[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-creating-data-lake-table":3,"mdc-3dtepj-key":35,"related-repo-aws-creating-data-lake-table":2113,"related-org-aws-creating-data-lake-table":2219},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":24,"repoUrl":25,"updatedAt":26,"license":27,"forks":28,"topics":29,"repo":30,"sourceUrl":33,"mdContent":34},"creating-data-lake-table","create managed Iceberg tables","Create managed Iceberg tables using Amazon S3 Tables (s3tables API namespace) with automatic compaction and snapshot management. Sets up table bucket, namespace, table, schema, Glue catalog registration, partitioning, IAM access control. Triggers on: create table, data lake table, analytics table, structured data storage, S3 Tables, Iceberg, Athena table, partitioning strategy, access permissions. 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Sets up table bucket, namespace, table,\n  schema, Glue catalog registration, partitioning, IAM access control. Triggers on:\n  create table, data lake table, analytics table, structured data storage, S3 Tables,\n  Iceberg, Athena table, partitioning strategy, access permissions. Do NOT use  for:\n  importing files (use ingesting-into-data-lake), vector storage (use storing-and-querying-vectors),\n  querying existing tables (use querying-data-lake), or locating existing table (use\n  finding-data-lake-assets).\nversion: 1\nargument-hint: '[table-description|schema-spec]'\n---\n\n# Create Data Lake Tables with Amazon S3 Tables\n\n## Overview\n\nAmazon S3 Tables provides managed Iceberg tables with automatic compaction and snapshot management. Queryable via Athena and Iceberg-compatible engines.\n\n## Common Tasks\n\nYou MUST use AWS MCP server tools when connected, they provide command validation, sandboxed execution, and audit logging. Fall back to AWS CLI if MCP unavailable.\n\n## Decision Guide\n\n**Before creating, You MUST check what exists:**\n\nYou MUST run `aws glue get-tables --database-name \u003CNAME>` when user mentions a database.\n\n| What you find | Action |\n|---------------|--------|\n| Fuzzy database name (\"our analytics db\") | You MUST STOP. Delegate to `finding-data-lake-assets` to resolve. |\n| Non-S3-Tables table with matching name | You MUST STOP. Delegate to `finding-data-lake-assets`. You MUST NOT create until user confirms. |\n| Existing S3 Tables table with matching name | You MUST check schema match. Reuse if compatible, recreate only if user confirms. |\n| No matching tables | Proceed with creation (Steps 1-8). |\n| User explicitly requests new S3 Tables table | Skip checks, proceed with creation. |\n\n**Creation paths:**\n\n- **Existing data in S3**: Create empty table (Steps 1-8), then use `ingesting-into-data-lake` skill.\n- **Glue ETL pipeline**: Read `references\u002Ftable-creation-glue-etl.md` first, then Steps 1-6.\n- **Lake Formation access control**: Search AWS docs for `\"S3 Tables integration with Lake Formation\"`.\n\n### 1. Verify Dependencies\n\n**Constraints:**\n\n- You MUST check whether AWS MCP server tools or AWS CLI are available and inform user if missing\n- You MUST confirm target AWS region and verify credentials with `aws sts get-caller-identity`\n\n### 2. Understand the Schema\n\n- **Explicit schema**: Validate Iceberg types.\n- **Loose description**: Ask columns, types, grain. Propose and confirm.\n- **Existing S3 data**: Infer schema from file headers only. Create empty table first, then use `ingesting-into-data-lake` skill.\n\n**Constraints:**\n\n- You MUST read `references\u002Fbest-practices.md` for Iceberg type mapping, partitions, and naming.\n- You MUST ask for all required parameters upfront: table name, columns, types, partition strategy. For schema evolution, see `references\u002Fathena-ddl-path.md`.\n- You MUST use all lowercase names -- Glue rejects mixed case with `GENERIC_INTERNAL_ERROR`. Namespace and table names MUST NOT contain hyphens.\n- You SHOULD suggest partition columns based on access patterns.\n\n### 3. Create Table Bucket\n\nNames: 3-63 chars, lowercase, numbers, hyphens.