[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-neon-postgres-egress-optimizer":3,"mdc--1ueg04-key":39,"related-org-openai-neon-postgres-egress-optimizer":973,"related-repo-openai-neon-postgres-egress-optimizer":1180},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":28,"repoUrl":29,"updatedAt":30,"license":31,"forks":32,"topics":33,"repo":34,"sourceUrl":37,"mdContent":38},"neon-postgres-egress-optimizer","optimize Neon Postgres network egress costs","Diagnose and fix excessive Postgres egress (network data transfer) in a codebase. Use when a user mentions high database bills, unexpected data transfer costs, network transfer charges, egress spikes, \"why is my Neon bill so high\", \"database costs jumped\", SELECT * optimization, query overfetching, reduce Neon costs, optimize database usage, or wants to reduce data sent from their database to their application. 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Also use when reviewing query\n  patterns for cost efficiency, even if the user doesn't explicitly mention\n  egress or data transfer.\n---\n\n# Postgres Egress Optimizer\n\nGuide the user through diagnosing and fixing application-side query patterns that cause excessive data transfer (egress) from their Postgres database. Most high egress bills come from the application fetching more data than it uses.\n\n## Step 1: Diagnose\n\nIdentify which queries transfer the most data. The primary tool is the `pg_stat_statements` extension.\n\n### Check if pg_stat_statements is available\n\n```sql\nSELECT 1 FROM pg_stat_statements LIMIT 1;\n```\n\nIf this errors, the extension needs to be created:\n\n```sql\nCREATE EXTENSION IF NOT EXISTS pg_stat_statements;\n```\n\nOn Neon, it is available by default but may need this CREATE EXTENSION step.\n\n### Handle empty stats\n\nStats are cleared when a Neon compute scales to zero and restarts. If the stats are empty or the compute recently woke up:\n\n1. Reset the stats to start a clean measurement window: `SELECT pg_stat_statements_reset();`\n2. Let the application run under representative traffic for at least an hour.\n3. Return and run the diagnostic queries below.\n\nIf the user has stats from a production database, use those. If they have no access to production stats, proceed to Step 2 and analyze the codebase directly — code-level patterns are often sufficient to identify the worst offenders.\n\n### Diagnostic queries\n\nRun these to identify the top egress contributors. Focus on queries that return many rows, return wide rows (JSONB, TEXT, BYTEA columns), or are called very frequently.\n\n**Queries returning the most total rows:**\n\n```sql\nSELECT query, calls, rows AS total_rows, rows \u002F calls AS avg_rows_per_call\nFROM pg_stat_statements\nWHERE calls > 0\nORDER BY rows DESC\nLIMIT 10;\n```\n\n**Queries returning the most rows per execution** (poorly scoped SELECTs, missing pagination):\n\n```sql\nSELECT query, calls, rows AS total_rows, rows \u002F calls AS avg_rows_per_call\nFROM pg_stat_statements\nWHERE calls > 0\nORDER BY avg_rows_per_call DESC\nLIMIT 10;\n```\n\n**Most frequently called queries** (candidates for caching):\n\n```sql\nSELECT query, calls, rows AS total_rows, rows \u002F calls AS avg_rows_per_call\nFROM pg_stat_statements\nWHERE calls > 0\nORDER BY calls DESC\nLIMIT 10;\n```\n\n**Longest running queries** (not a direct egress measure, but helps identify problem queries during a spike):\n\n```sql\nSELECT query, calls, rows AS total_rows,\n  round(total_exec_time::numeric, 2) AS total_exec_time_ms\nFROM pg_stat_statements\nWHERE calls > 0\nORDER BY total_exec_time DESC\nLIMIT 10;\n```\n\n### Interpret the results\n\nRank findings by estimated egress impact:\n\n- **High row count + wide rows** = biggest egress. A query returning 1,000 rows where each row includes a 50KB JSONB column transfers ~50MB per call.\n- **Extreme call frequency** on even small queries adds up. A query called 50,000 times\u002Fday returning 10 rows each = 500,000 rows\u002Fday.