[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADN4FAB3PGDVE6BFYGTMY2":3,"skill-same:01M6ADN4FAB3PGDVE6BFYGTMY2":87},{"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":26,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":58,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6ADN4FAB3PGDVE6BFYGTMY2","deep-research-notebooklm",null,"Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps).","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdeep-research-notebooklm\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"research","deep research","deep research notebooklm","notebooklm","deep","maps","source","research engine","briefs","studio","competitive","prospect research","prospect","trend","market analysis","MCP","reports","Audio","analysis","artifacts","google","2026-11-10 10:12:30",false,"---\nname: deep-research-notebooklm\ndescription: \"Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps).\"\n---\n\n# Deep Research via NotebookLM\n\nResearch **$ARGUMENTS** deeply using the NotebookLM MCP server and deliver a structured research brief. Optionally generate studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps) from the research.\n\n## Prerequisites\n\n- **NotebookLM MCP server** must be configured. Install via: `nlm setup add claude-code`\n- If NotebookLM MCP tools are not available, tell the user to run the setup command and restart their session.\n\n## Research Workflow\n\n### Step 1: Define Scope\n\nDetermine the **research type** based on the user's request:\n\n| Type | Focus |\n|------|-------|\n| **Market Research** | Industry trends, market sizing, opportunities, TAM\u002FSAM\u002FSOM |\n| **Competitive Intel** | Competitor analysis, positioning gaps, feature comparisons |\n| **Client\u002FProspect Research** | Company background, pain points, decision makers, recent news |\n| **Trend Analysis** | Technology trends, adoption patterns, forecasts, emerging players |\n| **Proposal Research** | Background for proposals, sector-specific data, case studies |\n| **Academic\u002FTechnical** | Papers, frameworks, methodologies, state of the art |\n\nTell the user what you plan to research and confirm the angle:\n\n> \"I'll research [topic]. My angle: [specific focus]. I'll investigate: [2-3 specific questions]. Sound right, or should I adjust?\"\n\nWait for confirmation before proceeding.\n\n### Step 2: Create NotebookLM Notebook\n\nUse `notebook_create` to create a notebook named:\n`Research: [Topic] - [YYYY-MM-DD]`\n\n### Step 3: Add Context Sources\n\nUse `source_add` to seed the notebook with relevant context:\n- Add any URLs the user provides (articles, company pages, reports)\n- Add any documents or files the user references\n- Add text summaries of relevant background if no URLs are available\n- If researching a company, add their website, LinkedIn, recent press\n\n### Step 4: Run Research\n\nUse `research_start` with a well-crafted query based on the topic and context.\n\n**Mode selection:**\n- Default: `\"fast\"` (~60 seconds, ~10 sources) -- good for most queries\n- Use `\"deep\"` only if the user explicitly asks for exhaustive research (can take 10+ minutes and may stall at 0 sources)\n\n**Tip:** Run direct `WebSearch` calls in parallel with NotebookLM for faster initial data gathering while the research engine works.\n\nPoll `research_status` until complete. Use the `query` parameter as fallback matching -- task IDs can change between `research_start` and `research_status` calls.\n\n### Step 5: Import Discovered Sources\n\nUse `research_import` to bring discovered sources into the notebook for deeper analysis.\n\n### Step 6: Query for Insights\n\nUse `notebook_query` to ask 3-5 targeted questions based on the research type:\n\n1. **Overview**: \"What are the key findings about [topic]?\"\n2. **Opportunities**: \"What opportunities or gaps exist in this space?\"\n3. **Actions**: \"What are the most actionable insights from this research?\"\n4. **Risks**: \"What are the main risks, challenges, or counterarguments?\"\n5. **Custom**: A question specific to the research type (e.g., \"Who are the top 5 competitors and how do they differentiate?