[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718AN12MJXDCPXF94HR7GGJ":3,"skill-same:01M718AN12MJXDCPXF94HR7GGJ":71},{"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":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M718AN12MJXDCPXF94HR7GGJ","write-query",null,"Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.","anthropics\u002Fknowledge-work-plugins","anthropics","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4","01M697XGDPXJAVQ8391V2BCYTE","Anthropic","01M7188MPC48111TN566QNQE3X",35660,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwrite-query\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"write","write query","query","bigquery","table","language","data","syntax","sql","multi","with","natural-language","best practices","you","when","use","snowflake","for","postgres","2026-11-10 10:12:30",false,"---\nname: write-query\ndescription: Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.\nargument-hint: \"\u003Cdescription of what data you need>\"\n---\n\n# \u002Fwrite-query - Write Optimized SQL\n\n> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](..\u002F..\u002FCONNECTORS.md).\n\nWrite a SQL query from a natural language description, optimized for your specific SQL dialect and following best practices.\n\n## Usage\n\n```\n\u002Fwrite-query \u003Cdescription of what data you need>\n```\n\n## Workflow\n\n### 1. Understand the Request\n\nParse the user's description to identify:\n\n- **Output columns**: What fields should the result include?\n- **Filters**: What conditions limit the data (time ranges, segments, statuses)?\n- **Aggregations**: Are there GROUP BY operations, counts, sums, averages?\n- **Joins**: Does this require combining multiple tables?\n- **Ordering**: How should results be sorted?\n- **Limits**: Is there a top-N or sample requirement?\n\n### 2. Determine SQL Dialect\n\nIf the user's SQL dialect is not already known, ask which they use:\n\n- **PostgreSQL** (including Aurora, RDS, Supabase, Neon)\n- **Snowflake**\n- **BigQuery** (Google Cloud)\n- **Redshift** (Amazon)\n- **Databricks SQL**\n- **MySQL** (including Aurora MySQL, PlanetScale)\n- **SQL Server** (Microsoft)\n- **DuckDB**\n- **SQLite**\n- **Other** (ask for specifics)\n\nRemember the dialect for future queries in the same session.\n\n### 3. Discover Schema (If Warehouse Connected)\n\nIf a data warehouse MCP server is connected:\n\n1. Search for relevant tables based on the user's description\n2. Inspect column names, types, and relationships\n3. Check for partitioning or clustering keys that affect performance\n4. Look for pre-built views or materialized views that might simplify the query\n\n### 4. Write the Query\n\nFollow these best practices:\n\n**Structure:**\n- Use CTEs (WITH clauses) for readability when queries have multiple logical steps\n- One CTE per logical transformation or data source\n- Name CTEs descriptively (e.g., `daily_signups`, `active_users`, `revenue_by_product`)\n\n**Performance:**\n- Never use `SELECT *` in production queries -- specify only needed columns\n- Filter early (push WHERE clauses as close to the base tables as possible)\n- Use partition filters when available (especially date partitions)\n- Prefer `EXISTS` over `IN` for subqueries with large result sets\n- Use appropriate JOIN types (don't use LEFT JOIN when INNER JOIN is correct)\n- Avoid correlated subqueries when a JOIN or window function works\n- Be mindful of exploding joins (many-to-many)\n\n**Readability:**\n- Add comments explaining the \"why\" for non-obvious logic\n- Use consistent indentation and formatting\n- Alias tables with meaningful short names (not just `a`, `b`, `c`)\n- Put each major clause on its own line\n\n**Dialect-specific optimizations:**\n- Apply dialect-specific syntax and functions (see `sql-queries` skill for details)\n- Use dialect-appropriate date functions, string functions, and window syntax\n- Note any dialect-specific performance features (e.g., Snowflake clustering, BigQuery partitioning)\n\n### 5. Present the Query\n\nProvide:\n\n1. **The complete query** in a SQL code block with syntax highlighting\n2. **Brief explanation** of what each CTE or section does\n3. **Performance notes** if relevant (expected cost, partition usage, potential bottlenecks)\n4. **Modification suggestions** -- how to adjust for common variations (different time range, different granularity, additional filters)\n\n### 6. Offer to Execute\n\nIf a data warehouse is connected, offer to run the query and analyze the results. If the user wants to run it themselves, the query is ready to copy-paste.