[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFCHHM28YXQRQFPD9F03DM":3,"skill-same:01M6AFCHHM28YXQRQFPD9F03DM":98},{"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":29,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":61,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6AFCHHM28YXQRQFPD9F03DM","denario",null,"Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33286,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdenario\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"ready","Publication","when","for","papers","scientific","literature","end","computational","ideas","agent orchestration","data analysis","system","multiagent","analysis","LaTeX","data","experiments","workflows","orchestration","research","2026-11-10 10:12:30",false,"---\nname: denario\ndescription: Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.\n---\n\n# Denario\n\n## Overview\n\nDenario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing.\n\n## When to Use This Skill\n\nUse this skill when:\n- Analyzing datasets to generate novel research hypotheses\n- Developing structured research methodologies\n- Executing computational experiments and generating visualizations\n- Conducting literature searches for research context\n- Writing journal-formatted LaTeX papers from research results\n- Automating the complete research pipeline from data to publication\n\n## Installation\n\nInstall denario using uv (recommended):\n\n```bash\nuv init\nuv add \"denario[app]\"\n```\n\nOr using pip:\n\n```bash\nuv pip install \"denario[app]\"\n```\n\nFor Docker deployment or building from source, see `references\u002Finstallation.md`.\n\n## LLM API Configuration\n\nDenario requires API keys from supported LLM providers. Supported providers include:\n- Google Vertex AI\n- OpenAI\n- Other LLM services compatible with AG2\u002FLangGraph\n\nStore API keys securely using environment variables or `.env` files. For detailed configuration instructions including Vertex AI setup, see `references\u002Fllm_configuration.md`.\n\n## Core Research Workflow\n\nDenario follows a structured four-stage research pipeline:\n\n### 1. Data Description\n\nDefine the research context by specifying available data and tools:\n\n```python\nfrom denario import Denario\n\nden = Denario(project_dir=\".\u002Fmy_research\")\nden.set_data_description(\"\"\"\nAvailable datasets: time-series data on X and Y\nTools: pandas, sklearn, matplotlib\nResearch domain: [specify domain]\n\"\"\")\n```\n\n### 2. Idea Generation\n\nGenerate research hypotheses from the data description:\n\n```python\nden.get_idea()\n```\n\nThis produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea:\n\n```python\nden.set_idea(\"Custom research hypothesis\")\n```\n\n### 3. Methodology Development\n\nDevelop the research methodology:\n\n```python\nden.get_method()\n```\n\nThis creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:\n\n```python\nden.set_method(\"path\u002Fto\u002Fmethodology.md\")\n```\n\n### 4. Results Generation\n\nExecute computational experiments and generate analysis:\n\n```python\nden.get_results()\n```\n\nThis runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:\n\n```python\nden.set_results(\"path\u002Fto\u002Fresults.md\")\n```\n\n### 5. Paper Generation\n\nCreate a publication-ready LaTeX paper:\n\n```python\nfrom denario import Journal\n\nden.get_paper(journal=Journal.APS)\n```\n\nThe generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.\n\n## Available Journals\n\nDenario supports multiple journal formatting styles:\n- `Journal.APS` - American Physical Society format\n- Additional journals may be available; check `references\u002Fresearch_pipeline.md` for the complete list\n\n## Launching the GUI\n\nRun the graphical user interface:\n\n```bash\ndenario run\n```\n\nThis launches a web-based interface for interactive research workflow management.\n\n## Common Workflows\n\n### End-to-End Research Pipeline\n\n```python\nfrom denario import Denario, Journal\n\n# Initialize project\nden = Denario(project_dir=\".\u002Fresearch_project\")\n\n# Define research context\nden.set_data_description(\"\"\"\nDataset: Time-series measurements of [phenomenon]\nAvailable tools: pandas, sklearn, scipy\nResearch goal: Investigate [research question]\n\"\"\")\n\n# Generate research idea\nden.get_idea()\n\n# Develop methodology\nden.get_method()\n\n# Execute analysis\nden.get_results()\n\n# Create publication\nden.get_paper(journal=Journal.APS)\n```\n\n### Hybrid Workflow (Custom + Automated)\n\n```python\n# Provide custom research idea\nden.set_idea(\"Investigate the correlation between X and Y using time-series analysis\")\n\n# Auto-generate methodology\nden.get_method()\n\n# Auto-generate results\nden.get_results()\n\n# Generate paper\nden.get_paper(journal=Journal.APS)\n```\n\n### Literature Search Integration\n\nFor literature search functionality and additional workflow examples, see `references\u002Fexamples.md`.