[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FBTZMC91F5XAJF8SHH7W45":3,"skill-same:01M6FBTZMC91F5XAJF8SHH7W45":99},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":27,"publishedAt":56,"linkOnly":57,"markdownContent":58,"markdownBody":59,"frontMatter":60,"refName":37,"commitSha":79,"skillDirectory":80,"forksCount":81,"repositoryHtmlUrl":82,"repositoryHomepageUrl":6,"ownerHtmlUrl":83,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":84,"install":85,"resources":98},"01M6FBTZMC91F5XAJF8SHH7W45","sql-sentinel",null,"Audit SQL for the cost & performance anti-patterns that burn warehouse credits. Scores warehouse health 0-100 and outputs a prioritized cost-reduction plan for BigQuery, Snowflake, Redshift, and Postgres.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",13,"MIT","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-sql-sentinel\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"sql sentinel","sql","sentinel","plan","2026","manual","critical","upstream","patterns","main","postgres","community","cost","credits","only","anti-patterns","gemini","health","data","Optimization","audit","snowflake","redshift","license","finops","run","commit","performance","2026-11-10 10:12:30",false,"---\nname: sql-sentinel\ndescription: \"Audit SQL for the cost & performance anti-patterns that burn warehouse credits. Scores warehouse health 0-100 and outputs a prioritized cost-reduction plan for BigQuery, Snowflake, Redshift, and Postgres.\"\ncategory: data\nrisk: critical\nsource: community\nsource_repo: takeaseatventure\u002Fsql-sentinel\nsource_type: community\ndate_added: \"2026-06-26\"\nauthor: takeaseat\ntags: [sql, bigquery, snowflake, redshift, postgres, data-warehouse, cost-optimization, performance, audit, finops]\ntools: [claude, cursor, codex, gemini]\nplugin:\n  targets:\n    codex: blocked\n    claude: blocked\n  setup:\n    type: manual\n    summary: \"Clone the upstream analyzer only after pinning or reviewing the exact commit to run.\"\n    docs: SKILL.md\nlicense: \"MIT\"\nlicense_source: \"https:\u002F\u002Fgithub.com\u002Ftakeaseatventure\u002Fsql-sentinel\u002Fblob\u002Fmain\u002FLICENSE\"\n---\n\n# sql-sentinel\n\n## Overview\n\nA static-analysis skill that audits SQL for the cost & performance anti-patterns that dominate warehouse bills — `SELECT *`, full-table scans, non-sargable predicates, Cartesian joins, the `NOT IN` NULL trap, and 15 more. It scores warehouse query health 0-100 (A-F) and outputs a prioritized cost-reduction plan, each finding with a `why`, a concrete `fix`, and an estimated savings.\n\nBuilt for analytics engineers (dbt, Looker), data platform teams running FinOps \u002F \"reduce cloud spend\" initiatives, and anyone reviewing a SQL pull request before it hits production. Works across BigQuery, Snowflake, Redshift, and Postgres. Zero dependencies, MIT licensed.\n\nThe executable engine and full rule set live in the source repository: https:\u002F\u002Fgithub.com\u002Ftakeaseatventure\u002Fsql-sentinel. Treat that repository as third-party executable code.\n\n## When to Use This Skill\n\n- A user writes or reviews a query for BigQuery, Snowflake, Redshift, Postgres, or Spark SQL.\n- A user asks \"why is this query so slow?\" or \"why is my warehouse bill so high?\"\n- A user is about to promote a dashboard query or dbt model to production.\n- A data engineer wants a second pair of eyes before a code review or a cost-optimization sweep.\n- A team is running a \"reduce cloud spend\" or FinOps initiative.\n\n## How It Works\n\nThe engine splits a SQL script into statements (honoring quotes and comments), runs 20 rules over each statement, scores health 0-100 weighted by severity (critical 25, high 12, medium 5, low 1), and returns a prioritized cost-reduction plan.