\" for competitive intel)\n\n### Step 7: Write Research Brief\n\nSave the findings to a local file using the research brief template:\n\n**File path:** `research\u002F[topic-slug]-[YYYY-MM-DD].md`\n\nUse the template from [research-brief-template.md](research-brief-template.md) to structure the output. Create the `research\u002F` directory if it does not exist.\n\n### Step 8: Present Takeaways\n\nAfter saving, present the user with:\n- **3-5 headline findings** (bullets, direct, no filler)\n- **1-2 recommended actions** connected to the user's stated goals\n- **Surprises or contrarian findings** -- anything that challenges assumptions\n- **The file path** where the full brief is saved\n- **The NotebookLM notebook URL** so the user can explore sources directly\n\n### Step 9 (Optional): Generate Studio Artifacts\n\nAsk the user: \"Want me to generate any artifacts from this research? Options: slides, audio (podcast), video, infographic, report, mind map.\"\n\nIf yes, use `studio_create` with the notebook_id from Step 2.\n\n**Available artifact types and recommended settings:**\n\n| Type | Key params | Best for |\n|------|-----------|----------|\n| `slide_deck` | `slide_format`: `detailed_deck` or `presenter_slides`; `slide_length`: `short` or `default` | Executive presentations, client pitches |\n| `audio` | `audio_format`: `deep_dive`, `brief`, `critique`, or `debate`; `audio_length`: `short`, `default`, `long` | Podcast-style deep dives, learning on the go |\n| `video` | `video_format`: `explainer`, `brief`, `cinematic`; `visual_style`: `auto_select`, `classic`, `whiteboard`, etc. | Visual explainers, social media content |\n| `infographic` | `orientation`: `landscape`, `portrait`, `square`; `infographic_style`: `professional`, `bento_grid`, etc. | One-pagers, social sharing |\n| `report` | `report_format`: `Briefing Doc`, `Study Guide`, `Blog Post`, `Create Your Own` | Written deliverables, summaries |\n| `mind_map` | `title` | Visual knowledge mapping |\n\n**Common params for all artifact types:**\n- `language`: Set to the user's preferred language (e.g., `\"en\"`, `\"es\"`, `\"pt\"`)\n- `focus_prompt`: A clear directive about what to emphasize in the artifact\n- `confirm`: Must be `true` to proceed with generation\n\n**After creating an artifact:**\n1. Poll `studio_status` until `completed` (audio\u002Fvideo: 5-15 min; slides\u002Finfographics: 2-5 min)\n2. Use `download_artifact` to save locally if needed\n3. Provide the notebook URL so the user can access artifacts directly\n\n**Tips:**\n- `audio` with `deep_dive` format produces the best podcast-style analysis\n- `slide_deck` with `detailed_deck` format works best for standalone reading; `presenter_slides` is better when accompanied by speaker notes\n- Audio status may show `\"unknown\"` once completed -- check for `audio_url` presence instead of waiting for a `\"completed\"` status\n\n## Notes\n\n- Fast mode is recommended as the default. Deep mode is powerful but can take 10+ minutes and occasionally stalls.\n- Always confirm the research scope with the user before starting -- a well-scoped query produces dramatically better results.\n- The research brief template ensures consistent, actionable output across all research types.\n\n## Additional Resources\n\n- [research-brief-template.md](research-brief-template.md) -- Template for structuring research brief output\n","# Deep Research via NotebookLM\n\nResearch **$ARGUMENTS** deeply using the NotebookLM MCP server and deliver a structured research brief. Optionally generate studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps) from the research.\n\n## Prerequisites\n\n- **NotebookLM MCP server** must be configured. Install via: `nlm setup add claude-code`\n- If NotebookLM MCP tools are not available, tell the user to run the setup command and restart their session.\n\n## Research Workflow\n\n### Step 1: Define Scope\n\nDetermine the **research type** based on the user's request:\n\n| Type | Focus |\n|------|-------|\n| **Market Research** | Industry trends, market sizing, opportunities, TAM\u002FSAM\u002FSOM |\n| **Competitive Intel** | Competitor analysis, positioning gaps, feature comparisons |\n| **Client\u002FProspect Research** | Company background, pain points, decision makers, recent news |\n| **Trend Analysis** | Technology trends, adoption patterns, forecasts, emerging players |\n| **Proposal Research** | Background for proposals, sector-specific data, case studies |\n| **Academic\u002FTechnical** | Papers, frameworks, methodologies, state of the art |\n\nTell the user what you plan to research and confirm the angle:\n\n> \"I'll research [topic]. My angle: [specific focus]. I'll investigate: [2-3 specific questions]. Sound right, or should I adjust?