\n\n## Examples\n\n**Simple aggregation:**\n```\n\u002Fwrite-query Count of orders by status for the last 30 days\n```\n\n**Complex analysis:**\n```\n\u002Fwrite-query Cohort retention analysis -- group users by their signup month, then show what percentage are still active (had at least one event) at 1, 3, 6, and 12 months after signup\n```\n\n**Performance-critical:**\n```\n\u002Fwrite-query We have a 500M row events table partitioned by date. Find the top 100 users by event count in the last 7 days with their most recent event type.\n```\n\n## Tips\n\n- Mention your SQL dialect upfront to get the right syntax immediately\n- If you know the table names, include them -- otherwise Claude will help you find them\n- Specify if you need the query to be idempotent (safe to re-run) or one-time\n- For recurring queries, mention if it should be parameterized for date ranges\n","# \u002Fwrite-query - Write Optimized SQL\n\n> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](..\u002F..\u002FCONNECTORS.md).\n\nWrite a SQL query from a natural language description, optimized for your specific SQL dialect and following best practices.\n\n## Usage\n\n```\n\u002Fwrite-query \u003Cdescription of what data you need>\n```\n\n## Workflow\n\n### 1. Understand the Request\n\nParse the user's description to identify:\n\n- **Output columns**: What fields should the result include?\n- **Filters**: What conditions limit the data (time ranges, segments, statuses)?\n- **Aggregations**: Are there GROUP BY operations, counts, sums, averages?\n- **Joins**: Does this require combining multiple tables?\n- **Ordering**: How should results be sorted?\n- **Limits**: Is there a top-N or sample requirement?\n\n### 2. Determine SQL Dialect\n\nIf the user's SQL dialect is not already known, ask which they use:\n\n- **PostgreSQL** (including Aurora, RDS, Supabase, Neon)\n- **Snowflake**\n- **BigQuery** (Google Cloud)\n- **Redshift** (Amazon)\n- **Databricks SQL**\n- **MySQL** (including Aurora MySQL, PlanetScale)\n- **SQL Server** (Microsoft)\n- **DuckDB**\n- **SQLite**\n- **Other** (ask for specifics)\n\nRemember the dialect for future queries in the same session.\n\n### 3. Discover Schema (If Warehouse Connected)\n\nIf a data warehouse MCP server is connected:\n\n1. Search for relevant tables based on the user's description\n2. Inspect column names, types, and relationships\n3. Check for partitioning or clustering keys that affect performance\n4. Look for pre-built views or materialized views that might simplify the query\n\n### 4. Write the Query\n\nFollow these best practices:\n\n**Structure:**\n- Use CTEs (WITH clauses) for readability when queries have multiple logical steps\n- One CTE per logical transformation or data source\n- Name CTEs descriptively (e.g., `daily_signups`, `active_users`, `revenue_by_product`)\n\n**Performance:**\n- Never use `SELECT *` in production queries -- specify only needed columns\n- Filter early (push WHERE clauses as close to the base tables as possible)\n- Use partition filters when available (especially date partitions)\n- Prefer `EXISTS` over `IN` for subqueries with large result sets\n- Use appropriate JOIN types (don't use LEFT JOIN when INNER JOIN is correct)\n- Avoid correlated subqueries when a JOIN or window function works\n- Be mindful of exploding joins (many-to-many)\n\n**Readability:**\n- Add comments explaining the \"why\" for non-obvious logic\n- Use consistent indentation and formatting\n- Alias tables with meaningful short names (not just `a`, `b`, `c`)\n- Put each major clause on its own line\n\n**Dialect-specific optimizations:**\n- Apply dialect-specific syntax and functions (see `sql-queries` skill for details)\n- Use dialect-appropriate date functions, string functions, and window syntax\n- Note any dialect-specific performance features (e.g., Snowflake clustering, BigQuery partitioning)\n\n### 5. Present the Query\n\nProvide:\n\n1. **The complete query** in a SQL code block with syntax highlighting\n2. **Brief explanation** of what each CTE or section does\n3. **Performance notes** if relevant (expected cost, partition usage, potential bottlenecks)\n4. **Modification suggestions** -- how to adjust for common variations (different time range, different granularity, additional filters)\n\n### 6. Offer to Execute\n\nIf a data warehouse is connected, offer to run the query and analyze the results. If the user wants to run it themselves, the query is ready to copy-paste.