\n\n### Step 1: Run the audit\n\nInstall or clone the source repository only after choosing a reviewed commit, tag, or release to trust. Do not run code from a mutable default branch just because this skill links to it:\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Ftakeaseatventure\u002Fsql-sentinel.git\ncd sql-sentinel\ngit checkout \u003Creviewed-commit-or-tag>\nnode scripts\u002Fsql-sentinel.js path\u002Fto\u002Fquery.sql\n```\n\nOr programmatically:\n\n```javascript\nconst { auditSql } = require('.\u002Fscripts\u002Fsql-sentinel');\nconst report = auditSql(yourSqlString, { dialect: 'bigquery' });\nconsole.log(report.healthScore);      \u002F\u002F 0-100\nconsole.log(report.grade);            \u002F\u002F 'A' | 'B' | 'C' | 'D' | 'E' | 'F'\nconsole.log(report.prioritizedPlan);  \u002F\u002F array, worst findings first\n```\n\n### Step 2: Read the prioritized plan\n\nThe output leads with critical findings (Cartesian joins, mass DELETE) and descends to low-severity style issues. Each finding explains *why* it costs money and *how* to fix it.\n\n## Examples\n\n### Example 1: A messy dashboard query\n\n```sql\nSELECT DISTINCT *\nFROM user_events, raw_logs\nWHERE LOWER(event_name) LIKE '%signup%'\n  AND user_id NOT IN (SELECT id FROM deleted_users)\nORDER BY created_at;\n```\n\nIllustrative candidate findings (no engine was run and no score\u002Fcost was measured):\n- CRITICAL: comma-join without a join predicate produces a Cartesian product; actual impact needs plan\u002Fcardinality evidence\n- HIGH: `SELECT *` may read unnecessary columns depending on engine\u002Fplan\n- HIGH: leading-wildcard `LIKE '%signup%'` defeats indexes\n- HIGH: `LOWER(event_name)` defeats indexes (non-sargable)\n- HIGH: `NOT IN (SELECT ...)` — NULL semantics hazard\n- MEDIUM: `SELECT DISTINCT` dedup cost\n- MEDIUM: `ORDER BY` without `LIMIT` sorts the full result\n\n### Example 2: A clean, sargable query\n\n```sql\n-- Illustrative bounded query, not a measured score or proof of no findings\nSELECT id, email, created_at\nFROM users\nWHERE created_at >= TIMESTAMP '2026-01-01'\n  AND created_at \u003C  TIMESTAMP '2026-02-01'\nORDER BY id\nLIMIT 100;\n```\n\nRules are heuristic candidates: expression\u002Ftrigram indexes, partition pruning, small tables and intended CROSS JOIN\u002FUNION semantics can change the verdict. Preserve result equivalence (including NULL\u002Fduplicate behavior), inspect EXPLAIN\u002Fwarehouse dry-run and actual billing before recommending changes. EXPLAIN ANALYZE executes queries and needs separate data\u002Fcost authorization. A health score is not measured savings.\n\n## The 20 rules (ruleset v1.0.0)\n\n| Rule | Severity | Catches |\n|---|---|---|\n| SQL001 | high | `SELECT *` full column scan |\n| SQL002 | critical | No `WHERE` → full table scan |\n| SQL003 | high | `LIKE '%term'` non-sargable |\n| SQL004 | high | Function on column kills index |\n| SQL005 | critical | `CROSS JOIN` \u002F comma-join |\n| SQL006 | medium | `SELECT DISTINCT` dedup cost |\n| SQL007 | medium | `ORDER BY` without `LIMIT` |\n| SQL008 | high | `NOT IN (SELECT ...)