\n- **Cross-reference with the schema** to identify which columns are wide. Look for JSONB, TEXT, BYTEA, and large VARCHAR columns.\n\n## Step 2: Analyze codebase\n\nFor each query identified in Step 1, or for each database query in the codebase if no stats are available, check:\n\n- Does it select only the columns the response needs?\n- Does it return a bounded number of rows (LIMIT\u002Fpagination)?\n- Is it called frequently enough to benefit from caching?\n- Does it fetch raw data that gets aggregated in application code?\n- Does it use a JOIN that duplicates parent data across child rows?\n\n## Step 3: Fix\n\nApply the appropriate fix for each problem found. Below are the most common egress anti-patterns and how to fix them.\n\n### Unused columns (SELECT \\*)\n\n**Problem:** The query fetches all columns but the application only uses a few. Large columns (JSONB blobs, TEXT fields) get transferred over the wire and discarded.\n\n**Before:**\n\n```sql\nSELECT * FROM products;\n```\n\n**After:**\n\n```sql\nSELECT id, name, price, image_urls FROM products;\n```\n\n### Missing pagination\n\n**Problem:** A list endpoint returns all rows with no LIMIT. This is an unbounded egress risk — every new row in the table increases data transfer on every request. Flag this regardless of current table size.\n\nThis is easy to miss because the application may work fine with small datasets. But at scale, an unpaginated endpoint returning 10,000 rows with even moderate column widths can transfer hundreds of megabytes per day.\n\n**Before:**\n\n```sql\nSELECT id, name, price FROM products;\n```\n\n**After:**\n\n```sql\nSELECT id, name, price FROM products\nORDER BY id\nLIMIT 50 OFFSET 0;\n```\n\nWhen adding pagination, check whether the consuming client already supports paginated responses. If not, pick sensible defaults and document the pagination parameters in the API.\n\n### High-frequency queries on static data\n\n**Problem:** A query is called thousands of times per day but returns data that rarely changes. Every call transfers the same rows from the database. This pattern is only visible from `pg_stat_statements` — the code itself looks normal.\n\nLook for queries with extremely high call counts relative to other queries. Common examples: configuration tables, category lists, feature flags, user role definitions.\n\n**Fix:** Add a caching layer between the application and the database so it avoids hitting the database on every request.\n\n### Application-side aggregation\n\n**Problem:** The application fetches all rows from a table and then computes aggregates (averages, counts, sums, groupings) in application code. The full dataset transfers over the wire even though the result is a small summary.\n\n**Fix:** Push the aggregation into SQL.\n\n**Before:** The application fetches entire tables and aggregates in code with loops or `.reduce()`.\n\n**After:**\n\n```sql\nSELECT p.category_id,\n       AVG(r.rating) AS avg_rating,\n       COUNT(r.id) AS review_count\nFROM reviews r\nINNER JOIN products p ON r.product_id = p.id\nGROUP BY p.category_id;\n```\n\n### JOIN duplication\n\n**Problem:** A JOIN between a wide parent table and a child table duplicates all parent columns across every child row. If a product has 200 reviews and the product row includes a 50KB JSONB column, the join sends that 50KB x 200 = ~10MB for a single request.\n\nThis is distinct from the SELECT \\* problem. Even if you select only needed columns, a JOIN still repeats the parent data for every child row. The fix is structural: avoid the join entirely.\n\n**Before:**\n\n```sql\nSELECT * FROM products\nLEFT JOIN reviews ON reviews.product_id = products.id\nWHERE products.id = 1;\n```\n\n**After (two separate queries):**\n\n```sql\nSELECT id, name, price, description, image_urls FROM products WHERE id = 1;\nSELECT id, user_name, rating, body FROM reviews WHERE product_id = 1;\n```\n\nTwo queries instead of one JOIN. The product data is fetched once. The reviews are fetched once. No duplication.