\n\n## Examples\n\n**Simple aggregation:**\n```\n\u002Fwrite-query Count of orders by status for the last 30 days\n```\n\n**Complex analysis:**\n```\n\u002Fwrite-query Cohort retention analysis -- group users by their signup month, then show what percentage are still active (had at least one event) at 1, 3, 6, and 12 months after signup\n```\n\n**Performance-critical:**\n```\n\u002Fwrite-query We have a 500M row events table partitioned by date. Find the top 100 users by event count in the last 7 days with their most recent event type.\n```\n\n## Tips\n\n- Mention your SQL dialect upfront to get the right syntax immediately\n- If you know the table names, include them -- otherwise Claude will help you find them\n- Specify if you need the query to be idempotent (safe to re-run) or one-time\n- For recurring queries, mention if it should be parameterized for date ranges\n",{"name":5,"description":7,"argument-hint":48},"\u003Cdescription of what data you need>","main","e605d8ddbfb688da57dfaaceebd93f12a64ef1d3","data\u002Fskills\u002Fwrite-query",3944,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins","https:\u002F\u002Fgithub.com\u002Fanthropics","https:\u002F\u002Fanthropic.com","2026-11-14 06:21:52",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add anthropics\u002Fknowledge-work-plugins --skill write-query",{"tool":63,"command":64},"bunx","bunx skills add anthropics\u002Fknowledge-work-plugins --skill write-query",{"tool":66,"command":67},"pnpm","pnpm dlx skills add anthropics\u002Fknowledge-work-plugins --skill write-query","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwrite-query\u002FSKILL.md",true,[],[72,98,123,145,172,197],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":43},"01M718Y97HCGW12Q53PSJDY03W","ticket-deflector","Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says \"draft a response,\" \"answer this customer,\" \"where's my order,\" \"I want a refund,\" \"check my orders,\" or \"anything about to blow up.\"","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fticket-deflector\u002FSKILL.md",[],[79,80,81,82,83,84,85,38,86,87,88,89,90,91,92,93,94,95,96,97],"ticket","ticket deflector","mode","up","payments","open","writing","workflow","ask","Connector","text","mail","window","next","customer","support","crm","past","read",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":43},"01M718Y55HFAN5705ME5VEDZ9K","tax-season-organizer","Prepares tax-season materials for the owner's accountant, not tax advice. US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags.\nAny tax request routes first to \u002Ftax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: \"books are closed, now do the 1099s,\" \"run the quarterly estimate off the closed numbers,\" or \"just the contractor W-9 list.\"","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-season-organizer\u002FSKILL.md",[],[105,106,107,108,109,110,111,112,113,114,38,115,116,117,118,119,97,120,121,122],"tax season organizer","tax","us federal","which","netsuite","paid","end","usd","only","now","books","xero","scanning","run","request","flags","list","stripe",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":43},"01M718Y13VVPHDD9NTDBVFJ2AZ","tax-prep","Prepares tax materials as a two-link chain — month-end-prep confirms the books are closed and reconciled first, then tax-season-organizer calculates the quarterly estimated payment or builds the year-end 1099-NEC list and accountant packet from those closed numbers. Requires a ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); uses Gusto, PayPal, and Stripe when connected, else CSV upload. US federal tax math; a non-US business gets the closed-books packet. Prep material for a CPA, never tax advice, nothing filed. Trigger on \"quarterly taxes,\" \"estimated tax payment,\" \"how much should I set aside,\" \"1099s,\" \"1099-NEC,\" \"W-9s,\" \"year-end tax prep,\" \"get my books ready for my accountant,\" any phrasing that suggests a tax deadline is coming, or a question about net profit or YTD income that sounds like worry about a tax bill. An owner who says the period's books are already closed routes to tax-season-organizer directly.