[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-managing-amazon-msk":3,"mdc-qbc66y-key":32,"related-repo-aws-managing-amazon-msk":810,"related-org-aws-managing-amazon-msk":916},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":21,"repoUrl":22,"updatedAt":23,"license":24,"forks":25,"topics":26,"repo":27,"sourceUrl":30,"mdContent":31},"managing-amazon-msk","operate Amazon MSK Provisioned clusters","Operates Amazon MSK Provisioned clusters (Standard and Express brokers). Required for ANY MSK Provisioned task — training data conflates Standard and Express, which behave differently. Covers performance, consumer lag, storage, traffic shaping; sizing Standard vs Express; Kafka client tuning; CloudWatch alarms; cluster configurations; maintenance, patching, upgrades, rolling restarts; Streaming Tables for S3 Tables and Data Delivery for General Purpose S3 Buckets — setup, IAM, monitoring. Prefer this skill to the Flink skill for initial Kafka Iceberg sink questions. Triggers: MSK Provisioned (Express\u002FStandard), Kafka, kafka.*or express.* instance types, AWS\u002FKafka namespace, consumer lag, patching, Streaming Tables, Kafka to Iceberg on S3 Tables, Kafka to S3, lakehouse, data lake from Kafka, Kafka Connect S3 Sink or Firehose alternative. DO NOT USE for MSK Connect or Replicator — search documentation instead. Only use for Serverless for eligibility questions for S3 Tables\u002Fstreaming tables\u002Fdata delivery.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"aws","AWS (Amazon)","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws.png",[12,16,19],{"name":13,"slug":14,"type":15},"Monitoring","monitoring","tag",{"name":17,"slug":18,"type":15},"Infrastructure","infrastructure",{"name":20,"slug":8,"type":15},"AWS",1822,"https:\u002F\u002Fgithub.com\u002Faws\u002Fagent-toolkit-for-aws","2026-08-01T05:42:05.387399",null,157,[],{"repoUrl":22,"stars":21,"forks":25,"topics":28,"description":29},[],"Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS","https:\u002F\u002Fgithub.com\u002Faws\u002Fagent-toolkit-for-aws\u002Ftree\u002FHEAD\u002Fskills\u002Fspecialized-skills\u002Fanalytics-skills\u002Fmanaging-amazon-msk","---\nname: managing-amazon-msk\ndescription: >-\n  Operates Amazon MSK Provisioned clusters (Standard and Express brokers). Required\n  for ANY MSK Provisioned task — training data conflates Standard and Express, which\n  behave differently. Covers performance, consumer lag, storage, traffic shaping;\n  sizing Standard vs Express; Kafka client tuning; CloudWatch alarms; cluster configurations;\n  maintenance, patching, upgrades, rolling restarts; Streaming Tables for S3 Tables\n  and Data Delivery for General Purpose S3 Buckets — setup, IAM, monitoring. Prefer\n  this skill to the Flink skill for initial Kafka Iceberg sink questions. Triggers:\n  MSK Provisioned (Express\u002FStandard), Kafka, kafka.*or express.* instance types,\n  AWS\u002FKafka namespace, consumer lag, patching, Streaming Tables, Kafka to Iceberg\n  on S3 Tables, Kafka to S3, lakehouse, data lake from Kafka, Kafka Connect S3 Sink\n  or Firehose alternative. DO NOT USE for MSK Connect or Replicator — search documentation\n  instead. Only use for Serverless for eligibility questions for S3 Tables\u002Fstreaming\n  tables\u002Fdata delivery.\nversion: 2\n---\n\n# Amazon MSK\n\n## Overview\n\nDomain expertise for operating Amazon MSK Provisioned clusters with Standard and Express broker types. Covers performance troubleshooting, consumer lag diagnosis, storage management, cluster sizing, client configuration, and CloudWatch monitoring.\n\nExecute commands using available tools from the AWS MCP server when connected — it provides sandboxed execution, audit logging, and observability. When the MCP server is not available, fall back to the AWS CLI or shell as needed.\n\n**Standard brokers** use customer-managed EBS volumes for storage. You choose instance types (kafka.m5\u002Fm7g families), provision EBS, and manage storage scaling.