[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-agentcore-patterns":3,"mdc--6tueou-key":34,"related-org-aws-labs-agentcore-patterns":241,"related-repo-aws-labs-agentcore-patterns":418},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"agentcore-patterns","manage agent identity and attribution","This skill should be used when an automated agent's output is published under its own identity and carries weight in someone else's work, such as a reviewer whose verdict lands on a pull request, and the open question is how much authority it has actually earned. Covers which conclusions such an agent may state as settled and which it must hand to a person, why a verdict that moves between runs on unchanged input cannot be allowed to decide anything, handing an uncertain item to a human rather than discarding it, grounding each claim in something computed rather than recalled, and the published-state and recall behaviour that decides whether people keep reading the output or learn to skim it. It should also be used when such an agent is being ignored, contradicts itself between runs, or reports a state that nothing current supports. Not for measuring or improving an agent's own output quality, which belongs to aws-agents:agents-optimize, nor for building, deploying, or hardening an agent.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"aws-labs","AWS Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws-labs.png","awslabs",[13,17,20],{"name":14,"slug":15,"type":16},"Best Practices","best-practices","tag",{"name":18,"slug":19,"type":16},"Agents","agents",{"name":21,"slug":22,"type":16},"Engineering","engineering",16,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fstartups","2026-09-02T07:48:14.446305","Apache-2.0",27,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Official AWS Startups repository that hosts plugins, skills, tools and resources to support startup builders on AWS","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fstartups\u002Ftree\u002FHEAD\u002Fsolution-architecture\u002Fplugins\u002Faws-startups-solution-architecture\u002Fskills\u002Fagentcore-patterns","---\nname: agentcore-patterns\ndescription: \"This skill should be used when an automated agent's output is published under its own identity and carries weight in someone else's work, such as a reviewer whose verdict lands on a pull request, and the open question is how much authority it has actually earned. Covers which conclusions such an agent may state as settled and which it must hand to a person, why a verdict that moves between runs on unchanged input cannot be allowed to decide anything, handing an uncertain item to a human rather than discarding it, grounding each claim in something computed rather than recalled, and the published-state and recall behaviour that decides whether people keep reading the output or learn to skim it. It should also be used when such an agent is being ignored, contradicts itself between runs, or reports a state that nothing current supports. Not for measuring or improving an agent's own output quality, which belongs to aws-agents:agents-optimize, nor for building, deploying, or hardening an agent.\"\nlicense: Apache-2.0\nmetadata:\n  audience: startup\n---\n\n# AgentCore Patterns\n\nApply these patterns to agents that act inside an engineering pipeline rather than serving end users. Upstream `aws-agents` owns the build, deploy, and hardening mechanics. What it does not cover is the judgment layer, which is where these agents actually fail.\n\nTreat one constraint as primary: nobody is available to babysit the agent. A judge that is wrong, slow, or noisy will not be tuned over a quarter by a platform team. It gets ignored, and then it is worse than nothing, because it occupies the slot where review attention used to be.\n\nDesign for that outcome. Measure stability before granting an agent authority to block, report a stance on every axis so silence is never ambiguous, and compute in code anything the judgment depends on.