\"\n\nWait for confirmation before proceeding.\n\n### Step 2: Create NotebookLM Notebook\n\nUse `notebook_create` to create a notebook named:\n`Research: [Topic] - [YYYY-MM-DD]`\n\n### Step 3: Add Context Sources\n\nUse `source_add` to seed the notebook with relevant context:\n- Add any URLs the user provides (articles, company pages, reports)\n- Add any documents or files the user references\n- Add text summaries of relevant background if no URLs are available\n- If researching a company, add their website, LinkedIn, recent press\n\n### Step 4: Run Research\n\nUse `research_start` with a well-crafted query based on the topic and context.\n\n**Mode selection:**\n- Default: `\"fast\"` (~60 seconds, ~10 sources) -- good for most queries\n- Use `\"deep\"` only if the user explicitly asks for exhaustive research (can take 10+ minutes and may stall at 0 sources)\n\n**Tip:** Run direct `WebSearch` calls in parallel with NotebookLM for faster initial data gathering while the research engine works.\n\nPoll `research_status` until complete. Use the `query` parameter as fallback matching -- task IDs can change between `research_start` and `research_status` calls.\n\n### Step 5: Import Discovered Sources\n\nUse `research_import` to bring discovered sources into the notebook for deeper analysis.\n\n### Step 6: Query for Insights\n\nUse `notebook_query` to ask 3-5 targeted questions based on the research type:\n\n1. **Overview**: \"What are the key findings about [topic]?\"\n2. **Opportunities**: \"What opportunities or gaps exist in this space?\"\n3. **Actions**: \"What are the most actionable insights from this research?\"\n4. **Risks**: \"What are the main risks, challenges, or counterarguments?\"\n5. **Custom**: A question specific to the research type (e.g., \"Who are the top 5 competitors and how do they differentiate?\" for competitive intel)\n\n### Step 7: Write Research Brief\n\nSave the findings to a local file using the research brief template:\n\n**File path:** `research\u002F[topic-slug]-[YYYY-MM-DD].md`\n\nUse the template from [research-brief-template.md](research-brief-template.md) to structure the output. Create the `research\u002F` directory if it does not exist.\n\n### Step 8: Present Takeaways\n\nAfter saving, present the user with:\n- **3-5 headline findings** (bullets, direct, no filler)\n- **1-2 recommended actions** connected to the user's stated goals\n- **Surprises or contrarian findings** -- anything that challenges assumptions\n- **The file path** where the full brief is saved\n- **The NotebookLM notebook URL** so the user can explore sources directly\n\n### Step 9 (Optional): Generate Studio Artifacts\n\nAsk the user: \"Want me to generate any artifacts from this research? Options: slides, audio (podcast), video, infographic, report, mind map.\"\n\nIf yes, use `studio_create` with the notebook_id from Step 2.\n\n**Available artifact types and recommended settings:**\n\n| Type | Key params | Best for |\n|------|-----------|----------|\n| `slide_deck` | `slide_format`: `detailed_deck` or `presenter_slides`; `slide_length`: `short` or `default` | Executive presentations, client pitches |\n| `audio` | `audio_format`: `deep_dive`, `brief`, `critique`, or `debate`; `audio_length`: `short`, `default`, `long` | Podcast-style deep dives, learning on the go |\n| `video` | `video_format`: `explainer`, `brief`, `cinematic`; `visual_style`: `auto_select`, `classic`, `whiteboard`, etc. | Visual explainers, social media content |\n| `infographic` | `orientation`: `landscape`, `portrait`, `square`; `infographic_style`: `professional`, `bento_grid`, etc. | One-pagers, social sharing |\n| `report` | `report_format`: `Briefing Doc`, `Study Guide`, `Blog Post`, `Create Your Own` | Written deliverables, summaries |\n| `mind_map` | `title` | Visual knowledge mapping |\n\n**Common params for all artifact types:**\n- `language`: Set to the user's preferred language (e.g., `\"en\"`, `\"es\"`, `\"pt\"`)\n- `focus_prompt`: A clear directive about what