` NULL trap |\n| SQL009 | medium | Implicit type cast |\n| SQL010 | low | Many `OR`s (use `IN`\u002F`UNION`) |\n| SQL011 | medium | `COUNT(DISTINCT)` at scale (use HLL) |\n| SQL012 | low | `LIMIT` without `ORDER BY` |\n| SQL013 | medium | Scalar subquery in `SELECT` |\n| SQL014 | medium | 5+ JOINs broadcast\u002Fspill risk |\n| SQL015 | high | Fact table, no partition filter |\n| SQL017 | low | String concat in `SELECT` |\n| SQL018 | medium | Window `OVER ()` no `PARTITION` |\n| SQL020 | critical | `DELETE`\u002F`UPDATE` without `WHERE` |\n| SQL021 | low | `SELECT *` in `EXISTS`\u002F`IN` |\n| SQL022 | medium | `UNION` vs `UNION ALL` |\n\nRun the test suite to verify each rule fires on real SQL:\n\n```bash\ncd scripts && node test.js   # 26 tests, zero dependencies\n```\n\n## Limitations\n\n- This is a **static** analyzer. It finds anti-patterns in the *text* of SQL; it does not read query plans, row counts, or billing. A flagged query on a 100-row table is cheap; the same query on a billion-row table is the problem the rule exists to prevent.\n- The fact-table heuristic (SQL015) keys off table *names* (`*_events`, `*_log`) and is advisory, not definitive.\n- The intended analyzer reads SQL text rather than executing it; review the pinned engine\u002Fdependencies before running and keep private SQL local unless external processing is authorized.\n","# sql-sentinel\n\n## Overview\n\nA static-analysis skill that audits SQL for the cost & performance anti-patterns that dominate warehouse bills — `SELECT *`, full-table scans, non-sargable predicates, Cartesian joins, the `NOT IN` NULL trap, and 15 more. It scores warehouse query health 0-100 (A-F) and outputs a prioritized cost-reduction plan, each finding with a `why`, a concrete `fix`, and an estimated savings.\n\nBuilt for analytics engineers (dbt, Looker), data platform teams running FinOps \u002F \"reduce cloud spend\" initiatives, and anyone reviewing a SQL pull request before it hits production. Works across BigQuery, Snowflake, Redshift, and Postgres. Zero dependencies, MIT licensed.\n\nThe executable engine and full rule set live in the source repository: https:\u002F\u002Fgithub.com\u002Ftakeaseatventure\u002Fsql-sentinel. Treat that repository as third-party executable code.\n\n## When to Use This Skill\n\n- A user writes or reviews a query for BigQuery, Snowflake, Redshift, Postgres, or Spark SQL.\n- A user asks \"why is this query so slow?\" or \"why is my warehouse bill so high?\"\n- A user is about to promote a dashboard query or dbt model to production.\n- A data engineer wants a second pair of eyes before a code review or a cost-optimization sweep.\n- A team is running a \"reduce cloud spend\" or FinOps initiative.\n\n## How It Works\n\nThe engine splits a SQL script into statements (honoring quotes and comments), runs 20 rules over each statement, scores health 0-100 weighted by severity (critical 25, high 12, medium 5, low 1), and returns a prioritized cost-reduction plan.\n\n### Step 1: Run the audit\n\nInstall or clone the source repository only after choosing a reviewed commit, tag, or release to trust. Do not run code from a mutable default branch just because this skill links to it:\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Ftakeaseatventure\u002Fsql-sentinel.git\ncd sql-sentinel\ngit checkout \u003Creviewed-commit-or-tag>\nnode scripts\u002Fsql-sentinel.js path\u002Fto\u002Fquery.sql\n```\n\nOr programmatically:\n\n```javascript\nconst { auditSql } = require('.\u002Fscripts\u002Fsql-sentinel');\nconst report = auditSql(yourSqlString, { dialect: 'bigquery' });\nconsole.log(report.healthScore);      \u002F\u002F 0-100\nconsole.log(report.grade);            \u002F\u002F 'A' | 'B' | 'C' | 'D' | 'E' | 'F'\nconsole.log(report.prioritizedPlan);  \u002F\u002F array, worst findings first\n```\n\n### Step 2: Read the prioritized plan\n\nThe output leads with critical findings (Cartesian joins, mass DELETE) and descends to low-severity style issues. Each finding explains *why* it costs money and *how* to fix it.