\n\n## Advanced Features\n\n- **Multiagent orchestration**: AG2 and LangGraph coordinate specialized agents for different research tasks\n- **Reproducible research**: All stages produce structured outputs that can be version-controlled\n- **Journal integration**: Automatic formatting for target publication venues\n- **Flexible input**: Manual or automated at each pipeline stage\n- **Docker deployment**: Containerized environment with LaTeX and all dependencies\n\n## Detailed References\n\nFor comprehensive documentation:\n- **Installation options**: `references\u002Finstallation.md`\n- **LLM configuration**: `references\u002Fllm_configuration.md`\n- **Complete API reference**: `references\u002Fresearch_pipeline.md`\n- **Example workflows**: `references\u002Fexamples.md`\n\n## Troubleshooting\n\nCommon issues and solutions:\n- **API key errors**: Ensure environment variables are set correctly (see `references\u002Fllm_configuration.md`)\n- **LaTeX compilation**: Install TeX distribution or use Docker image with pre-installed LaTeX\n- **Package conflicts**: Use virtual environments or Docker for isolation\n- **Python version**: Requires Python 3.12 or higher\n","# Denario\n\n## Overview\n\nDenario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing.\n\n## When to Use This Skill\n\nUse this skill when:\n- Analyzing datasets to generate novel research hypotheses\n- Developing structured research methodologies\n- Executing computational experiments and generating visualizations\n- Conducting literature searches for research context\n- Writing journal-formatted LaTeX papers from research results\n- Automating the complete research pipeline from data to publication\n\n## Installation\n\nInstall denario using uv (recommended):\n\n```bash\nuv init\nuv add \"denario[app]\"\n```\n\nOr using pip:\n\n```bash\nuv pip install \"denario[app]\"\n```\n\nFor Docker deployment or building from source, see `references\u002Finstallation.md`.\n\n## LLM API Configuration\n\nDenario requires API keys from supported LLM providers. Supported providers include:\n- Google Vertex AI\n- OpenAI\n- Other LLM services compatible with AG2\u002FLangGraph\n\nStore API keys securely using environment variables or `.env` files. For detailed configuration instructions including Vertex AI setup, see `references\u002Fllm_configuration.md`.\n\n## Core Research Workflow\n\nDenario follows a structured four-stage research pipeline:\n\n### 1. Data Description\n\nDefine the research context by specifying available data and tools:\n\n```python\nfrom denario import Denario\n\nden = Denario(project_dir=\".\u002Fmy_research\")\nden.set_data_description(\"\"\"\nAvailable datasets: time-series data on X and Y\nTools: pandas, sklearn, matplotlib\nResearch domain: [specify domain]\n\"\"\")\n```\n\n### 2. Idea Generation\n\nGenerate research hypotheses from the data description:\n\n```python\nden.get_idea()\n```\n\nThis produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea:\n\n```python\nden.set_idea(\"Custom research hypothesis\")\n```\n\n### 3. Methodology Development\n\nDevelop the research methodology:\n\n```python\nden.get_method()\n```\n\nThis creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:\n\n```python\nden.set_method(\"path\u002Fto\u002Fmethodology.md\")\n```\n\n### 4. Results Generation\n\nExecute computational experiments and generate analysis:\n\n```python\nden.get_results()\n```\n\nThis runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:\n\n```python\nden.set_results(\"path\u002Fto\u002Fresults.md\")\n```\n\n### 5. Paper Generation\n\nCreate a publication-ready LaTeX paper:\n\n```python\nfrom denario import Journal\n\nden.get_paper(journal=Journal.APS)\n```\n\nThe generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.\n\n## Available Journals\n\nDenario supports multiple journal formatting styles:\n- `Journal.APS` - American Physical Society format\n- Additional journals may be available; check `references\u002Fresearch_pipeline.md` for the complete list\n\n## Launching the GUI\n\nRun the graphical user interface:\n\n```bash\ndenario run\n```\n\nThis launches a web-based interface for interactive research workflow management.