\n\n## Reference files\n\n- **`references\u002Fgit-code-reviewer-agent.md`**: Read before letting any agent's output\n  carry weight in someone else's work. End-to-end wiring for a pull-request reviewer,\n  from the credential identity that decides whether a verdict can be recorded at all,\n  through reading the pull request as data, the trigger that reaches a credentialed\n  runtime, what the agent should remember between runs, and the judgment design that\n  decides whether anyone keeps reading the output. Written as field notes, including\n  the failures.\n\n## Upstream skills to defer to\n\nDo not restate the mechanics these own. Invoke them directly:\n\n- **`Skill(\"aws-agents:agents-build\")`**: Agent construction, tools, prompts, memory, and multi-agent composition.\n- **`Skill(\"aws-agents:agents-deploy\")`**: Container contract, deployment, versioning, rollback, and deploy-failure diagnosis.\n- **`Skill(\"aws-agents:agents-harden\")`**: IAM scoping, inbound auth, secret handling, session lifecycle, and quotas.\n- **`Skill(\"aws-agents:agents-debug\")`**: Traces, logs, and diagnosis of a deployed agent.\n- **`Skill(\"aws-agents:agents-optimize\")`**: Evaluators, online monitoring, CI\u002FCD quality gates, observability, and latency and token-cost tuning. **Read this first if the goal is to measure an agent's quality.** It owns the evaluator and quality-gate machinery; this skill covers only the narrower case where the agent _is_ the gate, and the question is whether its verdicts are stable enough to carry authority.\n- **`Skill(\"aws-core:amazon-bedrock\")`**: Model invocation, prompt caching, and throttling diagnosis.\n- **`Skill(\"aws-core:aws-ai-ml\")`**: Model selection and inference-cost comparison.\n\nFor runtime selection before any of this applies, such as AgentCore against Lambda or ECS, use `Skill(\"aws-startup-advisor:agent-advisor\")`.\n",{"data":35,"body":38},{"name":4,"description":6,"license":26,"metadata":36},{"audience":37},"startup",{"type":39,"children":40},"root",[41,49,64,69,74,81,101,107,112,228],{"type":42,"tag":43,"props":44,"children":45},"element","h1",{"id":4},[46],{"type":47,"value":48},"text","AgentCore Patterns",{"type":42,"tag":50,"props":51,"children":52},"p",{},[53,55,62],{"type":47,"value":54},"Apply these patterns to agents that act inside an engineering pipeline rather than serving end users. Upstream ",{"type":42,"tag":56,"props":57,"children":59},"code",{"className":58},[],[60],{"type":47,"value":61},"aws-agents",{"type":47,"value":63}," owns the build, deploy, and hardening mechanics. What it does not cover is the judgment layer, which is where these agents actually fail.",{"type":42,"tag":50,"props":65,"children":66},{},[67],{"type":47,"value":68},"Treat one constraint as primary: nobody is available to babysit the agent. A judge that is wrong, slow, or noisy will not be tuned over a quarter by a platform team. It gets ignored, and then it is worse than nothing, because it occupies the slot where review attention used to be.",{"type":42,"tag":50,"props":70,"children":71},{},[72],{"type":47,"value":73},"Design for that outcome. Measure stability before granting an agent authority to block, report a stance on every axis so silence is never ambiguous, and compute in code anything the judgment depends on.",{"type":42,"tag":75,"props":76,"children":78},"h2",{"id":77},"reference-files",[79],{"type":47,"value":80},"Reference files",{"type":42,"tag":82,"props":83,"children":84},"ul",{},[85],{"type":42,"tag":86,"props":87,"children":88},"li",{},[89,99],{"type":42,"tag":90,"props":91,"children":92},"strong",{},[93],{"type":42,"tag":56,"props":94,"children":96},{"className":95},[],[97],{"type":47,"value":98},"references\u002Fgit-code-reviewer-agent.md",{"type":47,"value":100},": Read before letting any agent's output\ncarry weight in someone else's work. End-to-end wiring for a pull-request reviewer,\nfrom the credential identity that decides whether a verdict can be recorded at all,\nthrough reading the pull request as data, the trigger that reaches a credentialed\nruntime, what the agent should remember