\n\n## Examples\n\n### Example 1: A messy dashboard query\n\n```sql\nSELECT DISTINCT *\nFROM user_events, raw_logs\nWHERE LOWER(event_name) LIKE '%signup%'\n  AND user_id NOT IN (SELECT id FROM deleted_users)\nORDER BY created_at;\n```\n\nIllustrative candidate findings (no engine was run and no score\u002Fcost was measured):\n- CRITICAL: comma-join without a join predicate produces a Cartesian product; actual impact needs plan\u002Fcardinality evidence\n- HIGH: `SELECT *` may read unnecessary columns depending on engine\u002Fplan\n- HIGH: leading-wildcard `LIKE '%signup%'` defeats indexes\n- HIGH: `LOWER(event_name)` defeats indexes (non-sargable)\n- HIGH: `NOT IN (SELECT ...)` — NULL semantics hazard\n- MEDIUM: `SELECT DISTINCT` dedup cost\n- MEDIUM: `ORDER BY` without `LIMIT` sorts the full result\n\n### Example 2: A clean, sargable query\n\n```sql\n-- Illustrative bounded query, not a measured score or proof of no findings\nSELECT id, email, created_at\nFROM users\nWHERE created_at >= TIMESTAMP '2026-01-01'\n  AND created_at \u003C  TIMESTAMP '2026-02-01'\nORDER BY id\nLIMIT 100;\n```\n\nRules are heuristic candidates: expression\u002Ftrigram indexes, partition pruning, small tables and intended CROSS JOIN\u002FUNION semantics can change the verdict. Preserve result equivalence (including NULL\u002Fduplicate behavior), inspect EXPLAIN\u002Fwarehouse dry-run and actual billing before recommending changes. EXPLAIN ANALYZE executes queries and needs separate data\u002Fcost authorization. A health score is not measured savings.\n\n## The 20 rules (ruleset v1.0.0)\n\n| Rule | Severity | Catches |\n|---|---|---|\n| SQL001 | high | `SELECT *` full column scan |\n| SQL002 | critical | No `WHERE` → full table scan |\n| SQL003 | high | `LIKE '%term'` non-sargable |\n| SQL004 | high | Function on column kills index |\n| SQL005 | critical | `CROSS JOIN` \u002F comma-join |\n| SQL006 | medium | `SELECT DISTINCT` dedup cost |\n| SQL007 | medium | `ORDER BY` without `LIMIT` |\n| SQL008 | high | `NOT IN (SELECT ...)` NULL trap |\n| SQL009 | medium | Implicit type cast |\n| SQL010 | low | Many `OR`s (use `IN`\u002F`UNION`) |\n| SQL011 | medium | `COUNT(DISTINCT)` at scale (use HLL) |\n| SQL012 | low | `LIMIT` without `ORDER BY` |\n| SQL013 | medium | Scalar subquery in `SELECT` |\n| SQL014 | medium | 5+ JOINs broadcast\u002Fspill risk |\n| SQL015 | high | Fact table, no partition filter |\n| SQL017 | low | String concat in `SELECT` |\n| SQL018 | medium | Window `OVER ()` no `PARTITION` |\n| SQL020 | critical | `DELETE`\u002F`UPDATE` without `WHERE` |\n| SQL021 | low | `SELECT *` in `EXISTS`\u002F`IN` |\n| SQL022 | medium | `UNION` vs `UNION ALL` |\n\nRun the test suite to verify each rule fires on real SQL:\n\n```bash\ncd scripts && node test.js   # 26 tests, zero dependencies\n```\n\n## Limitations\n\n- This is a **static** analyzer. It finds anti-patterns in the *text* of SQL; it does not read query plans, row counts, or billing. A flagged query on a 100-row table is cheap; the same query on a billion-row table is the problem the rule exists to prevent.\n- The fact-table heuristic (SQL015) keys off table *names* (`*_events`, `*_log`) and is advisory, not definitive.\n- The intended analyzer reads SQL text rather than executing it; review the pinned engine\u002Fdependencies before running and keep private SQL local unless external processing is authorized.