\n\n## Step 4: Verify\n\nAfter applying fixes:\n\n1. **Run existing tests** to confirm nothing broke.\n2. **Check the responses** — make sure the API still returns the same data shape. Column selection and pagination changes can break clients that depend on specific fields or full result sets.\n3. **Measure the improvement** — if pg_stat_statements data is available, reset it (`SELECT pg_stat_statements_reset();`), let traffic run, then re-run the diagnostic queries to compare before and after.\n\n## Further reading\n\n- https:\u002F\u002Fneon.com\u002Fdocs\u002Fintroduction\u002Fnetwork-transfer.md\n- https:\u002F\u002Fneon.com\u002Fdocs\u002Fintroduction\u002Fcost-optimization.md\n",{"data":40,"body":41},{"name":4,"description":6},{"type":42,"children":43},"root",[44,53,59,66,80,87,108,113,127,132,138,143,169,174,180,185,194,245,255,298,308,351,361,414,420,425,459,465,470,498,504,509,515,525,533,547,555,569,575,584,589,596,610,617,648,653,659,675,680,690,696,705,714,731,738,793,799,808,813,820,851,859,882,887,893,898,938,944,967],{"type":45,"tag":46,"props":47,"children":49},"element","h1",{"id":48},"postgres-egress-optimizer",[50],{"type":51,"value":52},"text","Postgres Egress Optimizer",{"type":45,"tag":54,"props":55,"children":56},"p",{},[57],{"type":51,"value":58},"Guide the user through diagnosing and fixing application-side query patterns that cause excessive data transfer (egress) from their Postgres database. 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This is an unbounded egress risk — every new row in the table increases data transfer on every request. Flag this regardless of current table size.",{"type":45,"tag":54,"props":585,"children":586},{},[587],{"type":51,"value":588},"This is easy to miss because the application may work fine with small datasets. But at scale, an unpaginated endpoint returning 10,000 rows with even moderate column widths can transfer hundreds of megabytes per day.",{"type":45,"tag":54,"props":590,"children":591},{},[592],{"type":45,"tag":189,"props":593,"children":594},{},[595],{"type":51,"value":532},{"type":45,"tag":88,"props":597,"children":599},{"className":90,"code":598,"language":92,"meta":93,"style":93},"SELECT id, name, price FROM products;\n",[600],{"type":45,"tag":72,"props":601,"children":602},{"__ignoreMap":93},[603],{"type":45,"tag":99,"props":604,"children":605},{"class":101,"line":102},[606],{"type":45,"tag":99,"props":607,"children":608},{},[609],{"type":51,"value":598},{"type":45,"tag":54,"props":611,"children":612},{},[613],{"type":45,"tag":189,"props":614,"children":615},{},[616],{"type":51,"value":554},{"type":45,"tag":88,"props":618,"children":620},{"className":90,"code":619,"language":92,"meta":93,"style":93},"SELECT id, name, price FROM products\nORDER BY id\nLIMIT 50 OFFSET 0;\n",[621],{"type":45,"tag":72,"props":622,"children":623},{"__ignoreMap":93},[624,632,640],{"type":45,"tag":99,"props":625,"children":626},{"class":101,"line":102},[627],{"type":45,"tag":99,"props":628,"children":629},{},[630],{"type":51,"value":631},"SELECT id, name, price FROM products\n",{"type":45,"tag":99,"props":633,"children":634},{"class":101,"line":211},[635],{"type":45,"tag":99,"props":636,"children":637},{},[638],{"type":51,"value":639},"ORDER BY id\n",{"type":45,"tag":99,"props":641,"children":642},{"class":101,"line":220},[643],{"type":45,"tag":99,"props":644,"children":645},{},[646],{"type":51,"value":647},"LIMIT 50 OFFSET 0;\n",{"type":45,"tag":54,"props":649,"children":650},{},[651],{"type":51,"value":652},"When adding pagination, check whether the consuming client already supports paginated responses. If not, pick sensible defaults and document the pagination parameters in the API.",{"type":45,"tag":81,"props":654,"children":656},{"id":655},"high-frequency-queries-on-static-data",[657],{"type":51,"value":658},"High-frequency queries on static