\n\n```bash\naws s3tables create-table-bucket --name \u003CBUCKET_NAME> --region \u003CREGION>\n```\n\nCapture `table-bucket-arn`. Encryption (SSE-S3 default, SSE-KMS) and storage class (STANDARD, INTELLIGENT_TIERING) set at creation. See `references\u002Fbest-practices.md`.\n\n**Constraints:**\n\n- You MUST check existing buckets with `aws s3tables list-table-buckets` and ask user to select or create new.\n- If using SSE-KMS, KMS key policy MUST allow S3 Tables maintenance service principal to read data. Search AWS docs for `\"S3 Tables KMS key policy\"` for required policy.\n- If bucket creation fails, see `references\u002Fbest-practices.md` for common errors.\n\n### 4. Create Namespace\n\n```bash\naws s3tables create-namespace --table-bucket-arn \u003CARN> --namespace \u003CNAMESPACE>\n```\n\n**Constraints:**\n\n- You MUST list existing namespaces first and suggest reusing if relevant\n- You MUST use lowercase names with no hyphens\n\n### 5. Create Glue Data Catalog Integration\n\nCheck if `s3tablescatalog` exists (create once per region per account):\n\n```bash\naws glue get-catalog --catalog-id s3tablescatalog\n```\n\nIf not found, create (requires `glue:CreateCatalog`, `glue:passConnection`):\n\n```bash\naws glue create-catalog --name \"s3tablescatalog\" --catalog-input '{\n  \"FederatedCatalog\": {\n    \"Identifier\": \"arn:aws:s3tables:\u003CREGION>:\u003CACCOUNT_ID>:bucket\u002F*\",\n    \"ConnectionName\": \"aws:s3tables\"\n  },\n  \"CreateDatabaseDefaultPermissions\": [{\"Principal\": {\"DataLakePrincipalIdentifier\": \"IAM_ALLOWED_PRINCIPALS\"}, \"Permissions\": [\"ALL\"]}],\n  \"CreateTableDefaultPermissions\": [{\"Principal\": {\"DataLakePrincipalIdentifier\": \"IAM_ALLOWED_PRINCIPALS\"}, \"Permissions\": [\"ALL\"]}],\n  \"AllowFullTableExternalDataAccess\": \"True\"\n}'\n```\n\nVerify with `aws glue get-catalogs --parent-catalog-id s3tablescatalog`.\n\n### 6. Configure Access Control\n\nS3 Tables uses `s3tables:*` IAM namespace (not `s3:*`).\n\n**Querying principal permissions (bucket policy):**\n\n- `s3tables:GetTableBucket`, `s3tables:GetNamespace`, `s3tables:GetTable`, `s3tables:GetTableMetadataLocation`, `s3tables:GetTableData`\n\n**Querying principal permissions (IAM policy):**\n\n- `glue:GetCatalog`, `glue:GetDatabase`, `glue:GetTable`\n\nYou MUST scope to correct ARN patterns. You MUST read `references\u002Faccess-control.md` for exact resource ARNs.\n\n**Constraints:**\n\n- You MUST ask user for querying principal ARN\n- You MUST NOT grant broader permissions than necessary\n- You MUST NOT create IAM roles automatically, verify existing and guide user\n\n### 7. Create the Table\n\n| Context | Path |\n|---------|------|\n| Default (any user) | **S3 Tables API** (below) |\n| User specifically wants SQL DDL | **Athena DDL** (see `references\u002Fathena-ddl-path.md`) |\n| Glue ETL pipeline | **Spark DDL** via `--conf` job args (not `spark.conf.set()`). You MUST read `references\u002Ftable-creation-glue-etl.md` for the `--conf` string. |\n\n**Default: S3 Tables API:**\n\n```bash\naws s3tables create-table \\\n  --table-bucket-arn \u003CARN> \\\n  --namespace \u003CNAMESPACE> \\\n  --name \u003CTABLE_NAME> \\\n  --format ICEBERG \\\n  --metadata '\u003CMETADATA_JSON>'\n```\n\nMetadata JSON MUST nest under `\"iceberg\"` key:\n\n```json\n{\"iceberg\":{\"schema\":{\"fields\":[\n  {\"name\":\"order_date\",\"type\":\"date\",\"required\":true},\n  {\"name\":\"customer_id\",\"type\":\"string\",\"required\":true},\n  {\"name\":\"amount\",\"type\":\"double\",\"required\":false}\n]},\n\"partitionSpec\":{\"fields\":[\n  {\"sourceId\":1,\"fieldId\":1000,\"transform\":\"month\",\"name\":\"order_date_month\"}\n]}}}\n```\n\n**Constraints:**\n\n- `partitionSpec.sourceId` MUST reference a valid schema field ID\n- For schema evolution after creation, use Athena DDL. See `references\u002Fathena-ddl-path.md`\n- You MUST use `schemaV2` for complex types (list, map, struct) with explicit field IDs. See `references\u002Fbest-practices.md`.\n- You SHOULD search AWS docs for `\"IcebergPartitionField S3 Tables\"` for supported partition transforms\n\n### 8. Verify and Confirm\n\nYou MUST verify with `aws s3tables get-table` and confirm queryability with `DESCRIBE \u003Ctable_name>` via Athena using `--query-execution-context '{\"Catalog\":\"s3tablescatalog\u002F\u003CBUCKET_NAME>\",\"Database\":\"\u003CNAMESPACE>\"}'`. Do NOT put catalog in SQL. Present summary: bucket ARN, namespace, table, schema, partitions.