between runs, and the judgment design that\ndecides whether anyone keeps reading the output. Written as field notes, including\nthe failures.",{"type":42,"tag":75,"props":102,"children":104},{"id":103},"upstream-skills-to-defer-to",[105],{"type":47,"value":106},"Upstream skills to defer to",{"type":42,"tag":50,"props":108,"children":109},{},[110],{"type":47,"value":111},"Do not restate the mechanics these own. Invoke them directly:",{"type":42,"tag":82,"props":113,"children":114},{},[115,129,143,157,171,200,214],{"type":42,"tag":86,"props":116,"children":117},{},[118,127],{"type":42,"tag":90,"props":119,"children":120},{},[121],{"type":42,"tag":56,"props":122,"children":124},{"className":123},[],[125],{"type":47,"value":126},"Skill(\"aws-agents:agents-build\")",{"type":47,"value":128},": Agent construction, tools, prompts, memory, and multi-agent composition.",{"type":42,"tag":86,"props":130,"children":131},{},[132,141],{"type":42,"tag":90,"props":133,"children":134},{},[135],{"type":42,"tag":56,"props":136,"children":138},{"className":137},[],[139],{"type":47,"value":140},"Skill(\"aws-agents:agents-deploy\")",{"type":47,"value":142},": Container contract, deployment, versioning, rollback, and deploy-failure diagnosis.",{"type":42,"tag":86,"props":144,"children":145},{},[146,155],{"type":42,"tag":90,"props":147,"children":148},{},[149],{"type":42,"tag":56,"props":150,"children":152},{"className":151},[],[153],{"type":47,"value":154},"Skill(\"aws-agents:agents-harden\")",{"type":47,"value":156},": IAM scoping, inbound auth, secret handling, session lifecycle, and quotas.",{"type":42,"tag":86,"props":158,"children":159},{},[160,169],{"type":42,"tag":90,"props":161,"children":162},{},[163],{"type":42,"tag":56,"props":164,"children":166},{"className":165},[],[167],{"type":47,"value":168},"Skill(\"aws-agents:agents-debug\")",{"type":47,"value":170},": Traces, logs, and diagnosis of a deployed agent.",{"type":42,"tag":86,"props":172,"children":173},{},[174,183,185,190,192,198],{"type":42,"tag":90,"props":175,"children":176},{},[177],{"type":42,"tag":56,"props":178,"children":180},{"className":179},[],[181],{"type":47,"value":182},"Skill(\"aws-agents:agents-optimize\")",{"type":47,"value":184},": Evaluators, online monitoring, CI\u002FCD quality gates, observability, and latency and token-cost tuning. ",{"type":42,"tag":90,"props":186,"children":187},{},[188],{"type":47,"value":189},"Read this first if the goal is to measure an agent's quality.",{"type":47,"value":191}," It owns the evaluator and quality-gate machinery; this skill covers only the narrower case where the agent ",{"type":42,"tag":193,"props":194,"children":195},"em",{},[196],{"type":47,"value":197},"is",{"type":47,"value":199}," the gate, and the question is whether its verdicts are stable enough to carry authority.",{"type":42,"tag":86,"props":201,"children":202},{},[203,212],{"type":42,"tag":90,"props":204,"children":205},{},[206],{"type":42,"tag":56,"props":207,"children":209},{"className":208},[],[210],{"type":47,"value":211},"Skill(\"aws-core:amazon-bedrock\")",{"type":47,"value":213},": Model invocation, prompt caching, and throttling diagnosis.",{"type":42,"tag":86,"props":215,"children":216},{},[217,226],{"type":42,"tag":90,"props":218,"children":219},{},[220],{"type":42,"tag":56,"props":221,"children":223},{"className":222},[],[224],{"type":47,"value":225},"Skill(\"aws-core:aws-ai-ml\")",{"type":47,"value":227},": Model selection and inference-cost comparison.",{"type":42,"tag":50,"props":229,"children":230},{},[231,233,239],{"type":47,"value":232},"For runtime selection before any of this applies, such as AgentCore against Lambda or ECS, use ",{"type":42,"tag":56,"props":234,"children":236},{"className":235},[],[237],{"type":47,"value":238},"Skill(\"aws-startup-advisor:agent-advisor\")",{"type":47,"value":240},".",{"items":242,"total":417},[243,264,285,295,308,321,331,341,357,368,388,404],{"slug":244,"name":244,"fn":245,"description":246,"org":247,"tags":248,"stars":261,"repoUrl":262,"updatedAt":263},"agentcore-investigation","investigate