data",{"type":45,"tag":54,"props":660,"children":661},{},[662,666,668,673],{"type":45,"tag":189,"props":663,"children":664},{},[665],{"type":51,"value":522},{"type":51,"value":667}," A query is called thousands of times per day but returns data that rarely changes. Every call transfers the same rows from the database. This pattern is only visible from ",{"type":45,"tag":72,"props":669,"children":671},{"className":670},[],[672],{"type":51,"value":77},{"type":51,"value":674}," — the code itself looks normal.",{"type":45,"tag":54,"props":676,"children":677},{},[678],{"type":51,"value":679},"Look for queries with extremely high call counts relative to other queries. Common examples: configuration tables, category lists, feature flags, user role definitions.",{"type":45,"tag":54,"props":681,"children":682},{},[683,688],{"type":45,"tag":189,"props":684,"children":685},{},[686],{"type":51,"value":687},"Fix:",{"type":51,"value":689}," Add a caching layer between the application and the database so it avoids hitting the database on every request.",{"type":45,"tag":81,"props":691,"children":693},{"id":692},"application-side-aggregation",[694],{"type":51,"value":695},"Application-side aggregation",{"type":45,"tag":54,"props":697,"children":698},{},[699,703],{"type":45,"tag":189,"props":700,"children":701},{},[702],{"type":51,"value":522},{"type":51,"value":704}," The application fetches all rows from a table and then computes aggregates (averages, counts, sums, groupings) in application code. The full dataset transfers over the wire even though the result is a small summary.",{"type":45,"tag":54,"props":706,"children":707},{},[708,712],{"type":45,"tag":189,"props":709,"children":710},{},[711],{"type":51,"value":687},{"type":51,"value":713}," Push the aggregation into SQL.",{"type":45,"tag":54,"props":715,"children":716},{},[717,721,723,729],{"type":45,"tag":189,"props":718,"children":719},{},[720],{"type":51,"value":532},{"type":51,"value":722}," The application fetches entire tables and aggregates in code with loops or ",{"type":45,"tag":72,"props":724,"children":726},{"className":725},[],[727],{"type":51,"value":728},".reduce()",{"type":51,"value":730},".",{"type":45,"tag":54,"props":732,"children":733},{},[734],{"type":45,"tag":189,"props":735,"children":736},{},[737],{"type":51,"value":554},{"type":45,"tag":88,"props":739,"children":741},{"className":90,"code":740,"language":92,"meta":93,"style":93},"SELECT p.category_id,\n       AVG(r.rating) AS avg_rating,\n       COUNT(r.id) AS review_count\nFROM reviews r\nINNER JOIN products p ON r.product_id = p.id\nGROUP BY p.category_id;\n",[742],{"type":45,"tag":72,"props":743,"children":744},{"__ignoreMap":93},[745,753,761,769,777,785],{"type":45,"tag":99,"props":746,"children":747},{"class":101,"line":102},[748],{"type":45,"tag":99,"props":749,"children":750},{},[751],{"type":51,"value":752},"SELECT p.category_id,\n",{"type":45,"tag":99,"props":754,"children":755},{"class":101,"line":211},[756],{"type":45,"tag":99,"props":757,"children":758},{},[759],{"type":51,"value":760},"       AVG(r.rating) AS avg_rating,\n",{"type":45,"tag":99,"props":762,"children":763},{"class":101,"line":220},[764],{"type":45,"tag":99,"props":765,"children":766},{},[767],{"type":51,"value":768},"       COUNT(r.id) AS review_count\n",{"type":45,"tag":99,"props":770,"children":771},{"class":101,"line":229},[772],{"type":45,"tag":99,"props":773,"children":774},{},[775],{"type":51,"value":776},"FROM reviews r\n",{"type":45,"tag":99,"props":778,"children":779},{"class":101,"line":238},[780],{"type":45,"tag":99,"props":781,"children":782},{},[783],{"type":51,"value":784},"INNER JOIN products p ON r.product_id = p.id\n",{"type":45,"tag":99,"props":786,"children":787},{"class":101,"line":408},[788],{"type":45,"tag":99,"props":789,"children":790},{},[791],{"type":51,"value":792},"GROUP BY p.category_id;\n",{"type":45,"tag":81,"props":794,"children":796},{"id":795},"join-duplication",[797],{"type":51,"value":798},"JOIN