[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AF1NBNS6WVZVT8614YK8C5":3,"skill-same:01M6AF1NBNS6WVZVT8614YK8C5":73},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6AF1NBNS6WVZVT8614YK8C5","excel-analysis",null,"Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.","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\u002Fenterprise-communication\u002Fexcel-analysis\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000004","电子表格","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"xlsx","excel analysis","analysis","when","tabular","tables","excel spreadsheets","files","use","analyze","Charts","data","analyzing","generate","create","spreadsheets","data analysis","2026-11-10 10:12:30",false,"---\nname: excel-analysis\ndescription: Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.\n---\n\n# Excel Analysis\n\n## Quick start\n\nRead Excel files with pandas:\n\n```python\nimport pandas as pd\n\n# Read Excel file\ndf = pd.read_excel(\"data.xlsx\", sheet_name=\"Sheet1\")\n\n# Display first few rows\nprint(df.head())\n\n# Basic statistics\nprint(df.describe())\n```\n\n## Reading multiple sheets\n\nProcess all sheets in a workbook:\n\n```python\nimport pandas as pd\n\n# Read all sheets\nexcel_file = pd.ExcelFile(\"workbook.xlsx\")\n\nfor sheet_name in excel_file.sheet_names:\n    df = pd.read_excel(excel_file, sheet_name=sheet_name)\n    print(f\"\\n{sheet_name}:\")\n    print(df.head())\n```\n\n## Data analysis\n\nPerform common analysis tasks:\n\n```python\nimport pandas as pd\n\ndf = pd.read_excel(\"sales.xlsx\")\n\n# Group by and aggregate\nsales_by_region = df.groupby(\"region\")[\"sales\"].sum()\nprint(sales_by_region)\n\n# Filter data\nhigh_sales = df[df[\"sales\"] > 10000]\n\n# Calculate metrics\ndf[\"profit_margin\"] = (df[\"revenue\"] - df[\"cost\"]) \u002F df[\"revenue\"]\n\n# Sort by column\ndf_sorted = df.sort_values(\"sales\", ascending=False)\n```\n\n## Creating Excel files\n\nWrite data to Excel with formatting:\n\n```python\nimport pandas as pd\n\ndf = pd.DataFrame({\n    \"Product\": [\"A\", \"B\", \"C\"],\n    \"Sales\": [100, 200, 150],\n    \"Profit\": [20, 40, 30]\n})\n\n# Write to Excel\nwriter = pd.ExcelWriter(\"output.xlsx\", engine=\"openpyxl\")\ndf.to_excel(writer, sheet_name=\"Sales\", index=False)\n\n# Get worksheet for formatting\nworksheet = writer.sheets[\"Sales\"]\n\n# Auto-adjust column widths\nfor column in worksheet.columns:\n    max_length = 0\n    column_letter = column[0].column_letter\n    for cell in column:\n        if len(str(cell.value)) > max_length:\n            max_length = len(str(cell.value))\n    worksheet.column_dimensions[column_letter].width = max_length + 2\n\nwriter.close()\n```\n\n## Pivot tables\n\nCreate pivot tables programmatically:\n\n```python\nimport pandas as pd\n\ndf = pd.read_excel(\"sales_data.xlsx\")\n\n# Create pivot table\npivot = pd.pivot_table(\n    df,\n    values=\"sales\",\n    index=\"region\",\n    columns=\"product\",\n    aggfunc=\"sum\",\n    fill_value=0\n)\n\nprint(pivot)\n\n# Save pivot table\npivot.to_excel(\"pivot_report.xlsx\")\n```\n\n## Charts and visualization\n\nGenerate charts from Excel data:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_excel(\"data.xlsx\")\n\n# Create bar chart\ndf.plot(x=\"category\", y=\"value\", kind=\"bar\")\nplt.title(\"Sales by Category\")\nplt.xlabel(\"Category\")\nplt.ylabel(\"Sales\")\nplt.tight_layout()\nplt.savefig(\"chart.png\")\n\n# Create pie chart\ndf.set_index(\"category\")[\"value\"].plot(kind=\"pie\", autopct=\"%1.1f%%\")\nplt.title(\"Market