeDesc":28},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",{"categoryNo":30,"categoryName":31,"taxonomyType":27,"taxonomyTypeDesc":28},"01JBSEEDCAT000000000000036","内容创作者",[33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"content","seo","seo content","when","content audit","content analysis","true","e-e-a-t","URL","with","readiness","readability","Citation","use","user","check","quality","analysis","audit","assessment","2026-11-10 10:12:30",false,"---\nname: seo-content\ndescription: >\n  Content quality and E-E-A-T analysis with AI citation readiness assessment.\n  Use when user says \"content quality\", \"E-E-A-T\", \"content analysis\",\n  \"readability check\", \"thin content\", or \"content audit\".\nuser-invokable: true\nargument-hint: \"[url]\"\nlicense: MIT\nmetadata:\n  author: AgriciDaniel\n  version: \"1.7.0\"\n  category: seo\n---\n\n# Content Quality & E-E-A-T Analysis\n\n## E-E-A-T Framework (updated Sept 2025 QRG)\n\nRead `skills\u002Fseo\u002Freferences\u002Feeat-framework.md` for full criteria.\n\n### Experience (first-hand signals)\n- Original research, case studies, before\u002Fafter results\n- Personal anecdotes, process documentation\n- Unique data, proprietary insights\n- Photos\u002Fvideos from direct experience\n\n### Expertise\n- Author credentials, certifications, bio\n- Professional background relevant to topic\n- Technical depth appropriate for audience\n- Accurate, well-sourced claims\n\n### Authoritativeness\n- External citations, backlinks from authoritative sources\n- Brand mentions, industry recognition\n- Published in recognized outlets\n- Cited by other experts\n\n### Trustworthiness\n- Contact information, physical address\n- Privacy policy, terms of service\n- Customer testimonials, reviews\n- Date stamps, transparent corrections\n- Secure site (HTTPS)\n\n## Content Metrics\n\n### Word Count Analysis\nCompare against page type minimums:\n| Page Type | Minimum |\n|-----------|---------|\n| Homepage | 500 |\n| Service page | 800 |\n| Blog post | 1,500 |\n| Product page | 300+ (400+ for complex products) |\n| Location page | 500-600 |\n\n> **Important:** These are **topical coverage floors**, not targets. Google has confirmed word count is NOT a direct ranking factor. The goal is comprehensive topical coverage; a 500-word page that thoroughly answers the query will outrank a 2,000-word page that doesn't. Use these as guidelines for adequate coverage depth, not rigid requirements.\n\n### Readability\n- Flesch Reading Ease: target 60-70 for general audience\n\n> **Note:** Flesch Reading Ease is a useful proxy for content accessibility but is NOT a direct Google ranking factor. John Mueller has confirmed Google does not use basic readability scores for ranking. Yoast deprioritized Flesch scores in v19.3. Use readability analysis as a content quality indicator, not as an SEO metric to optimize directly.\n- Grade level: match target audience\n- Sentence length: average 15-20 words\n- Paragraph length: 2-4 sentences\n\n### Keyword Optimization\n- Primary keyword in title, H1, first 100 words\n- Natural density (1-3%)\n- Semantic variations present\n- No keyword stuffing\n\n### Content Structure\n- Logical heading hierarchy (H1 -> H2 -> H3)\n- Scannable sections with descriptive headings\n- Bullet\u002Fnumbered lists where appropriate\n- Table of contents for long-form content\n\n### Multimedia\n- Relevant images with proper alt text\n- Videos where appropriate\n- Infographics for complex data\n- Charts\u002Fgraphs for statistics\n\n### Internal Linking\n- 3-5 relevant internal links per 1000 words\n- Descriptive anchor text\n- Links to related content\n- No orphan pages\n\n### External Linking\n- Cite authoritative sources\n- Open in new tab for user experience\n- Reasonable count (not excessive)\n\n## AI Content Assessment (Sept 2025 QRG addition)\n\nGoogle's raters now formally assess whether content appears AI-generated.\n\n### Acceptable AI Content\n- Demonstrates genuine E-E-A-T\n- Provides unique value\n- Has human oversight and editing\n- Contains original insights\n\n### Low-Quality AI Content Markers\n- Generic phrasing, lack of specificity\n- No original insight\n- Repetitive structure across pages\n- No author attribution\n- Factual inaccuracies\n\n> **Helpful Content System (March 2024):** The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now weighted within every core update. The same principles apply (people-first content, demonstrating E-E-A-T, satisfying user intent), but enforcement is continuous rather than through separate HCU updates.