duplication",{"type":45,"tag":54,"props":800,"children":801},{},[802,806],{"type":45,"tag":189,"props":803,"children":804},{},[805],{"type":51,"value":522},{"type":51,"value":807}," A JOIN between a wide parent table and a child table duplicates all parent columns across every child row. If a product has 200 reviews and the product row includes a 50KB JSONB column, the join sends that 50KB x 200 = ~10MB for a single request.",{"type":45,"tag":54,"props":809,"children":810},{},[811],{"type":51,"value":812},"This is distinct from the SELECT * problem. Even if you select only needed columns, a JOIN still repeats the parent data for every child row. The fix is structural: avoid the join entirely.",{"type":45,"tag":54,"props":814,"children":815},{},[816],{"type":45,"tag":189,"props":817,"children":818},{},[819],{"type":51,"value":532},{"type":45,"tag":88,"props":821,"children":823},{"className":90,"code":822,"language":92,"meta":93,"style":93},"SELECT * FROM products\nLEFT JOIN reviews ON reviews.product_id = products.id\nWHERE products.id = 1;\n",[824],{"type":45,"tag":72,"props":825,"children":826},{"__ignoreMap":93},[827,835,843],{"type":45,"tag":99,"props":828,"children":829},{"class":101,"line":102},[830],{"type":45,"tag":99,"props":831,"children":832},{},[833],{"type":51,"value":834},"SELECT * FROM products\n",{"type":45,"tag":99,"props":836,"children":837},{"class":101,"line":211},[838],{"type":45,"tag":99,"props":839,"children":840},{},[841],{"type":51,"value":842},"LEFT JOIN reviews ON reviews.product_id = products.id\n",{"type":45,"tag":99,"props":844,"children":845},{"class":101,"line":220},[846],{"type":45,"tag":99,"props":847,"children":848},{},[849],{"type":51,"value":850},"WHERE products.id = 1;\n",{"type":45,"tag":54,"props":852,"children":853},{},[854],{"type":45,"tag":189,"props":855,"children":856},{},[857],{"type":51,"value":858},"After (two separate queries):",{"type":45,"tag":88,"props":860,"children":862},{"className":90,"code":861,"language":92,"meta":93,"style":93},"SELECT id, name, price, description, image_urls FROM products WHERE id = 1;\nSELECT id, user_name, rating, body FROM reviews WHERE product_id = 1;\n",[863],{"type":45,"tag":72,"props":864,"children":865},{"__ignoreMap":93},[866,874],{"type":45,"tag":99,"props":867,"children":868},{"class":101,"line":102},[869],{"type":45,"tag":99,"props":870,"children":871},{},[872],{"type":51,"value":873},"SELECT id, name, price, description, image_urls FROM products WHERE id = 1;\n",{"type":45,"tag":99,"props":875,"children":876},{"class":101,"line":211},[877],{"type":45,"tag":99,"props":878,"children":879},{},[880],{"type":51,"value":881},"SELECT id, user_name, rating, body FROM reviews WHERE product_id = 1;\n",{"type":45,"tag":54,"props":883,"children":884},{},[885],{"type":51,"value":886},"Two queries instead of one JOIN. The product data is fetched once. The reviews are fetched once. No duplication.",{"type":45,"tag":60,"props":888,"children":890},{"id":889},"step-4-verify",[891],{"type":51,"value":892},"Step 4: Verify",{"type":45,"tag":54,"props":894,"children":895},{},[896],{"type":51,"value":897},"After applying fixes:",{"type":45,"tag":144,"props":899,"children":900},{},[901,911,921],{"type":45,"tag":148,"props":902,"children":903},{},[904,909],{"type":45,"tag":189,"props":905,"children":906},{},[907],{"type":51,"value":908},"Run existing tests",{"type":51,"value":910}," to confirm nothing broke.",{"type":45,"tag":148,"props":912,"children":913},{},[914,919],{"type":45,"tag":189,"props":915,"children":916},{},[917],{"type":51,"value":918},"Check the responses",{"type":51,"value":920}," — make sure the API still returns the same data shape. Column selection and pagination changes can break clients that depend on specific fields or full result sets.",{"type":45,"tag":148,"props":922,"children":923},{},[924,929,931,936],{"type":45,"tag":189,"props":925,"children":926},{},[927],{"type":51,"value":928},"Measure the improvement",{"type":51,"value":930}," — if