Bedrock AgentCore runtime sessions","Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session\u002Ftrace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[249,252,255,258],{"name":250,"slug":251,"type":16},"AWS","aws",{"name":253,"slug":254,"type":16},"Debugging","debugging",{"name":256,"slug":257,"type":16},"Logs","logs",{"name":259,"slug":260,"type":16},"Observability","observability",9645,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":265,"name":266,"fn":267,"description":268,"org":269,"tags":270,"stars":261,"repoUrl":262,"updatedAt":284},"amazon-aurora-dsql","amazon aurora dsql","build applications with Aurora DSQL","Deprecated compatibility redirect for Aurora DSQL guidance. Use when a request concerns DSQL, Aurora DSQL, distributed SQL, DSQL schemas, migrations, queries, authentication, performance, or application development.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[271,274,275,278,281],{"name":272,"slug":273,"type":16},"Aurora","aurora",{"name":250,"slug":251,"type":16},{"name":276,"slug":277,"type":16},"Database","database",{"name":279,"slug":280,"type":16},"Serverless","serverless",{"name":282,"slug":283,"type":16},"SQL","sql","2026-09-02T07:20:51.53702",{"slug":286,"name":287,"fn":267,"description":268,"org":288,"tags":289,"stars":261,"repoUrl":262,"updatedAt":294},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[290,291,292,293],{"name":250,"slug":251,"type":16},{"name":276,"slug":277,"type":16},{"name":279,"slug":280,"type":16},{"name":282,"slug":283,"type":16},"2026-09-02T07:20:46.533217",{"slug":296,"name":297,"fn":267,"description":268,"org":298,"tags":299,"stars":261,"repoUrl":262,"updatedAt":307},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[300,301,302,305,306],{"name":250,"slug":251,"type":16},{"name":276,"slug":277,"type":16},{"name":303,"slug":304,"type":16},"Migration","migration",{"name":279,"slug":280,"type":16},{"name":282,"slug":283,"type":16},"2026-09-02T07:20:49.531712",{"slug":309,"name":310,"fn":267,"description":268,"org":311,"tags":312,"stars":261,"repoUrl":262,"updatedAt":320},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[313,314,315,318,319],{"name":250,"slug":251,"type":16},{"name":276,"slug":277,"type":16},{"name":316,"slug":317,"type":16},"PostgreSQL","postgresql",{"name":279,"slug":280,"type":16},{"name":282,"slug":283,"type":16},"2026-09-02T07:20:47.592534",{"slug":322,"name":323,"fn":267,"description":268,"org":324,"tags":325,"stars":261,"repoUrl":262,"updatedAt":330},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[326,327,328,329],{"name":250,"slug":251,"type":16},{"name":276,"slug":277,"type":16},{"name":279,"slug":280,"type":16},{"name":282,"slug":283,"type":16},"2026-09-02T07:20:50.520015",{"slug":332,"name":332,"fn":267,"description":268,"org":333,"tags":334,"stars":261,"repoUrl":262,"updatedAt":340},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[335,336,337,338,339],{"name":250,"slug":251,"type":16},{"name":276,"slug":277,"type":16},{"name":303,"slug":304,"type":16},{"name":279,"slug":280,"type":16},{"name":282,"slug":283,"type":16},"2026-09-02T07:20:48.570617",{"slug":342,"name":342,"fn":343,"description":344,"org":345,"tags":346,"stars":354,"repoUrl":355,"updatedAt":356},"aidlc","orchestrate AI-driven development lifecycle workflows","AI-DLC workflow orchestrator. Start, resume, or manage an AI-driven development lifecycle. Scopes are defined one file per scope under `.kiro\u002Fscopes\u002F`; run `bun .kiro\u002Ftools\u002Faidlc-utility.ts help` for the authoritative list and descriptions. Utilities: --status, --doctor, --stage, --phase, --scope, --depth, --test-strategy, --review, --version, --help, plus the intent and space verbs. Or describe what you want to build and the scope will be auto-detected.