\n\n## Troubleshooting\n\n| Error | Cause | Fix |\n|-------|-------|-----|\n| \"Table location can not be specified\" | LOCATION in CREATE TABLE | Remove LOCATION clause. S3 Tables manages storage automatically. |\n| `AccessDeniedException` with `s3:*` policy | Using `s3:*` not `s3tables:*` | S3 Tables uses `s3tables:*` namespace. 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Update IAM policy.",{"type":43,"tag":52,"props":2046,"children":2048},{"id":2047},"additional-resources",[2049],{"type":49,"value":2050},"Additional Resources",{"type":43,"tag":220,"props":2052,"children":2053},{},[2054,2065,2075,2085,2095],{"type":43,"tag":224,"props":2055,"children":2056},{},[2057,2063],{"type":43,"tag":2058,"props":2059,"children":2060},"a",{"href":1002},[2061],{"type":49,"value":2062},"access-control.md",{"type":49,"value":2064}," -- IAM permissions, ARN patterns, permission errors",{"type":43,"tag":224,"props":2066,"children":2067},{},[2068,2073],{"type":43,"tag":2058,"props":2069,"children":2070},{"href":377},[2071],{"type":49,"value":2072},"best-practices.md",{"type":49,"value":2074}," -- Iceberg types, partitions, naming, common errors",{"type":43,"tag":224,"props":2076,"children":2077},{},[2078,2083],{"type":43,"tag":2058,"props":2079,"children":2080},{"href":390},[2081],{"type":49,"value":2082},"athena-ddl-path.md",{"type":49,"value":2084}," -- Athena DDL, schema evolution",{"type":43,"tag":224,"props":2086,"children":2087},{},[2088,2093],{"type":43,"tag":2058,"props":2089,"children":2090},{"href":257},[2091],{"type":49,"value":2092},"table-creation-glue-etl.md",{"type":49,"value":2094}," -- Spark DDL via Glue ETL",{"type":43,"tag":224,"props":2096,"children":2097},{},[2098,2100,2105],{"type":49,"value":2099},"Loading data: ",{"type":43,"tag":96,"props":2101,"children":2103},{"className":2102},[],[2104],{"type":49,"value":239},{"type":49,"value":2106}," skill",{"type":43,"tag":2108,"props":2109,"children":2110},"style",{},[2111],{"type":49,"value":2112},"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":2114,"total":2218},[2115,2132,2147,2162,2177,2187,2202],{"slug":2116,"name":2116,"fn":2117,"description":2118,"org":2119,"tags":2120,"stars":24,"repoUrl":25,"updatedAt":2131},"agents-build","add capabilities to existing agent projects","Use when adding capabilities to an existing agent project — memory, app integration, VPC, multi-agent, migration, model changes, browser, code interpreter, or resource removal. Triggers on: \"add memory\", \"remember across sessions\", \"call agent from app\", \"invoke agent from code\", \"auth to call agent\", \"streaming responses\", \"VPC\", \"VPC connectivity\", \"VPC error\", \"can't reach from VPC\", \"multi-agent\", \"A2A\", \"A2A auth\", \"orchestrator not delegating\", \"specialist not called\", \"migrate Bedrock Agent\", \"after import\", \"migration issue\", \"framework for migration\", \"change model\", \"browser tool\", \"code interpreter\", \"delete agent\", \"tear down\", \"agentcore remove\", \"cross-account memory\", \"resource-based policy on memory\", \"pay for x402 content\", \"402 Payment Required\", \"microtransactions\", \"paid API or tool\". Not for connecting to external APIs via Gateway — use agents-connect. Not for scaffolding a new project — use agents-get-started. Not for CLI\u002Fdev server errors — use agents-debug. Strands vs LangGraph in a migration context routes here.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2121,2124,2127,2128],{"name":2122,"slug":2123,"type":15},"Agents","agents",{"name":2125,"slug":2126,"type":15},"Automation","automation",{"name":23,"slug":8,"type":15},{"name":2129,"slug":2130,"type":15},"Engineering","engineering","2026-07-12T08:42:53.812877",{"slug":2133,"name":2133,"fn":2134,"description":2135,"org":2136,"tags":2137,"stars":24,"repoUrl":25,"updatedAt":2146},"agents-connect","connect agents to external services","Use when connecting your agent to external APIs, tools, or services via Gateway, or restricting tool access with Cedar policies. Handles gateway setup, target types, outbound auth (OAuth, API