\n\n**Express brokers** provide fully managed, pay-as-you-go storage with no EBS provisioning. They use instance types prefixed with `express.m7g`, offer up to 3x more throughput per broker, and have no maintenance windows. Express brokers have NO customer-managed EBS — do NOT recommend EBS expansion or provisioned throughput for Express clusters. Express brokers enforce fixed replication factor of 3 and `min.insync.replicas=2` — do NOT attempt to create topics with RF=1 on Express. If RF=1 is needed, use Standard brokers.\n\n## Which Workflow Do You Need?\n\nDetermine the broker type first: `aws kafka describe-cluster-v2 --cluster-arn \u003Carn>`. Check `Provisioned.BrokerNodeGroupInfo.InstanceType` — if it starts with `express.`, it is an Express cluster.\n\n| Customer Intent | Reference |\n|---|---|\n| High CPU, high latency, slow cluster, traffic shaping | [troubleshoot-performance.md](references\u002Ftroubleshoot-performance.md) |\n| Consumer lag increasing, rebalance storms, stuck consumer groups | [troubleshoot-consumer-lag.md](references\u002Ftroubleshoot-consumer-lag.md) |\n| Disk filling up, retention planning, tiered storage | [manage-storage.md](references\u002Fmanage-storage.md) |\n| Choosing Standard vs Express, sizing a cluster, partition limits, broker count, monthly cost | [size-and-choose-cluster.md](references\u002Fsize-and-choose-cluster.md) |\n| Producer\u002Fconsumer configuration, IAM\u002FSCRAM\u002FTLS auth | [configure-clients.md](references\u002Fconfigure-clients.md) |\n| Setting up monitoring, dashboards, alarms | [monitor-and-alarm.md](references\u002Fmonitor-and-alarm.md) |\n| Full CloudWatch metric list (Standard or Express) | Search AWS docs for `\"MSK CloudWatch metrics Standard brokers\"` or `\"MSK CloudWatch metrics Express brokers\"` |\n| Rolling restart impact, patching, maintenance resilience | [maintenance-operations.md](references\u002Fmaintenance-operations.md) |\n| Deliver streaming data to Apache Iceberg tables on S3 Tables with low cost in a fully managed service (Streaming Tables) — setup, IAM, schema, create\u002Fupdate\u002Fdelete\u002Flist\u002Fdescribe channels | [streaming-tables.md](references\u002Fstreaming-tables.md) |\n| Deliver topic data to S3 bucket as JSON\u002FByteArray\u002FString objects with low cost in a fully managed service (Data Delivery for General Purpose S3 buckets) — setup, IAM, output key templates, create\u002Fupdate\u002Fdelete\u002Flist\u002Fdescribe channels | [data-delivery-for-general-purpose-s3.md](references\u002Fdata-delivery-for-general-purpose-s3.md) |\n| Build a lakehouse \u002F data lake from Kafka; make streaming data queryable in Athena | [streaming-tables.md](references\u002Fstreaming-tables.md) |\n| Alternative to Kafka Connect S3 Sink or Amazon Data Firehose for MSK; zero-ops streaming delivery to S3 | [data-delivery-for-general-purpose-s3.md](references\u002Fdata-delivery-for-general-purpose-s3.md) |\n| Streaming Tables \u002F Data Delivery CloudWatch metrics and alarms, DLQ errors, failed deliveries, channel state transitions, freshness lag | [streaming-tables-troubleshooting.md](references\u002Fstreaming-tables-troubleshooting.md) |\n| \"Can I use Streaming Tables \u002F Data Delivery on MSK Serverless \u002F Standard brokers?\" — eligibility routing | [streaming-tables.md](references\u002Fstreaming-tables.md) (answer is always: Express brokers only) |\n\n## Available scripts\n\n- **`scripts\u002Fmsk_sizing.py`** — **MUST** be run for any sizing question (broker count, instance choice, cost). See [size-and-choose-cluster.md](references\u002Fsize-and-choose-cluster.md) for the required workflow and script reference.\n\n## Guardrail — where this skill's own files live (MCP vs local install)\n\nThis skill can be loaded two ways, and they resolve the skill's **own bundled files** — the `references\u002F` documents and the `scripts\u002F` files\nfrom different places. Determine how the skill was loaded before you read a reference or run a script:\n\n- **Loaded through the AWS MCP `retrieve_skill` tool call.