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[347,348,351],{"name":18,"slug":19,"type":16},{"name":349,"slug":350,"type":16},"Automation","automation",{"name":352,"slug":353,"type":16},"Workflow","workflow",4261,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Faidlc-workflows","2026-09-02T07:47:34.75815",{"slug":358,"name":358,"fn":359,"description":360,"org":361,"tags":362,"stars":354,"repoUrl":355,"updatedAt":367},"aidlc-jump","navigate AI-DLC workflow stages and phases","Jump the active AI-DLC workflow to a stage or phase. A Cursor-native shortcut for `\u002Faidlc --stage \u003Ctarget>` or `\u002Faidlc --phase \u003Ctarget>`.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[363,364],{"name":349,"slug":350,"type":16},{"name":365,"slug":366,"type":16},"Navigation","navigation","2026-09-02T07:48:09.505949",{"slug":369,"name":369,"fn":370,"description":371,"org":372,"tags":373,"stars":354,"repoUrl":355,"updatedAt":387},"aidlc-knowledge","index documents for AI-DLC agent citation","Index the team's own documents — PDFs, Word files, Markdown, plain text — into a per-space catalog the AI-DLC agents can cite. Wraps `aidlc-knowledge.ts`: onboard, sync, list, show, associate, dissociate, rebind, summarize. Every catalog row is written by the tool under a workspace lock; this skill never edits the catalog by hand and never advances workflow state.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[374,375,378,381,384],{"name":18,"slug":19,"type":16},{"name":376,"slug":377,"type":16},"Documents","documents",{"name":379,"slug":380,"type":16},"Knowledge Management","knowledge-management",{"name":382,"slug":383,"type":16},"Markdown","markdown",{"name":385,"slug":386,"type":16},"PDF","pdf","2026-09-02T07:48:10.614775",{"slug":389,"name":389,"fn":390,"description":391,"org":392,"tags":393,"stars":354,"repoUrl":355,"updatedAt":403},"aidlc-outcomes-pack","generate AI-DLC workflow handover documentation","Generate a comprehensive handover document at workflow close so the team can own, operate, and continue the system without re-running the workflow. Stage\u002Fphase\u002Flearning counts come from `aidlc-runtime.ts summary`; prose comes from the artefacts. Writes OUTCOMES.md but never mutates workflow state or emits audit events.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[394,397,400],{"name":395,"slug":396,"type":16},"Documentation","documentation",{"name":398,"slug":399,"type":16},"Process Documentation","process-documentation",{"name":401,"slug":402,"type":16},"Reporting","reporting","2026-09-02T07:47:34.212738",{"slug":405,"name":405,"fn":406,"description":407,"org":408,"tags":409,"stars":354,"repoUrl":355,"updatedAt":416},"aidlc-replay","generate AI-DLC session narrative reports","Print a structured session narrative for stakeholders who weren't in the room. Numbers (stage counts, phase rollup, duration) come from `aidlc-runtime.ts summary`; prose comes from the audit trail and artefacts. Renders to the terminal only — writes no file, never mutates workflow state, never emits audit events.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[410,413,414,415],{"name":411,"slug":412,"type":16},"Audit","audit",{"name":395,"slug":396,"type":16},{"name":21,"slug":22,"type":16},{"name":401,"slug":402,"type":16},"2026-09-02T07:47:45.055994",142,{"items":419,"total":514},[420,433,439,453,468,481,496],{"slug":421,"name":421,"fn":422,"description":423,"org":424,"tags":425,"stars":23,"repoUrl":24,"updatedAt":432},"agent-advisor","plan and build AWS AI agents","Unified entry point for AI-agent work on AWS: evaluate and pick a runtime, generate a full migration plan (for existing workloads), and build an executable POC — all in one flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move my agents to AWS, migrate my agents to AWS with a plan, agent migration plan, add AgentCore services, add memory\u002Fgateway\u002Fidentity\u002Fpolicy to my agent, enable AgentCore Memory, add observability to my agent, I'm already on AWS and want to add agent capabilities, migrate Temporal workers to AWS, Temporal to AWS, run Temporal on AWS, Temporal workers on AWS, we use Temporal and want to move to AWS, our service is orchestrated by Temporal, what do I build on AWS for my Temporal workers, move a Temporal-based service to AWS, Temporal Cloud or self-hosted on AWS. Runs a phased