pg_stat_statements data is available, reset it (",{"type":45,"tag":72,"props":932,"children":934},{"className":933},[],[935],{"type":51,"value":158},{"type":51,"value":937},"), let traffic run, then re-run the diagnostic queries to compare before and after.",{"type":45,"tag":60,"props":939,"children":941},{"id":940},"further-reading",[942],{"type":51,"value":943},"Further reading",{"type":45,"tag":426,"props":945,"children":946},{},[947,958],{"type":45,"tag":148,"props":948,"children":949},{},[950],{"type":45,"tag":951,"props":952,"children":956},"a",{"href":953,"rel":954},"https:\u002F\u002Fneon.com\u002Fdocs\u002Fintroduction\u002Fnetwork-transfer.md",[955],"nofollow",[957],{"type":51,"value":953},{"type":45,"tag":148,"props":959,"children":960},{},[961],{"type":45,"tag":951,"props":962,"children":965},{"href":963,"rel":964},"https:\u002F\u002Fneon.com\u002Fdocs\u002Fintroduction\u002Fcost-optimization.md",[955],[966],{"type":51,"value":963},{"type":45,"tag":968,"props":969,"children":970},"style",{},[971],{"type":51,"value":972},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":974,"total":1179},[975,996,1019,1036,1052,1071,1090,1106,1122,1136,1148,1163],{"slug":976,"name":976,"fn":977,"description":978,"org":979,"tags":980,"stars":993,"repoUrl":994,"updatedAt":995},"prior-auth-packet-builder","build healthcare prior authorization packets","Build a concise prior authorization packet from local case files and payer policy docs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[981,984,987,990],{"name":982,"slug":983,"type":15},"Documents","documents",{"name":985,"slug":986,"type":15},"Healthcare","healthcare",{"name":988,"slug":989,"type":15},"Insurance","insurance",{"name":991,"slug":992,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":997,"name":997,"fn":998,"description":999,"org":1000,"tags":1001,"stars":1016,"repoUrl":1017,"updatedAt":1018},"aspnet-core","build ASP.NET Core web applications","Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1002,1005,1007,1010,1013],{"name":1003,"slug":1004,"type":15},".NET","dotnet",{"name":1006,"slug":997,"type":15},"ASP.NET Core",{"name":1008,"slug":1009,"type":15},"Blazor","blazor",{"name":1011,"slug":1012,"type":15},"C#","csharp",{"name":1014,"slug":1015,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":1020,"name":1020,"fn":1021,"description":1022,"org":1023,"tags":1024,"stars":1016,"repoUrl":1017,"updatedAt":1035},"chatgpt-apps","build ChatGPT Apps SDK applications","Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1025,1028,1031,1034],{"name":1026,"slug":1027,"type":15},"Apps SDK","apps-sdk",{"name":1029,"slug":1030,"type":15},"ChatGPT","chatgpt",{"name":1032,"slug":1033,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":1037,"name":1037,"fn":1038,"description":1039,"org":1040,"tags":1041,"stars":1016,"repoUrl":1017,"updatedAt":1051},"cli-creator","build CLIs from API docs","Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read\u002Fwrite commands, return stable JSON, manage auth, and pair with a companion skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1042,1045,1048],{"name":1043,"slug":1044,"type":15},"API Development","api-development",{"name":1046,"slug":1047,"type":15},"CLI","cli",{"name":1049,"slug":1050,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":1053,"name":1053,"fn":1054,"description":1055,"org":1056,"tags":1057,"stars":1016,"repoUrl":1017,"updatedAt":1070},"cloudflare-deploy","deploy projects to Cloudflare","Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1058,1061,1064,1067],{"name":1059,"slug":1060,"type":15},"Cloudflare","cloudflare",{"name":1062,"slug":1063,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":1065,"slug":1066,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":1068,"slug":1069,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":1072,"name":1072,"fn":1073,"description":1074,"org":1075,"tags":1076,"stars":1016,"repoUrl":1017,"updatedAt":1089},"define-goal","define and set