** The skill is **not\n  installed on the local filesystem**; its reference files and scripts do not\n  exist on disk. You MUST fetch each reference or script through the same\n  `retrieve_skill` tool by passing the `file` parameter (for example,\n  `file=\"references\u002Fconfigure-clients.md\"` or `file=\"scripts\u002Fmsk_sizing.py\"`),\n  and run a script from the content that tool returns. Do NOT `file_read` these\n  paths from the local or working directory, and do NOT search the filesystem\n  for them — they are not there, and any local file that happens to match the\n  name is unrelated to this skill.\n- **Installed locally** (the skill lives in a local skills directory such as\n  `.claude\u002Fskills\u002Fmanaging-amazon-msk\u002F`, `~\u002F.claude\u002Fskills\u002Fmanaging-amazon-msk\u002F`, or\n  `.kiro\u002Fskills\u002Fmanaging-amazon-msk\u002F`). Read references and run scripts from the\n  local skill directory using the relative paths shown throughout this\n  documentation.\n\nThis distinction applies **only** to the skill's own packaged files. Every artifact\ncreated during a session or supplied by users are read from and written to\nthe user's working directory regardless of how the skill was loaded. Never\nfetch or write customer data through `retrieve_skill`.\n\n## Common Workflows\n\n**Create cluster configuration (`server.properties`):**\n\nThe `--server-properties` argument MUST be a real Kafka properties file with one `key=value` per line, separated by actual newline (`\\n`) characters — NOT the literal two-character escape sequence `\\n`. The MSK API accepts the bytes as-is; if you pass `\"k1=v1\\nk2=v2\"` as a single string with escaped newlines, MSK stores ONE invalid property line and the cluster will fail to apply it.\n\nRecommended pattern: write the properties to a local file with real newlines, then pass it via `fileb:\u002F\u002F` so the CLI uploads the raw bytes verbatim. Verify by reading the revision back with `describe-configuration-revision` and base64-decoding `ServerProperties` — you should see one property per line.\n\n```\ncat > server.properties \u003C\u003C'EOF'\nauto.create.topics.enable=false\ndefault.replication.factor=3\nmin.insync.replicas=2\nunclean.leader.election.enable=false\nnum.io.threads=32\nnum.network.threads=16\nlog.retention.hours=168\nEOF\n\naws kafka create-configuration \\\n  --name \u003Cconfig-name> \\\n  --kafka-versions \"3.6.0\" \\\n  --server-properties fileb:\u002F\u002Fserver.properties\n```\n\nFor per-instance-size thread tuning (`num.io.threads`, `num.network.threads`) and durability defaults, see [size-and-choose-cluster.md](references\u002Fsize-and-choose-cluster.md) and [configure-clients.md](references\u002Fconfigure-clients.md).\n\n## Additional Resources\n\n- [MSK Best Practices - Standard](https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fbestpractices.html)\n- [MSK Best Practices - Express](https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fbestpractices-express.html)\n- [MSK Client Best Practices](https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fbestpractices-kafka-client.html)\n- [MSK CloudWatch Metrics](https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmetrics-details.html)\n- [MSK Quotas](https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Flimits.html)\n- [MSK Configuration](https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-configuration.html)\n",{"data":33,"body":35},{"name":4,"description":6,"version":34},2,{"type":36,"children":37},"root",[38,47,54,60,65,76,103,109,138,411,417,449,455,483,583,602,608,624,668,697,709,740,746],{"type":39,"tag":40,"props":41,"children":43},"element","h1",{"id":42},"amazon-msk",[44],{"type":45,"value":46},"text","Amazon MSK",{"type":39,"tag":48,"props":49,"children":51},"h2",{"id":50},"overview",[52],{"type":45,"value":53},"Overview",{"type":39,"tag":55,"props":56,"children":57},"p",{},[58],{"type":45,"value":59},"Domain expertise for operating Amazon MSK Provisioned clusters with Standard and Express broker types. 