to emphasize in the artifact\n- `confirm`: Must be `true` to proceed with generation\n\n**After creating an artifact:**\n1. Poll `studio_status` until `completed` (audio\u002Fvideo: 5-15 min; slides\u002Finfographics: 2-5 min)\n2. Use `download_artifact` to save locally if needed\n3. Provide the notebook URL so the user can access artifacts directly\n\n**Tips:**\n- `audio` with `deep_dive` format produces the best podcast-style analysis\n- `slide_deck` with `detailed_deck` format works best for standalone reading; `presenter_slides` is better when accompanied by speaker notes\n- Audio status may show `\"unknown\"` once completed -- check for `audio_url` presence instead of waiting for a `\"completed\"` status\n\n## Notes\n\n- Fast mode is recommended as the default. Deep mode is powerful but can take 10+ minutes and occasionally stalls.\n- Always confirm the research scope with the user before starting -- a well-scoped query produces dramatically better results.\n- The research brief template ensures consistent, actionable output across all research types.\n\n## Additional Resources\n\n- [research-brief-template.md](research-brief-template.md) -- Template for structuring research brief output\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdeep-research-notebooklm",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add davila7\u002Fclaude-code-templates --skill deep-research-notebooklm",{"tool":69,"command":70},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill deep-research-notebooklm",{"tool":72,"command":73},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill deep-research-notebooklm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdeep-research-notebooklm\u002FSKILL.md",true,[77,83],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"agents\u002Fopenai.yaml","REFERENCE","参考文档",135,"https:\u002F\u002Fgithub.com\u002FHasarAli\u002Fdotfiles\u002Fblob\u002Fmain\u002Fdot-agents\u002Fskills\u002Fto-spec\u002Fagents\u002Fopenai.yaml",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"research-brief-template.md",1191,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdeep-research-notebooklm\u002Fresearch-brief-template.md",[88,116,143,175,209,233],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":95,"publishedAt":48},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[94],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"publishedAt":48},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[122],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[124,125,126,127,128,129,130,131,132,133,134,105,135,136,137,138,139,140,141,142],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"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":158,"publishedAt":48},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[149,152,153],{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":154,"categoryName":155,"taxonomyType":156,"taxonomyTypeDesc":157},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[159,160,161,162,163,164,165,101,166,167,168,169,170,171,42,104,172,173,105,174],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","access","database","query",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":186,"publishedAt":48},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[187,188,189,46,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,101,208],"artifacts builder","web","web artifacts builder","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":213,"sourceHtmlUrl":214,"categories":215,"keywords":220,"publishedAt":48},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[216,219],{"categoryNo":217,"categoryName":218,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},[221,222,223,224,225,188,226,196,197,227,228,229,230,101,231,107,232],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":237,"sourceHtmlUrl":238,"categories":239,"keywords":247,"publishedAt":48},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[240,243,244],{"categoryNo":241,"categoryName":242,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":245,"categoryName":246,"taxonomyType":156,"taxonomyTypeDesc":157},"01JBSEEDCAT000000000000036","内容创作者",[248,249,250,251,252,253,254,255,46,256,199,135,257,258,259,260,261,171,262,105,263,45,264,265,266,113,267],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]