\n\n## AI Citation Readiness (GEO signals)\n\nOptimize for AI search engines (ChatGPT, Perplexity, Google AI Overviews):\n\n- Clear, quotable statements with statistics\u002Ffacts\n- Structured data (especially for data points)\n- Strong heading hierarchy (H1->H2->H3 flow)\n- Answer-first formatting for key questions\n- Tables and lists for comparative data\n- Clear attribution and source citations\n\n### AI Search Visibility & GEO (2025-2026)\n\n**Google AI Mode** launched publicly in May 2025 as a separate tab in Google Search, available in 180+ countries. Unlike AI Overviews (which appear above organic results), AI Mode provides a fully conversational search experience with **zero organic blue links**, making AI citation the only visibility mechanism.\n\n**Key optimization strategies for AI citation:**\n- **Structured answers:** Clear question-answer formats, definition patterns, and step-by-step instructions that AI systems can extract and cite\n- **First-party data:** Original research, statistics, case studies, and unique datasets are highly cited by AI systems\n- **Schema markup:** Article, FAQ (for non-Google AI platforms), and structured content schemas help AI systems parse and attribute content\n- **Topical authority:** AI systems preferentially cite sources that demonstrate deep expertise. Build content clusters, not isolated pages\n- **Entity clarity:** Ensure brand, authors, and key concepts are clearly defined with structured data (Organization, Person schema)\n- **Multi-platform tracking:** Monitor visibility across Google AI Overviews, AI Mode, ChatGPT, Perplexity, and Bing Copilot, not just traditional rankings. Treat AI citation as a standalone KPI alongside organic rankings and traffic.\n\n**Generative Engine Optimization (GEO):**\nGEO is the emerging discipline of optimizing content specifically for AI-generated answers. Key GEO signals include: quotability (clear, concise extractable facts), attribution (source citations within your content), structure (well-organized heading hierarchy), and freshness (regularly updated data). Cross-reference the `seo-geo` skill for detailed GEO workflows.\n\n## Content Freshness\n\n- Publication date visible\n- Last updated date if content has been revised\n- Flag content older than 12 months without update for fast-changing topics\n\n## Output\n\n### Content Quality Score: XX\u002F100\n\n### E-E-A-T Breakdown\n| Factor | Score | Key Signals |\n|--------|-------|-------------|\n| Experience | XX\u002F25 | ... |\n| Expertise | XX\u002F25 | ... |\n| Authoritativeness | XX\u002F25 | ... |\n| Trustworthiness | XX\u002F25 | ... |\n\n### AI Citation Readiness: XX\u002F100\n\n### Issues Found\n### Recommendations\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `kw_data_google_ads_search_volume` for real keyword volume data, `dataforseo_labs_bulk_keyword_difficulty` for difficulty scores, `dataforseo_labs_search_intent` for intent classification, and `content_analysis_summary` for content quality analysis.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again. |\n| Content behind paywall (402\u002F403, login wall) | Report that the content is not publicly accessible. Analyze only the visible portion (meta tags, headers) and note the limitation. |\n| Thin content (fewer than 100 words retrievable) | Report the findings as-is rather than guessing. Flag the page as potentially JavaScript-rendered or gated, and suggest the user provide the full text directly. |\n","# Content Quality & E-E-A-T Analysis\n\n## E-E-A-T Framework (updated Sept 2025 QRG)\n\nRead `skills\u002Fseo\u002Freferences\u002Feeat-framework.md` for full criteria.