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See ",{"type":39,"tag":178,"props":444,"children":445},{"href":231},[446],{"type":45,"value":234},{"type":45,"value":448}," for the required workflow and script reference.",{"type":39,"tag":48,"props":450,"children":452},{"id":451},"guardrail-where-this-skills-own-files-live-mcp-vs-local-install",[453],{"type":45,"value":454},"Guardrail — where this skill's own files live (MCP vs local install)",{"type":39,"tag":55,"props":456,"children":457},{},[458,460,465,467,473,475,481],{"type":45,"value":459},"This skill can be loaded two ways, and they resolve the skill's ",{"type":39,"tag":69,"props":461,"children":462},{},[463],{"type":45,"value":464},"own bundled files",{"type":45,"value":466}," — the ",{"type":39,"tag":87,"props":468,"children":470},{"className":469},[],[471],{"type":45,"value":472},"references\u002F",{"type":45,"value":474}," documents and the ",{"type":39,"tag":87,"props":476,"children":478},{"className":477},[],[479],{"type":45,"value":480},"scripts\u002F",{"type":45,"value":482}," files\nfrom different places. Determine how the skill was loaded before you read a reference or run a script:",{"type":39,"tag":418,"props":484,"children":485},{},[486,549],{"type":39,"tag":422,"props":487,"children":488},{},[489,502,504,509,511,516,518,524,526,532,533,539,541,547],{"type":39,"tag":69,"props":490,"children":491},{},[492,494,500],{"type":45,"value":493},"Loaded through the AWS MCP ",{"type":39,"tag":87,"props":495,"children":497},{"className":496},[],[498],{"type":45,"value":499},"retrieve_skill",{"type":45,"value":501}," tool call.",{"type":45,"value":503}," The skill is ",{"type":39,"tag":69,"props":505,"children":506},{},[507],{"type":45,"value":508},"not\ninstalled on the local filesystem",{"type":45,"value":510},"; its reference files and scripts do not\nexist on disk. You MUST fetch each reference or script through the same\n",{"type":39,"tag":87,"props":512,"children":514},{"className":513},[],[515],{"type":45,"value":499},{"type":45,"value":517}," tool by passing the ",{"type":39,"tag":87,"props":519,"children":521},{"className":520},[],[522],{"type":45,"value":523},"file",{"type":45,"value":525}," parameter (for example,\n",{"type":39,"tag":87,"props":527,"children":529},{"className":528},[],[530],{"type":45,"value":531},"file=\"references\u002Fconfigure-clients.md\"",{"type":45,"value":289},{"type":39,"tag":87,"props":534,"children":536},{"className":535},[],[537],{"type":45,"value":538},"file=\"scripts\u002Fmsk_sizing.py\"",{"type":45,"value":540},"),\nand run a script from the content that tool returns. Do NOT ",{"type":39,"tag":87,"props":542,"children":544},{"className":543},[],[545],{"type":45,"value":546},"file_read",{"type":45,"value":548}," these\npaths from the local or working directory, and do NOT search the filesystem\nfor them — they are not there, and any local file that happens to match the\nname is unrelated to this skill.",{"type":39,"tag":422,"props":550,"children":551},{},[552,557,559,565,567,573,575,581],{"type":39,"tag":69,"props":553,"children":554},{},[555],{"type":45,"value":556},"Installed locally",{"type":45,"value":558}," (the skill lives in a local skills directory such as\n",{"type":39,"tag":87,"props":560,"children":562},{"className":561},[],[563],{"type":45,"value":564},".claude\u002Fskills\u002Fmanaging-amazon-msk\u002F",{"type":45,"value":566},", ",{"type":39,"tag":87,"props":568,"children":570},{"className":569},[],[571],{"type":45,"value":572},"~\u002F.claude\u002Fskills\u002Fmanaging-amazon-msk\u002F",{"type":45,"value":574},", or\n",{"type":39,"tag":87,"props":576,"children":578},{"className":577},[],[579],{"type":45,"value":580},".kiro\u002Fskills\u002Fmanaging-amazon-msk\u002F",{"type":45,"value":582},"). Read references and run scripts from the\nlocal skill directory using the relative paths shown throughout this\ndocumentation.",{"type":39,"tag":55,"props":584,"children":585},{},[586,588,593,595,600],{"type":45,"value":587},"This distinction applies ",{"type":39,"tag":69,"props":589,"children":590},{},[591],{"type":45,"value":592},"only",{"type":45,"value":594}," to the skill's own packaged files. Every artifact\ncreated during a session or supplied by users are read from and written to\nthe user's working directory regardless of how the skill was loaded. Never\nfetch or write customer data through ",{"type":39,"tag":87,"props":596,"children":598},{"className":597},[],[599],{"type":45,"value":499},{"type":45,"value":601},".",{"type":39,"tag":48,"props":603,"children":605},{"id":604},"common-workflows",[606],{"type":45,"value":607},"Common Workflows",{"type":39,"tag":55,"props":609,"children":610},{},[611],{"type":39,"tag":69,"props":612,"children":613},{},[614,616,622],{"type":45,"value":615},"Create cluster configuration (",{"type":39,"tag":87,"props":617,"children":619},{"className":618},[],[620],{"type":45,"value":621},"server.properties",{"type":45,"value":623},"):",{"type":39,"tag":55,"props":625,"children":626},{},[627,629,635,637,643,645,651,653,658,660,666],{"type":45,"value":628},"The ",{"type":39,"tag":87,"props":630,"children":632},{"className":631},[],[633],{"type":45,"value":634},"--server-properties",{"type":45,"value":636}," argument MUST be a real Kafka properties file with one ",{"type":39,"tag":87,"props":638,"children":640},{"className":639},[],[641],{"type":45,"value":642},"key=value",{"type":45,"value":644}," per line, separated by actual newline (",{"type":39,"tag":87,"props":646,"children":648},{"className":647},[],[649],{"type":45,"value":650},"\\n",{"type":45,"value":652},") characters — NOT the literal two-character escape sequence ",{"type":39,"tag":87,"props":654,"children":656},{"className":655},[],[657],{"type":45,"value":650},{"type":45,"value":659},". The MSK API accepts the bytes as-is; if you pass ",{"type":39,"tag":87,"props":661,"children":663},{"className":662},[],[664],{"type":45,"value":665},"\"k1=v1\\nk2=v2\"",{"type":45,"value":667}," as a single string with escaped newlines, MSK stores ONE invalid property line and the cluster will fail to apply it.",{"type":39,"tag":55,"props":669,"children":670},{},[671,673,679,681,687,689,695],{"type":45,"value":672},"Recommended pattern: write the properties to a local file with real newlines, then pass it via ",{"type":39,"tag":87,"props":674,"children":676},{"className":675},[],[677],{"type":45,"value":678},"fileb:\u002F\u002F",{"type":45,"value":680}," so the CLI uploads the raw bytes verbatim. Verify by reading the revision back with ",{"type":39,"tag":87,"props":682,"children":684},{"className":683},[],[685],{"type":45,"value":686},"describe-configuration-revision",{"type":45,"value":688}," and base64-decoding ",{"type":39,"tag":87,"props":690,"children":692},{"className":691},[],[693],{"type":45,"value":694},"ServerProperties",{"type":45,"value":696}," — you should see one property per line.",{"type":39,"tag":698,"props":699,"children":703},"pre",{"className":700,"code":702,"language":45},[701],"language-text","cat > server.properties \u003C\u003C'EOF'\nauto.create.topics.enable=false\ndefault.replication.factor=3\nmin.insync.replicas=2\nunclean.leader.election.enable=false\nnum.io.threads=32\nnum.network.threads=16\nlog.retention.hours=168\nEOF\n\naws kafka create-configuration \\\n  --name \u003Cconfig-name> \\\n  --kafka-versions \"3.6.0\" \\\n  --server-properties fileb:\u002F\u002Fserver.properties\n",[704],{"type":39,"tag":87,"props":705,"children":707},{"__ignoreMap":706},"",[708],{"type":45,"value":702},{"type":39,"tag":55,"props":710,"children":711},{},[712,714,720,721,727,729,733,735,739],{"type":45,"value":713},"For per-instance-size thread tuning (",{"type":39,"tag":87,"props":715,"children":717},{"className":716},[],[718],{"type":45,"value":719},"num.io.threads",{"type":45,"value":566},{"type":39,"tag":87,"props":722,"children":724},{"className":723},[],[725],{"type":45,"value":726},"num.network.threads",{"type":45,"value":728},") and durability defaults, see ",{"type":39,"tag":178,"props":730,"children":731},{"href":231},[732],{"type":45,"value":234},{"type":45,"value":734}," and ",{"type":39,"tag":178,"props":736,"children":737},{"href":248},[738],{"type":45,"value":251},{"type":45,"value":601},{"type":39,"tag":48,"props":741,"children":743},{"id":742},"additional-resources",[744],{"type":45,"value":745},"Additional Resources",{"type":39,"tag":418,"props":747,"children":748},{},[749,760,770,780,790,800],{"type":39,"tag":422,"props":750,"children":751},{},[752],{"type":39,"tag":178,"props":753,"children":757},{"href":754,"rel":755},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fbestpractices.html",[756],"nofollow",[758],{"type":45,"value":759},"MSK