\n\n## Common Workflows\n\n### End-to-End Research Pipeline\n\n```python\nfrom denario import Denario, Journal\n\n# Initialize project\nden = Denario(project_dir=\".\u002Fresearch_project\")\n\n# Define research context\nden.set_data_description(\"\"\"\nDataset: Time-series measurements of [phenomenon]\nAvailable tools: pandas, sklearn, scipy\nResearch goal: Investigate [research question]\n\"\"\")\n\n# Generate research idea\nden.get_idea()\n\n# Develop methodology\nden.get_method()\n\n# Execute analysis\nden.get_results()\n\n# Create publication\nden.get_paper(journal=Journal.APS)\n```\n\n### Hybrid Workflow (Custom + Automated)\n\n```python\n# Provide custom research idea\nden.set_idea(\"Investigate the correlation between X and Y using time-series analysis\")\n\n# Auto-generate methodology\nden.get_method()\n\n# Auto-generate results\nden.get_results()\n\n# Generate paper\nden.get_paper(journal=Journal.APS)\n```\n\n### Literature Search Integration\n\nFor literature search functionality and additional workflow examples, see `references\u002Fexamples.md`.\n\n## Advanced Features\n\n- **Multiagent orchestration**: AG2 and LangGraph coordinate specialized agents for different research tasks\n- **Reproducible research**: All stages produce structured outputs that can be version-controlled\n- **Journal integration**: Automatic formatting for target publication venues\n- **Flexible input**: Manual or automated at each pipeline stage\n- **Docker deployment**: Containerized environment with LaTeX and all dependencies\n\n## Detailed References\n\nFor comprehensive documentation:\n- **Installation options**: `references\u002Finstallation.md`\n- **LLM configuration**: `references\u002Fllm_configuration.md`\n- **Complete API reference**: `references\u002Fresearch_pipeline.md`\n- **Example workflows**: `references\u002Fexamples.md`\n\n## Troubleshooting\n\nCommon issues and solutions:\n- **API key errors**: Ensure environment variables are set correctly (see `references\u002Fllm_configuration.md`)\n- **LaTeX compilation**: Install TeX distribution or use Docker image with pre-installed LaTeX\n- **Package conflicts**: Use virtual environments or Docker for isolation\n- **Python version**: Requires Python 3.12 or 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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",[105],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[107,108,109,110,111,33,112,113,114,32,115,116,117,118,119,120,121,122,123,124],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":132,"publishedAt":51},"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",[131],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[133,134,135,136,137,138,139,140,141,142,143,32,144,145,146,147,148,149,150,151],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":167,"publishedAt":51},"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",[158,161,162],{"categoryNo":159,"categoryName":160,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":163,"categoryName":164,"taxonomyType":165,"taxonomyTypeDesc":166},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[168,169,170,171,172,173,174,33,175,176,177,178,179,180,181,114,182,183,32,184],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":190,"sourceHtmlUrl":191,"categories":192,"keywords":196,"publishedAt":51},"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",[193],{"categoryNo":194,"categoryName":195,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,33,219],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":224,"sourceHtmlUrl":225,"categories":226,"keywords":231,"publishedAt":51},"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",[227,230],{"categoryNo":228,"categoryName":229,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":194,"categoryName":195,"taxonomyType":21,"taxonomyTypeDesc":22},[232,233,234,235,236,198,237,207,208,238,239,240,241,33,242,116,243],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":245,"name":246,"nameZh":6,"description":247,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":248,"sourceHtmlUrl":249,"categories":250,"keywords":258,"publishedAt":51},"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",[251,254,255],{"categoryNo":252,"categoryName":253,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":256,"categoryName":257,"taxonomyType":165,"taxonomyTypeDesc":166},"01JBSEEDCAT000000000000036","内容创作者",[259,260,261,262,263,264,46,265,200,266,210,144,267,268,269,270,271,180,272,32,273,44,274,275,276,122,277],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]