\n\n### Experience (first-hand signals)\n- Original research, case studies, before\u002Fafter results\n- Personal anecdotes, process documentation\n- Unique data, proprietary insights\n- Photos\u002Fvideos from direct experience\n\n### Expertise\n- Author credentials, certifications, bio\n- Professional background relevant to topic\n- Technical depth appropriate for audience\n- Accurate, well-sourced claims\n\n### Authoritativeness\n- External citations, backlinks from authoritative sources\n- Brand mentions, industry recognition\n- Published in recognized outlets\n- Cited by other experts\n\n### Trustworthiness\n- Contact information, physical address\n- Privacy policy, terms of service\n- Customer testimonials, reviews\n- Date stamps, transparent corrections\n- Secure site (HTTPS)\n\n## Content Metrics\n\n### Word Count Analysis\nCompare against page type minimums:\n| Page Type | Minimum |\n|-----------|---------|\n| Homepage | 500 |\n| Service page | 800 |\n| Blog post | 1,500 |\n| Product page | 300+ (400+ for complex products) |\n| Location page | 500-600 |\n\n> **Important:** These are **topical coverage floors**, not targets. Google has confirmed word count is NOT a direct ranking factor. The goal is comprehensive topical coverage; a 500-word page that thoroughly answers the query will outrank a 2,000-word page that doesn't. Use these as guidelines for adequate coverage depth, not rigid requirements.\n\n### Readability\n- Flesch Reading Ease: target 60-70 for general audience\n\n> **Note:** Flesch Reading Ease is a useful proxy for content accessibility but is NOT a direct Google ranking factor. John Mueller has confirmed Google does not use basic readability scores for ranking. Yoast deprioritized Flesch scores in v19.3. Use readability analysis as a content quality indicator, not as an SEO metric to optimize directly.\n- Grade level: match target audience\n- Sentence length: average 15-20 words\n- Paragraph length: 2-4 sentences\n\n### Keyword Optimization\n- Primary keyword in title, H1, first 100 words\n- Natural density (1-3%)\n- Semantic variations present\n- No keyword stuffing\n\n### Content Structure\n- Logical heading hierarchy (H1 -> H2 -> H3)\n- Scannable sections with descriptive headings\n- Bullet\u002Fnumbered lists where appropriate\n- Table of contents for long-form content\n\n### Multimedia\n- Relevant images with proper alt text\n- Videos where appropriate\n- Infographics for complex data\n- Charts\u002Fgraphs for statistics\n\n### Internal Linking\n- 3-5 relevant internal links per 1000 words\n- Descriptive anchor text\n- Links to related content\n- No orphan pages\n\n### External Linking\n- Cite authoritative sources\n- Open in new tab for user experience\n- Reasonable count (not excessive)\n\n## AI Content Assessment (Sept 2025 QRG addition)\n\nGoogle's raters now formally assess whether content appears AI-generated.\n\n### Acceptable AI Content\n- Demonstrates genuine E-E-A-T\n- Provides unique value\n- Has human oversight and editing\n- Contains original insights\n\n### Low-Quality AI Content Markers\n- Generic phrasing, lack of specificity\n- No original insight\n- Repetitive structure across pages\n- No author attribution\n- Factual inaccuracies\n\n> **Helpful Content System (March 2024):** The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now weighted within every core update. The same principles apply (people-first content, demonstrating E-E-A-T, satisfying user intent), but enforcement is continuous rather than through separate HCU updates.\n\n## AI Citation Readiness (GEO signals)\n\nOptimize for AI search engines (ChatGPT, Perplexity, Google AI Overviews):\n\n- Clear, quotable statements with statistics\u002Ffacts\n- Structured data (especially for data points)\n- Strong heading hierarchy (H1->H2->H3 flow)\n- Answer-first formatting for key questions\n- Tables and lists for comparative data\n- Clear attribution and source citations\n\n### AI Search Visibility & GEO (2025-2026)\n\n**Google AI Mode** launched publicly in May 2025 as a separate tab in Google Search, available in 180+ countries. Unlike AI Overviews (which appear above organic results), AI Mode provides a fully conversational search experience with **zero organic blue links**, making AI citation the only visibility mechanism.\n\n**Key optimization strategies for AI citation:**\n- **Structured answers:** Clear question-answer formats, definition patterns, and step-by-step instructions that AI systems can extract and cite\n- **First-party data:** Original research, statistics, case studies, and unique datasets are highly cited by AI systems\n- **Schema markup:** Article, FAQ (for non-Google AI platforms), and structured content schemas help AI systems parse and attribute content\n- **Topical authority:** AI systems preferentially cite sources that demonstrate deep expertise. Build content clusters, not isolated pages\n- **Entity clarity:** Ensure brand, authors, and key concepts are clearly defined with structured data (Organization, Person schema)\n- **Multi-platform tracking:** Monitor visibility across Google AI Overviews, AI Mode, ChatGPT, Perplexity, and Bing Copilot, not just traditional rankings. Treat AI citation as a standalone KPI alongside organic rankings and traffic.