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-prep\u002FSKILL.md",[],[130,106,131,132,107,133,134,109,135,136,137,138,139,41,140,111,141,142,143,144],"tax prep","prep","SET","as","ready","never","zoho books","get","chain","profit","trigger","material","question","paypal","business",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":154,"publishedAt":43},"01M718XWTWQZ01GYSG9T8RW8TW","speed-to-lead","Makes sure no inbound lead goes unanswered. Watches web forms, the shared inbox, and the CRM for new inquiries, qualifies each one against the owner's criteria, drafts a reply in the owner's voice with real meeting times for the owner to approve and send, routes anything hot to a human right away, and logs the whole thing to the CRM. Never sends on its own. Built to run on a schedule so overnight inquiries are drafted and sorted by morning, and works from forwarded or pasted inquiries when no connector is available. Use this whenever the owner is worried about inbound leads or response time — including phrasings like \"leads are going cold,\" \"did anyone answer that inquiry,\" \"I keep missing form submissions,\" \"nothing falls through the cracks,\" \"handle my inbound,\" \"respond to this lead,\" or \"we lose people because nobody gets back fast enough.\" Reach for it for a single unanswered inquiry as readily as for a standing setup.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fspeed-to-lead\u002FSKILL.md",[151],{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000034","行政与办公人员",[155,156,157,158,159,160,135,161,162,163,164,165,166,167,38,168,169,88,170,171],"speed to lead","lead","speed","shared","back","cold","time","inbox","send","respond","new","web forms","people","schedule","logs","sure","human",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":43},"01M718XQYMN1KY3TPS33967FK0","social-content-engine","Runs the owner's content operation as a standing thing rather than a one-off: keeps a rolling posting calendar, learns the owner's voice, generates on-brand Canva graphics, writes captions that sound like them, repurposes one asset into every channel format, and stages posts for approval instead of publishing on its own. Pulls product photos and prices from Shopify when connected; with no connectors, drafts the calendar and asset briefs for manual posting. Use this whenever the owner talks about posting, content, or social — including \"I need to post more,\" \"we've gone quiet on Instagram,\" \"what should we post this month,\" \"make the content,\" \"give me a month of posts,\" \"write this in my voice,\" \"repurpose this,\" or \"set up our content calendar.\" Reach for it for a single post as readily as for a full quarter. One handoff: a finished, approved campaign brief goes to canva-creator; this skill owns the standing operation.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsocial-content-engine\u002FSKILL.md",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者",[182,183,184,185,186,187,132,188,189,190,191,82,192,38,193,194,195,97,24,196],"social","social content engine","engine","content engine","social content","content","brief","posts","Captions","prices","campaign","me","product","calendar","drafts",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":203,"publishedAt":43},"01M718XMPJVH0EVRVP998BVZPC","smb-router","The front door to the Small Business plugin. Listens to what the owner needs right now — vague or specific — and routes them to the best skill or slash command for the moment. Also serves as a guide: explains what's available, suggests what to try next, and adapts recommendations based on stored business context. Trigger whenever the owner asks \"what can you do,\" \"help me with my business,\" \"what should I focus on,\" \"I don't know where to start,\" or any open-ended business request that doesn't clearly match a single skill.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsmb-router\u002FSKILL.md",[],[204,205,206,133,207,41,140,208,84,209,114,144,37,210,211,34,212,193,213,92],"router","smb","smb router","do","front","single","vague","can","plugin","focus"]