Best Practices - Standard",{"type":39,"tag":422,"props":761,"children":762},{},[763],{"type":39,"tag":178,"props":764,"children":767},{"href":765,"rel":766},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fbestpractices-express.html",[756],[768],{"type":45,"value":769},"MSK Best Practices - Express",{"type":39,"tag":422,"props":771,"children":772},{},[773],{"type":39,"tag":178,"props":774,"children":777},{"href":775,"rel":776},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fbestpractices-kafka-client.html",[756],[778],{"type":45,"value":779},"MSK Client Best Practices",{"type":39,"tag":422,"props":781,"children":782},{},[783],{"type":39,"tag":178,"props":784,"children":787},{"href":785,"rel":786},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmetrics-details.html",[756],[788],{"type":45,"value":789},"MSK CloudWatch Metrics",{"type":39,"tag":422,"props":791,"children":792},{},[793],{"type":39,"tag":178,"props":794,"children":797},{"href":795,"rel":796},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Flimits.html",[756],[798],{"type":45,"value":799},"MSK Quotas",{"type":39,"tag":422,"props":801,"children":802},{},[803],{"type":39,"tag":178,"props":804,"children":807},{"href":805,"rel":806},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-configuration.html",[756],[808],{"type":45,"value":809},"MSK Configuration",{"items":811,"total":915},[812,829,844,859,874,884,899],{"slug":813,"name":813,"fn":814,"description":815,"org":816,"tags":817,"stars":21,"repoUrl":22,"updatedAt":828},"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},[818,821,824,825],{"name":819,"slug":820,"type":15},"Agents","agents",{"name":822,"slug":823,"type":15},"Automation","automation",{"name":20,"slug":8,"type":15},{"name":826,"slug":827,"type":15},"Engineering","engineering","2026-07-12T08:42:53.812877",{"slug":830,"name":830,"fn":831,"description":832,"org":833,"tags":834,"stars":21,"repoUrl":22,"updatedAt":843},"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},[835,836,839,842],{"name":819,"slug":820,"type":15},{"name":837,"slug":838,"type":15},"API Development","api-development",{"name":840,"slug":841,"type":15},"Authentication","authentication",{"name":20,"slug":8,"type":15},"2026-07-16T06:00:38.866147",{"slug":845,"name":845,"fn":846,"description":847,"org":848,"tags":849,"stars":21,"repoUrl":22,"updatedAt":858},"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},[850,851,852,855],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":853,"slug":854,"type":15},"Debugging","debugging",{"name":856,"slug":857,"type":15},"Observability","observability","2026-07-16T06:00:44.679093",{"slug":860,"name":860,"fn":861,"description":862,"org":863,"tags":864,"stars":21,"repoUrl":22,"updatedAt":873},"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},[865,866,867,870],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":868,"slug":869,"type":15},"CI\u002FCD","ci-cd",{"name":871,"slug":872,"type":15},"Deployment","deployment","2026-07-12T08:42:55.059577",{"slug":875,"name":875,"fn":876,"description":877,"org":878,"tags":879,"stars":21,"repoUrl":22,"updatedAt":883},"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},[880,881,882],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":871,"slug":872,"type":15},"2026-07-12T08:42:51.963247",{"slug":885,"name":885,"fn":886,"description":887,"org":888,"tags":889,"stars":21,"repoUrl":22,"updatedAt":898},"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},[890,891,892,895],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":893,"slug":894,"type":15},"Best