Share\")\nplt.ylabel(\"\")\nplt.savefig(\"pie_chart.png\")\n```\n\n## Data cleaning\n\nClean and prepare Excel data:\n\n```python\nimport pandas as pd\n\ndf = pd.read_excel(\"messy_data.xlsx\")\n\n# Remove duplicates\ndf = df.drop_duplicates()\n\n# Handle missing values\ndf = df.fillna(0)  # or df.dropna()\n\n# Remove whitespace\ndf[\"name\"] = df[\"name\"].str.strip()\n\n# Convert data types\ndf[\"date\"] = pd.to_datetime(df[\"date\"])\ndf[\"amount\"] = pd.to_numeric(df[\"amount\"], errors=\"coerce\")\n\n# Save cleaned data\ndf.to_excel(\"cleaned_data.xlsx\", index=False)\n```\n\n## Merging and joining\n\nCombine multiple Excel files:\n\n```python\nimport pandas as pd\n\n# Read multiple files\ndf1 = pd.read_excel(\"sales_q1.xlsx\")\ndf2 = pd.read_excel(\"sales_q2.xlsx\")\n\n# Concatenate vertically\ncombined = pd.concat([df1, df2], ignore_index=True)\n\n# Merge on common column\ncustomers = pd.read_excel(\"customers.xlsx\")\nsales = pd.read_excel(\"sales.xlsx\")\n\nmerged = pd.merge(sales, customers, on=\"customer_id\", how=\"left\")\n\nmerged.to_excel(\"merged_data.xlsx\", index=False)\n```\n\n## Advanced formatting\n\nApply conditional formatting and styles:\n\n```python\nimport pandas as pd\nfrom openpyxl import load_workbook\nfrom openpyxl.styles import PatternFill, Font\n\n# Create Excel file\ndf = pd.DataFrame({\n    \"Product\": [\"A\", \"B\", \"C\"],\n    \"Sales\": [100, 200, 150]\n})\n\ndf.to_excel(\"formatted.xlsx\", index=False)\n\n# Load workbook for formatting\nwb = load_workbook(\"formatted.xlsx\")\nws = wb.active\n\n# Apply conditional formatting\nred_fill = PatternFill(start_color=\"FF0000\", end_color=\"FF0000\", fill_type=\"solid\")\ngreen_fill = PatternFill(start_color=\"00FF00\", end_color=\"00FF00\", fill_type=\"solid\")\n\nfor row in range(2, len(df) + 2):\n    cell = ws[f\"B{row}\"]\n    if cell.value \u003C 150:\n        cell.fill = red_fill\n    else:\n        cell.fill = green_fill\n\n# Bold headers\nfor cell in ws[1]:\n    cell.font = Font(bold=True)\n\nwb.save(\"formatted.xlsx\")\n```\n\n## Performance tips\n\n- Use `read_excel` with `usecols` to read specific columns only\n- Use `chunksize` for very large files\n- Consider using `engine='openpyxl'` or `engine='xlrd'` based on file type\n- Use `dtype` parameter to specify column types for faster reading\n\n## Available packages\n\n- **pandas** - Data analysis and manipulation (primary)\n- **openpyxl** - Excel file creation and formatting\n- **xlrd** - Reading older .xls files\n- **xlsxwriter** - Advanced Excel writing capabilities\n- **matplotlib** - Chart generation\n","# Excel Analysis\n\n## Quick start\n\nRead Excel files with pandas:\n\n```python\nimport pandas as pd\n\n# Read Excel file\ndf = pd.read_excel(\"data.xlsx\", sheet_name=\"Sheet1\")\n\n# Display first few rows\nprint(df.head())\n\n# Basic statistics\nprint(df.describe())\n```\n\n## Reading multiple sheets\n\nProcess all sheets in a workbook:\n\n```python\nimport pandas as pd\n\n# Read all sheets\nexcel_file = pd.ExcelFile(\"workbook.xlsx\")\n\nfor sheet_name in excel_file.sheet_names:\n    df = pd.read_excel(excel_file, sheet_name=sheet_name)\n    print(f\"\\n{sheet_name}:\")\n    print(df.head())\n```\n\n## Data analysis\n\nPerform common analysis tasks:\n\n```python\nimport pandas as pd\n\ndf = pd.read_excel(\"sales.xlsx\")\n\n# Group by and aggregate\nsales_by_region = df.groupby(\"region\")[\"sales\"].sum()\nprint(sales_by_region)\n\n# Filter data\nhigh_sales = df[df[\"sales\"] > 10000]\n\n# Calculate