key, IAM), credentials, and Cedar policy authoring. Triggers on: \"connect to API\", \"add gateway\", \"connect to MCP server\", \"Lambda tools\", \"OpenAPI\", \"gateway target\", \"Cedar policy\", \"restrict tools\", \"policy engine\", \"gateway auth error\", \"store API key\", \"outbound credential\", \"env var API key\", \"API key None after deploy\", \"credential not available after deploy\", \"should this be a gateway target\", \"give my agent tools\", \"add tools to agent\". Not for inbound auth (who can call your agent) — use agents-harden. Not for debugging agent behavior — use agents-debug. Not for VPC networking errors (agent can't reach APIs due to VPC) — use agents-build. Not for creating or hosting a new MCP server project — use agents-get-started.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2138,2139,2142,2145],{"name":2122,"slug":2123,"type":15},{"name":2140,"slug":2141,"type":15},"API Development","api-development",{"name":2143,"slug":2144,"type":15},"Authentication","authentication",{"name":23,"slug":8,"type":15},"2026-07-16T06:00:38.866147",{"slug":2148,"name":2148,"fn":2149,"description":2150,"org":2151,"tags":2152,"stars":24,"repoUrl":25,"updatedAt":2161},"agents-debug","debug agent and environment issues","Use when your agent or environment is broken — wrong answers, errors, timeouts, tool failures, or CLI issues. Reads traces and logs to diagnose root causes. Also checks prerequisites when the CLI itself isn't working. Triggers on: \"agent not working\", \"wrong answer\", \"agent error\", \"tool call failing\", \"debug agent\", \"check logs\", \"read traces\", \"broken\", \"500 error\", \"424 error\", \"model access denied\", \"command not found\", \"stuck in DELETING\", \"maxVms exceeded\", \"cold start diagnosis\", \"cold start slow\", \"agentcore create error\", \"create failed\", \"exit code 7\", \"connection refused local dev\". Not for deploy failures — use agents-deploy. Not for performance tuning without errors — use agents-optimize. Not for VPC configuration — use agents-build. Not for observability setup or missing logs — use agents-optimize.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2153,2154,2155,2158],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2156,"slug":2157,"type":15},"Debugging","debugging",{"name":2159,"slug":2160,"type":15},"Observability","observability","2026-07-16T06:00:44.679093",{"slug":2163,"name":2163,"fn":2164,"description":2165,"org":2166,"tags":2167,"stars":24,"repoUrl":25,"updatedAt":2176},"agents-deploy","deploy AI agents to AWS","Use when deploying your agent to AWS, or when a deploy has failed. Handles pre-flight validation, CDK\u002FIAM\u002Fquota error diagnosis, version management, rollback, and canary deployments. Triggers on: \"deploy my agent\", \"agentcore deploy\", \"deploy failed\", \"CDK error\", \"rollback\", \"canary deploy\", \"pin version\", \"redeploy\", \"deploy stuck\". Not for production hardening — use agents-harden. Not for adding capabilities before deploy — use agents-build or agents-connect. Not for VPC configuration errors — use agents-build.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2168,2169,2170,2173],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2171,"slug":2172,"type":15},"CI\u002FCD","ci-cd",{"name":2174,"slug":2175,"type":15},"Deployment","deployment","2026-07-12T08:42:55.059577",{"slug":2178,"name":2178,"fn":2179,"description":2180,"org":2181,"tags":2182,"stars":24,"repoUrl":25,"updatedAt":2186},"agents-get-started","scaffold and deploy new agent projects","Use when a developer wants to create a new agent project or get started with AgentCore. Handles framework selection, project scaffolding, first deploy, and first invocation. Triggers on: \"build an agent\", \"create an agent\", \"get started\", \"new project\", \"agentcore create\", \"which framework\", \"Strands vs LangGraph\", \"hello world agent\", \"first agent\", \"create MCP server\", \"host MCP server\", \"agentcore dev\", \"dev server\", \"what port\", \"local development\". Not for adding capabilities to existing projects — use agents-build or agents-connect. Strands vs LangGraph in a migration context routes to agents-build, not here. Connecting to an existing MCP server routes to agents-connect, not here.