\n\n**Generative Engine Optimization (GEO):**\nGEO is the emerging discipline of optimizing content specifically for AI-generated answers. Key GEO signals include: quotability (clear, concise extractable facts), attribution (source citations within your content), structure (well-organized heading hierarchy), and freshness (regularly updated data). Cross-reference the `seo-geo` skill for detailed GEO workflows.\n\n## Content Freshness\n\n- Publication date visible\n- Last updated date if content has been revised\n- Flag content older than 12 months without update for fast-changing topics\n\n## Output\n\n### Content Quality Score: XX\u002F100\n\n### E-E-A-T Breakdown\n| Factor | Score | Key Signals |\n|--------|-------|-------------|\n| Experience | XX\u002F25 | ... |\n| Expertise | XX\u002F25 | ... |\n| Authoritativeness | XX\u002F25 | ... |\n| Trustworthiness | XX\u002F25 | ... |\n\n### AI Citation Readiness: XX\u002F100\n\n### Issues Found\n### Recommendations\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `kw_data_google_ads_search_volume` for real keyword volume data, `dataforseo_labs_bulk_keyword_difficulty` for difficulty scores, `dataforseo_labs_search_intent` for intent classification, and `content_analysis_summary` for content quality analysis.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again. |\n| Content behind paywall (402\u002F403, login wall) | Report that the content is not publicly accessible. Analyze only the visible portion (meta tags, headers) and note the limitation. |\n| Thin content (fewer than 100 words retrievable) | Report the findings as-is rather than guessing. Flag the page as potentially JavaScript-rendered or gated, and suggest the user provide the full text directly. |\n",{"name":5,"license":15,"metadata":58,"description":61,"argument-hint":62,"user-invokable":63},{"author":59,"version":60,"category":34},"AgriciDaniel","1.7.0","Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says \"content quality\", \"E-E-A-T\", \"content analysis\", \"readability check\", \"thin content\", or \"content audit\".\n","[url]",true,"main","b0f0a87593c578a7898eccaf1b363f7f87c1f99b","plugins\u002FBestLemoon\u002Fcodex-seo\u002Fskills\u002Fseo-content",490,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-14 02:12:02",{"source":8,"skillName":5,"commands":75,"prompt":85,"zipAvailable":63},[76,79,82],{"tool":77,"command":78},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill seo-content",{"tool":80,"command":81},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill seo-content",{"tool":83,"command":84},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill seo-content","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FBestLemoon\u002Fcodex-seo\u002Fskills\u002Fseo-content\u002FSKILL.md",[87],{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",142,[93,102,109,116,142,166],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":101},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":101},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":101},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":122,"publishedAt":53},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[123,124,125,126,127,128,129,130,131,132,133,36,134,135,136,137,138,139,140,141],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":53},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[149,150,151,137,42,152,153,154,155,156,157,158,159,160,128,161,162,163,164,165],"deja reflect","reflect","review","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":181,"publishedAt":53},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[172,175,178],{"categoryNo":173,"categoryName":174,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":176,"categoryName":177,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":179,"categoryName":180,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000010","后端与接口",[168,41,182,183,184,185,186,162,187,129,188,189,190,191,36,192,193,194,195,196],"Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]