measurable project goals","Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1077,1080,1083,1086],{"name":1078,"slug":1079,"type":15},"Productivity","productivity",{"name":1081,"slug":1082,"type":15},"Project Management","project-management",{"name":1084,"slug":1085,"type":15},"Strategy","strategy",{"name":1087,"slug":1088,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":1091,"name":1091,"fn":1092,"description":1093,"org":1094,"tags":1095,"stars":1016,"repoUrl":1017,"updatedAt":1105},"figma","translate Figma designs into code","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1096,1099,1101,1104],{"name":1097,"slug":1098,"type":15},"Design","design",{"name":1100,"slug":1091,"type":15},"Figma",{"name":1102,"slug":1103,"type":15},"Frontend","frontend",{"name":1032,"slug":1033,"type":15},"2026-04-12T05:06:47.939943",{"slug":1107,"name":1107,"fn":1108,"description":1109,"org":1110,"tags":1111,"stars":1016,"repoUrl":1017,"updatedAt":1121},"figma-code-connect-components","connect Figma designs to code components","Connects Figma design components to code components using Code Connect mapping tools. Use when user says \"code connect\", \"connect this component to code\", \"map this component\", \"link component to code\", \"create code connect mapping\", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1112,1113,1116,1117,1118],{"name":1097,"slug":1098,"type":15},{"name":1114,"slug":1115,"type":15},"Design System","design-system",{"name":1100,"slug":1091,"type":15},{"name":1102,"slug":1103,"type":15},{"name":1119,"slug":1120,"type":15},"UI Components","ui-components","2026-05-10T05:59:52.971881",{"slug":1123,"name":1123,"fn":1124,"description":1125,"org":1126,"tags":1127,"stars":1016,"repoUrl":1017,"updatedAt":1135},"figma-create-design-system-rules","generate design system rules from Figma","Generates custom design system rules for the user's codebase. Use when user says \"create design system rules\", \"generate rules for my project\", \"set up design rules\", \"customize design system guidelines\", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1128,1129,1130,1133,1134],{"name":1097,"slug":1098,"type":15},{"name":1114,"slug":1115,"type":15},{"name":1131,"slug":1132,"type":15},"Documentation","documentation",{"name":1100,"slug":1091,"type":15},{"name":1102,"slug":1103,"type":15},"2026-05-16T06:07:47.821474",{"slug":1137,"name":1137,"fn":1138,"description":1139,"org":1140,"tags":1141,"stars":1016,"repoUrl":1017,"updatedAt":1147},"figma-implement-design","translate Figma designs into application code","Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions \"implement design\", \"generate code\", \"implement component\", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1142,1143,1144,1145,1146],{"name":1097,"slug":1098,"type":15},{"name":1100,"slug":1091,"type":15},{"name":1102,"slug":1103,"type":15},{"name":1119,"slug":1120,"type":15},{"name":1014,"slug":1015,"type":15},"2026-05-16T06:07:40.583615",{"slug":1149,"name":1149,"fn":1150,"description":1151,"org":1152,"tags":1153,"stars":1016,"repoUrl":1017,"updatedAt":1162},"hatch-pet","create animated pets for Codex","Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1154,1157,1158,1161],{"name":1155,"slug":1156,"type":15},"Animation","animation",{"name":1049,"slug":1050,"type":15},{"name":1159,"slug":1160,"type":15},"Creative","creative",{"name":1097,"slug":1098,"type":15},"2026-05-02T05:31:48.48485",{"slug":1164,"name":1164,"fn":1165,"description":1166,"org":1167,"tags":1168,"stars":1016,"repoUrl":1017,"updatedAt":1178},"imagegen","generate and edit raster images","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1169,1170,1171,1174,1177],{"name":1159,"slug":1160,"type":15},{"name":1097,"slug":1098,"type":15},{"name":1172,"slug":1173,"type":15},"Image Generation","image-generation",{"name":1175,"slug":1176,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675,{"items":1181,"total":1289},[1182,1199,1215,1227,1245,1263,1279],{"slug":1183,"name":1183,"fn":1184,"description":1185,"org":1186,"tags":1187,"stars":28,"repoUrl":29,"updatedAt":1198},"accessibility-and-inclusive-visualization","make