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2183,2184,2185],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2174,"slug":2175,"type":15},"2026-07-12T08:42:51.963247",{"slug":2188,"name":2188,"fn":2189,"description":2190,"org":2191,"tags":2192,"stars":24,"repoUrl":25,"updatedAt":2201},"agents-harden","harden agents for production","Use when preparing your agent for production — IAM scoping, inbound auth (JWT, SigV4), secrets management, cold start optimization, session lifecycle, rate limiting, input validation, and quota guidance. Triggers on: \"production checklist\", \"harden agent\", \"production ready\", \"secure agent\", \"inbound auth\", \"going live\", \"cold start optimization\", \"session lifecycle\", \"StopRuntimeSession\", \"quota\", \"throttling\", \"maxVms\", \"rate limit\", \"security audit of outbound API calls\", \"gateway target audit for production\", \"restrict who can call\", \"lock down endpoint\", \"only our app can call\". Not for Cedar tool-restriction policies — use agents-connect. Not for quality measurement — use agents-optimize. Not for outbound credential storage or API key wiring — use agents-connect. Not for A2A agent-to-agent auth — use agents-build. Cold start observation and diagnosis (not optimization) routes to agents-debug.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2193,2194,2195,2198],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2196,"slug":2197,"type":15},"Best Practices","best-practices",{"name":2199,"slug":2200,"type":15},"Security","security","2026-07-16T06:00:42.174705",{"slug":2203,"name":2203,"fn":2204,"description":2205,"org":2206,"tags":2207,"stars":24,"repoUrl":25,"updatedAt":2217},"agents-optimize","optimize agent quality and performance","Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI\u002FCD quality gates, observability, or cost optimization. Triggers on: \"evaluate my agent\", \"add evaluator\", \"measure quality\", \"quality gate\", \"run evals\", \"agent too slow\", \"why is it slow\", \"reduce latency\", \"set up observability\", \"CloudWatch dashboard\", \"how much does my agent cost\", \"cost optimization\", \"logs not showing up\", \"logs missing\", \"spans not found\", \"eval failing\", \"eval error\", \"dev traces\", \"local traces\", \"agentcore dev traces\", \"traces to CloudWatch\". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2208,2209,2210,2213,2214],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2211,"slug":2212,"type":15},"Evals","evals",{"name":2159,"slug":2160,"type":15},{"name":2215,"slug":2216,"type":15},"Performance","performance","2026-07-12T08:42:56.488105",114,{"items":2220,"total":2335},[2221,2228,2235,2242,2249,2255,2262,2270,2285,2298,2310,2325],{"slug":2116,"name":2116,"fn":2117,"description":2118,"org":2222,"tags":2223,"stars":24,"repoUrl":25,"updatedAt":2131},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2224,2225,2226,2227],{"name":2122,"slug":2123,"type":15},{"name":2125,"slug":2126,"type":15},{"name":23,"slug":8,"type":15},{"name":2129,"slug":2130,"type":15},{"slug":2133,"name":2133,"fn":2134,"description":2135,"org":2229,"tags":2230,"stars":24,"repoUrl":25,"updatedAt":2146},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2231,2232,2233,2234],{"name":2122,"slug":2123,"type":15},{"name":2140,"slug":2141,"type":15},{"name":2143,"slug":2144,"type":15},{"name":23,"slug":8,"type":15},{"slug":2148,"name":2148,"fn":2149,"description":2150,"org":2236,"tags":2237,"stars":24,"repoUrl":25,"updatedAt":2161},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2238,2239,2240,2241],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2156,"slug":2157,"type":15},{"name":2159,"slug":2160,"type":15},{"slug":2163,"name":2163,"fn":2164,"description":2165,"org":2243,"tags":2244,"stars":24,"repoUrl":25,"updatedAt":2176},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2245,2246,2247,2248],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2171,"slug":2172,"type":15},{"name":2174,"slug":2175,"type":15},{"slug":2178,"name":2178,"fn":2179,"description":2180,"org":2250,"tags":2251,"stars":24,"repoUrl":25,"updatedAt":2186},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