metrics\ndf[\"profit_margin\"] = (df[\"revenue\"] - df[\"cost\"]) \u002F df[\"revenue\"]\n\n# Sort by column\ndf_sorted = df.sort_values(\"sales\", ascending=False)\n```\n\n## Creating Excel files\n\nWrite data to Excel with formatting:\n\n```python\nimport pandas as pd\n\ndf = pd.DataFrame({\n    \"Product\": [\"A\", \"B\", \"C\"],\n    \"Sales\": [100, 200, 150],\n    \"Profit\": [20, 40, 30]\n})\n\n# Write to Excel\nwriter = pd.ExcelWriter(\"output.xlsx\", engine=\"openpyxl\")\ndf.to_excel(writer, sheet_name=\"Sales\", index=False)\n\n# Get worksheet for formatting\nworksheet = writer.sheets[\"Sales\"]\n\n# Auto-adjust column widths\nfor column in worksheet.columns:\n    max_length = 0\n    column_letter = column[0].column_letter\n    for cell in column:\n        if len(str(cell.value)) > max_length:\n            max_length = len(str(cell.value))\n    worksheet.column_dimensions[column_letter].width = max_length + 2\n\nwriter.close()\n```\n\n## Pivot tables\n\nCreate pivot tables programmatically:\n\n```python\nimport pandas as pd\n\ndf = pd.read_excel(\"sales_data.xlsx\")\n\n# Create pivot table\npivot = pd.pivot_table(\n    df,\n    values=\"sales\",\n    index=\"region\",\n    columns=\"product\",\n    aggfunc=\"sum\",\n    fill_value=0\n)\n\nprint(pivot)\n\n# Save pivot table\npivot.to_excel(\"pivot_report.xlsx\")\n```\n\n## Charts and visualization\n\nGenerate charts from Excel data:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_excel(\"data.xlsx\")\n\n# Create bar chart\ndf.plot(x=\"category\", y=\"value\", kind=\"bar\")\nplt.title(\"Sales by Category\")\nplt.xlabel(\"Category\")\nplt.ylabel(\"Sales\")\nplt.tight_layout()\nplt.savefig(\"chart.png\")\n\n# Create pie chart\ndf.set_index(\"category\")[\"value\"].plot(kind=\"pie\", autopct=\"%1.1f%%\")\nplt.title(\"Market Share\")\nplt.ylabel(\"\")\nplt.savefig(\"pie_chart.png\")\n```\n\n## Data cleaning\n\nClean and prepare Excel data:\n\n```python\nimport pandas as pd\n\ndf = pd.read_excel(\"messy_data.xlsx\")\n\n# Remove duplicates\ndf = df.drop_duplicates()\n\n# Handle missing values\ndf = df.fillna(0)  # or df.dropna()\n\n# Remove whitespace\ndf[\"name\"] = df[\"name\"].str.strip()\n\n# Convert data types\ndf[\"date\"] = pd.to_datetime(df[\"date\"])\ndf[\"amount\"] = pd.to_numeric(df[\"amount\"], errors=\"coerce\")\n\n# Save cleaned data\ndf.to_excel(\"cleaned_data.xlsx\", index=False)\n```\n\n## Merging and joining\n\nCombine multiple Excel files:\n\n```python\nimport pandas as pd\n\n# Read multiple files\ndf1 = pd.read_excel(\"sales_q1.xlsx\")\ndf2 = pd.read_excel(\"sales_q2.xlsx\")\n\n# Concatenate vertically\ncombined = pd.concat([df1, df2], ignore_index=True)\n\n# Merge on common column\ncustomers = pd.read_excel(\"customers.xlsx\")\nsales = pd.read_excel(\"sales.xlsx\")\n\nmerged = pd.merge(sales, customers, on=\"customer_id\", how=\"left\")\n\nmerged.to_excel(\"merged_data.xlsx\", index=False)\n```\n\n## Advanced formatting\n\nApply conditional formatting and styles:\n\n```python\nimport pandas as pd\nfrom openpyxl import load_workbook\nfrom openpyxl.styles import PatternFill, Font\n\n# Create Excel file\ndf = pd.DataFrame({\n    \"Product\": [\"A\", \"B\", \"C\"],\n    \"Sales\": [100, 200, 150]\n})\n\ndf.to_excel(\"formatted.xlsx\", index=False)\n\n# Load workbook for formatting\nwb = load_workbook(\"formatted.xlsx\")\nws = wb.active\n\n# Apply conditional formatting\nred_fill = PatternFill(start_color=\"FF0000\", end_color=\"FF0000\", fill_type=\"solid\")\ngreen_fill = PatternFill(start_color=\"00FF00\", end_color=\"00FF00\", fill_type=\"solid\")\n\nfor row in range(2, len(df) + 2):\n    cell = ws[f\"B{row}\"]\n    if cell.value \u003C 150:\n        cell.fill = red_fill\n    else:\n        cell.fill = green_fill\n\n# Bold headers\nfor cell in ws[1]:\n    cell.font = Font(bold=True)\n\nwb.save(\"formatted.xlsx\")\n```\n\n## Performance tips\n\n- Use `read_excel` with `usecols` to read specific columns only\n- Use `chunksize` for very large files\n- Consider using `engine='openpyxl'` or `engine='xlrd'` based on file type\n- Use `dtype` parameter to specify column types for faster reading\n\n## Available packages\n\n- **pandas** - Data analysis and manipulation (primary)\n- **openpyxl** - Excel file creation and formatting\n- **xlrd** - Reading older .xls files\n- **xlsxwriter** - Advanced Excel writing capabilities\n- **matplotlib** - Chart generation\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fexcel-analysis",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":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add davila7\u002Fclaude-code-templates --skill excel-analysis",{"tool":65,"command":66},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill excel-analysis",{"tool":68,"command":69},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill excel-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fexcel-analysis\u002FSKILL.md",true,[],[74,102,129,162,197,221],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":83,"publishedAt":44},"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",[80],{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[84,85,86,87,88,89,90,91,35,30,92,93,94,95,96,97,98,99,100,101],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":109,"publishedAt":44},"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",[108],{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},[110,111,112,113,114,115,116,117,118,119,120,30,121,122,123,124,125,126,127,128],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":144,"publishedAt":44},"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",[135,138,139],{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[145,146,147,148,149,150,151,89,152,153,154,155,156,157,158,35,159,160,30,161],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":167,"sourceHtmlUrl":168,"categories":169,"keywords":173,"publishedAt":44},"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",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,89,196],"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":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":201,"sourceHtmlUrl":202,"categories":203,"keywords":208,"publishedAt":44},"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",[204,207],{"categoryNo":205,"categoryName":206,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},[209,210,211,212,213,175,214,184,185,215,216,217,218,89,219,93,220],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":225,"sourceHtmlUrl":226,"categories":227,"keywords":235,"publishedAt":44},"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",[228,231,232],{"categoryNo":229,"categoryName":230,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":81,"categoryName":82,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":233,"categoryName":234,"taxonomyType":142,"taxonomyTypeDesc":143},"01JBSEEDCAT000000000000036","内容创作者",[236,237,238,239,240,241,38,242,177,243,187,121,244,245,246,247,248,157,249,30,250,29,251,252,253,99,254],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]