data visualizations accessible","Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1188,1191,1194,1197],{"name":1189,"slug":1190,"type":15},"Accessibility","accessibility",{"name":1192,"slug":1193,"type":15},"Charts","charts",{"name":1195,"slug":1196,"type":15},"Data Visualization","data-visualization",{"name":1097,"slug":1098,"type":15},"2026-06-30T19:00:57.102",{"slug":1200,"name":1200,"fn":1201,"description":1202,"org":1203,"tags":1204,"stars":28,"repoUrl":29,"updatedAt":1214},"agent-browser","automate browser interactions for agents","Browser automation CLI for AI agents. Use when the user needs to interact with websites, verify dev server output, test web apps, navigate pages, fill forms, click buttons, take screenshots, extract data, or automate any browser task. Also triggers when a dev server starts so you can verify it visually.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1205,1208,1211],{"name":1206,"slug":1207,"type":15},"Agents","agents",{"name":1209,"slug":1210,"type":15},"Browser Automation","browser-automation",{"name":1212,"slug":1213,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":1216,"name":1216,"fn":1217,"description":1218,"org":1219,"tags":1220,"stars":28,"repoUrl":29,"updatedAt":1226},"agent-browser-verify","verify dev server output with automated browser","Automated browser verification for dev servers. Triggers when a dev server starts to run a visual gut-check with agent-browser — verifies the page loads, checks for console errors, validates key UI elements, and reports pass\u002Ffail before continuing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1221,1222,1225],{"name":1209,"slug":1210,"type":15},{"name":1223,"slug":1224,"type":15},"Local Development","local-development",{"name":1212,"slug":1213,"type":15},"2026-04-06T18:41:17.526867",{"slug":1228,"name":1228,"fn":1229,"description":1230,"org":1231,"tags":1232,"stars":28,"repoUrl":29,"updatedAt":1244},"agents-sdk","build AI agents on Cloudflare Workers","Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1233,1234,1235,1238,1241],{"name":1206,"slug":1207,"type":15},{"name":1065,"slug":1066,"type":15},{"name":1236,"slug":1237,"type":15},"SDK","sdk",{"name":1239,"slug":1240,"type":15},"Serverless","serverless",{"name":1242,"slug":1243,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":1246,"name":1246,"fn":1247,"description":1248,"org":1249,"tags":1250,"stars":28,"repoUrl":29,"updatedAt":1262},"ai-elements","build chat UIs with AI Elements","AI Elements component library guidance — pre-built React components for AI interfaces built on shadcn\u002Fui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1251,1252,1255,1258,1259],{"name":1102,"slug":1103,"type":15},{"name":1253,"slug":1254,"type":15},"React","react",{"name":1256,"slug":1257,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":1119,"slug":1120,"type":15},{"name":1260,"slug":1261,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":1264,"name":1264,"fn":1265,"description":1266,"org":1267,"tags":1268,"stars":28,"repoUrl":29,"updatedAt":1278},"ai-gateway","configure Vercel AI Gateway","Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1269,1272,1273,1276,1277],{"name":1270,"slug":1271,"type":15},"AI Infrastructure","ai-infrastructure",{"name":20,"slug":21,"type":15},{"name":1274,"slug":1275,"type":15},"LLM","llm",{"name":13,"slug":14,"type":15},{"name":1260,"slug":1261,"type":15},"2026-04-06T18:40:44.377464",{"slug":1280,"name":1280,"fn":1281,"description":1282,"org":1283,"tags":1284,"stars":28,"repoUrl":29,"updatedAt":1288},"ai-generation-persistence","implement persistence patterns for AI generations","AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1285,1286,1287],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1274,"slug":1275,"type":15},"2026-04-06T18:41:08.513425",600]