2252,2253,2254],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2174,"slug":2175,"type":15},{"slug":2188,"name":2188,"fn":2189,"description":2190,"org":2256,"tags":2257,"stars":24,"repoUrl":25,"updatedAt":2201},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2258,2259,2260,2261],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2196,"slug":2197,"type":15},{"name":2199,"slug":2200,"type":15},{"slug":2203,"name":2203,"fn":2204,"description":2205,"org":2263,"tags":2264,"stars":24,"repoUrl":25,"updatedAt":2217},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2265,2266,2267,2268,2269],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2211,"slug":2212,"type":15},{"name":2159,"slug":2160,"type":15},{"name":2215,"slug":2216,"type":15},{"slug":2271,"name":2271,"fn":2272,"description":2273,"org":2274,"tags":2275,"stars":24,"repoUrl":25,"updatedAt":2284},"amazon-aurora-mysql","manage Amazon Aurora MySQL clusters","Amazon Aurora MySQL — creates, modifies, and advises on Aurora MySQL clusters specifically (MySQL-compatible engine, Aurora serverless, parallel query). Trigger for Aurora MySQL cluster operations, ACU sizing, I\u002FO-Optimized storage, commitment pricing, or MySQL upgrade planning. Aurora MySQL uses full (VPC-based) configuration — express configuration is PostgreSQL-only. For Aurora PostgreSQL, use amazon-aurora-postgresql instead. Contains safety guardrails and response templates that override defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2276,2277,2278,2281],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":2279,"slug":2280,"type":15},"MySQL","mysql",{"name":2282,"slug":2283,"type":15},"Serverless","serverless","2026-07-12T08:43:13.27939",{"slug":2286,"name":2286,"fn":2287,"description":2288,"org":2289,"tags":2290,"stars":24,"repoUrl":25,"updatedAt":2297},"amazon-aurora-postgresql","configure Amazon Aurora PostgreSQL clusters","Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I\u002FO-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2291,2292,2293,2296],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":2294,"slug":2295,"type":15},"PostgreSQL","postgresql",{"name":2282,"slug":2283,"type":15},"2026-07-16T06:00:34.789624",{"slug":2299,"name":2299,"fn":2300,"description":2301,"org":2302,"tags":2303,"stars":24,"repoUrl":25,"updatedAt":2309},"amazon-bedrock","build generative AI apps with Amazon Bedrock","Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating\u002Fporting\u002Fconverting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments setup (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, 402 Payment Required, pay for content, paid endpoint, agent payments). NOT for custom model training, Rekognition, or Comprehend.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2304,2305,2306],{"name":2122,"slug":2123,"type":15},{"name":23,"slug":8,"type":15},{"name":2307,"slug":2308,"type":15},"LLM","llm","2026-07-25T05:30:35.20899",{"slug":2311,"name":2311,"fn":2312,"description":2313,"org":2314,"tags":2315,"stars":24,"repoUrl":25,"updatedAt":2324},"amazon-documentdb","manage Amazon DocumentDB clusters","Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS\u002FVPC\u002Fdriver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0→5.0→8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2316,2317,2318,2321],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":2319,"slug":2320,"type":15},"MongoDB","mongodb",{"name":2322,"slug":2323,"type":15},"NoSQL","nosql","2026-07-12T08:43:00.455878",{"slug":2326,"name":2326,"fn":2327,"description":2328,"org":2329,"tags":2330,"stars":24,"repoUrl":25,"updatedAt":2334},"amazon-dynamodb","design and debug DynamoDB data layers","Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition\u002Fsort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, and zero-ETL integrations to OpenSearch\u002FRedshift\u002FSageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines — even when they don't say \"axioms\" or \"design review.\" Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2331,2332,2333],{"name":23,"slug":8,"type":15},{"name":17,"slug":18,"type":15},{"name":2322,"slug":2323,"type":15},"2026-07-16T06:00:37.690386",115]