\n",{"name":5,"risk":34,"tags":61,"tools":65,"author":69,"plugin":70,"source":39,"license":14,"category":46,"date_added":76,"description":7,"source_repo":77,"source_type":39,"license_source":78},[29,62,49,50,38,63,64,55,48,52],"bigquery","data-warehouse","cost-optimization",[66,67,68,44],"claude","cursor","codex","takeaseat",{"setup":71,"targets":74},{"docs":72,"type":33,"summary":73},"SKILL.md","Clone the upstream analyzer only after pinning or reviewing the exact commit to run.",{"codex":75,"claude":75},"blocked","2026-06-26","takeaseatventure\u002Fsql-sentinel","https:\u002F\u002Fgithub.com\u002Ftakeaseatventure\u002Fsql-sentinel\u002Fblob\u002Fmain\u002FLICENSE","7bf2279c8a65758319526a721d5c4b75683aec9c","skills\u002Fantigravity-sql-sentinel",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-14 14:18:11",{"source":8,"skillName":5,"commands":86,"prompt":96,"zipAvailable":97},[87,90,93],{"tool":88,"command":89},"npx","npx skills add boisenoise\u002Fskills-collections --skill sql-sentinel",{"tool":91,"command":92},"bunx","bunx skills add boisenoise\u002Fskills-collections --skill sql-sentinel",{"tool":94,"command":95},"pnpm","pnpm dlx skills add boisenoise\u002Fskills-collections --skill sql-sentinel","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-sql-sentinel\u002FSKILL.md",true,[],[100,126,151,175,194,217],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":104,"licenseFileUrl":105,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":56},"01M6WNH92BHVFXHGPDZCPJ0AG0","web-access","Use Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[109,110,111,112,113,114,115,46,116,117,118,42,119,120,121,34,122,123,124,125,32],"web","access","web access","official","development","browser","search","Tools","read","fetch","youtube","free tools","platform","free","cover","normal","when",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":104,"licenseFileUrl":130,"sourceHtmlUrl":131,"categories":132,"keywords":136,"publishedAt":56},"01M6WNF9XAYAB2CZDDJBAKNM93","treg","Use the Treg CLI or MCP catalog to discover, price and call authorized external tools with team-scoped credentials and a user-approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FSKILL.md",[133],{"categoryNo":134,"categoryName":135,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[128,137,138,139,112,113,51,116,140,141,142,143,144,145,146,34,147,148,149,150,32],"MCP","cli","github","Budget","credentials","team","catalog","use","call","discover","external tools","with","scoped","blob",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":104,"licenseFileUrl":155,"sourceHtmlUrl":156,"categories":157,"keywords":161,"publishedAt":56},"01M6WN93MEAZ4E41DYAHJRPHQT","recruit","Turn a job description into an evidence-linked candidate shortlist through Treg, with job-related ranking and optional authorized business contact enrichment.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FSKILL.md",[158],{"categoryNo":159,"categoryName":160,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000038","人力资源",[153,139,112,51,162,163,164,165,166,167,34,168,169,170,148,171,172,173,174,150,128,32],"https","email","enrichment","business","evidence","phone","ranking","post","job description","for","person","candidate","reason",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":104,"licenseFileUrl":179,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":56},"01M6WN8R5RXSMHM9B4D9FAFX42","rea-growth","Analyze a company’s public growth and content through Treg, distinguish observations from hypotheses, and produce an evidence-linked playbook within an approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FSKILL.md",[],[183,184,185,139,112,51,186,187,162,188,140,189,190,191,166,34,192,193,150,128,32],"growth","rea","rea 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information.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FSKILL.md",[],[225,226,227,139,112,51,228,229,165,230,231,34,232,233,234,235,236,237,238,150],"signals","lead","lead signals","how","find","ready","as","how to","qualify","people","information","enrich","changes","funding"]