Practices","best-practices",{"name":896,"slug":897,"type":15},"Security","security","2026-07-16T06:00:42.174705",{"slug":900,"name":900,"fn":901,"description":902,"org":903,"tags":904,"stars":21,"repoUrl":22,"updatedAt":914},"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},[905,906,907,910,911],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":908,"slug":909,"type":15},"Evals","evals",{"name":856,"slug":857,"type":15},{"name":912,"slug":913,"type":15},"Performance","performance","2026-07-12T08:42:56.488105",114,{"items":917,"total":1034},[918,925,932,939,946,952,959,967,984,997,1009,1024],{"slug":813,"name":813,"fn":814,"description":815,"org":919,"tags":920,"stars":21,"repoUrl":22,"updatedAt":828},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[921,922,923,924],{"name":819,"slug":820,"type":15},{"name":822,"slug":823,"type":15},{"name":20,"slug":8,"type":15},{"name":826,"slug":827,"type":15},{"slug":830,"name":830,"fn":831,"description":832,"org":926,"tags":927,"stars":21,"repoUrl":22,"updatedAt":843},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[928,929,930,931],{"name":819,"slug":820,"type":15},{"name":837,"slug":838,"type":15},{"name":840,"slug":841,"type":15},{"name":20,"slug":8,"type":15},{"slug":845,"name":845,"fn":846,"description":847,"org":933,"tags":934,"stars":21,"repoUrl":22,"updatedAt":858},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[935,936,937,938],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":853,"slug":854,"type":15},{"name":856,"slug":857,"type":15},{"slug":860,"name":860,"fn":861,"description":862,"org":940,"tags":941,"stars":21,"repoUrl":22,"updatedAt":873},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[942,943,944,945],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":868,"slug":869,"type":15},{"name":871,"slug":872,"type":15},{"slug":875,"name":875,"fn":876,"description":877,"org":947,"tags":948,"stars":21,"repoUrl":22,"updatedAt":883},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[949,950,951],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":871,"slug":872,"type":15},{"slug":885,"name":885,"fn":886,"description":887,"org":953,"tags":954,"stars":21,"repoUrl":22,"updatedAt":898},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[955,956,957,958],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":893,"slug":894,"type":15},{"name":896,"slug":897,"type":15},{"slug":900,"name":900,"fn":901,"description":902,"org":960,"tags":961,"stars":21,"repoUrl":22,"updatedAt":914},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[962,963,964,965,966],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":908,"slug":909,"type":15},{"name":856,"slug":857,"type":15},{"name":912,"slug":913,"type":15},{"slug":968,"name":968,"fn":969,"description":970,"org":971,"tags":972,"stars":21,"repoUrl":22,"updatedAt":983},"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},[973,974,977,980],{"name":20,"slug":8,"type":15},{"name":975,"slug":976,"type":15},"Database","database",{"name":978,"slug":979,"type":15},"MySQL","mysql",{"name":981,"slug":982,"type":15},"Serverless","serverless","2026-07-12T08:43:13.27939",{"slug":985,"name":985,"fn":986,"description":987,"org":988,"tags":989,"stars":21,"repoUrl":22,"updatedAt":996},"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},[990,991,992,995],{"name":20,"slug":8,"type":15},{"name":975,"slug":976,"type":15},{"name":993,"slug":994,"type":15},"PostgreSQL","postgresql",{"name":981,"slug":982,"type":15},"2026-07-16T06:00:34.789624",{"slug":998,"name":998,"fn":999,"description":1000,"org":1001,"tags":1002,"stars":21,"repoUrl":22,"updatedAt":1008},"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},[1003,1004,1005],{"name":819,"slug":820,"type":15},{"name":20,"slug":8,"type":15},{"name":1006,"slug":1007,"type":15},"LLM","llm","2026-07-25T05:30:35.20899",{"slug":1010,"name":1010,"fn":1011,"description":1012,"org":1013,"tags":1014,"stars":21,"repoUrl":22,"updatedAt":1023},"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},[1015,1016,1017,1020],{"name":20,"slug":8,"type":15},{"name":975,"slug":976,"type":15},{"name":1018,"slug":1019,"type":15},"MongoDB","mongodb",{"name":1021,"slug":1022,"type":15},"NoSQL","nosql","2026-07-12T08:43:00.455878",{"slug":1025,"name":1025,"fn":1026,"description":1027,"org":1028,"tags":1029,"stars":21,"repoUrl":22,"updatedAt":1033},"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},[1030,1031,1032],{"name":20,"slug":8,"type":15},{"name":975,"slug":976,"type":15},{"name":1021,"slug":1022,"type":15},"2026-07-16T06:00:37.690386",115]