flow: Intake (entry point + technical background), Discover (lightweight code detection), Clarify (adaptive questions), deterministic scoring, Design (runtime + deployment model + services + model), Estimate (coarse cost), Generate (layered recommendation doc + scaffolding), then optional gated stages: Migration Plan (full plan generated in-skill by reusing this plugin's gcp-to-aws engine, with the advisor's decisions carried over) and POC (deployment plan + deployable proof-of-concept on the recommended runtime — AgentCore, ECS, EKS, or Lambda; generated deliverables by default, or assisted build in your account on explicit opt-in). Systems with several workloads (interacting or independent agents, batch jobs, services) are decomposed into workload units, each getting its own verdict with a consolidation option. An add-capabilities branch (for teams already running agents on AWS) recommends which AgentCore services to enable on any runtime — no runtime scoring. Temporal systems dissolve into the same unit flow — worker polling tiers and Activity execution classes become units (rules in the Temporal decision reference); Workflow orchestration code is never rewritten — never a Step Functions translation. Requires at least one agentic component: a purely non-agent system (only plain services \u002F batch jobs \u002F HTTP endpoints, or a Temporal worker whose Activities are all non-agent) is out of scope — Clarify halts it (scope gate) and points to gcp-to-aws \u002F heroku-to-aws \u002F llm-to-bedrock. Not for: pure compute\u002Fdata migration with no AI agent; pure LLM SDK rewrite without agent architecture (use llm-to-bedrock); or detailed per-model pricing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[426,427,430,431],{"name":18,"slug":19,"type":16},{"name":428,"slug":429,"type":16},"AI Infrastructure","ai-infrastructure",{"name":250,"slug":251,"type":16},{"name":21,"slug":22,"type":16},"2026-08-23T03:33:24.455574",{"slug":4,"name":4,"fn":5,"description":6,"org":434,"tags":435,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[436,437,438],{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":21,"slug":22,"type":16},{"slug":440,"name":440,"fn":441,"description":442,"org":443,"tags":444,"stars":23,"repoUrl":24,"updatedAt":452},"architect-for-startups","advise on AWS architecture for startups","Startup-tailored AWS architecture advice that adjusts recommendations to the company's stage (pre-revenue through Series B+), team size, runway, and available credits. Use when a founder wants guidance or a recommendation rather than code changes: which services to choose, how to plan or review an architecture, how to stretch credits and control cost, or how to prepare architecture for a fundraise or technical diligence. For an interactive discovery flow that scaffolds and writes the architecture into the codebase, use start-building-for-startups. For AI-agent runtime selection or agentic architecture recommendations specifically, use agent-advisor. Do not use for: writing or scaffolding code, factual AWS Activate \u002F programs \u002F credits lookups (see knowledge-base-for-startups), a single copy-paste prompt (see prompt-library-for-startups), or migration intent such as GCP-to-AWS or Heroku-to-AWS (see the migration skills: `gcp-to-aws`, `heroku-to-aws`, `llm-to-bedrock`).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[445,448,449],{"name":446,"slug":447,"type":16},"Architecture","architecture",{"name":250,"slug":251,"type":16},{"name":450,"slug":451,"type":16},"Strategy","strategy","2026-08-20T03:27:01.975773",{"slug":454,"name":454,"fn":455,"description":456,"org":457,"tags":458,"stars":23,"repoUrl":24,"updatedAt":467},"gcp-to-aws","migrate workloads from GCP to AWS","Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, migrate App Engine to Elastic Beanstalk, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency\u002Fquality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[459,460,463,466],{"name":250,"slug":251,"type":16},{"name":461,"slug":462,"type":16},"Google Cloud","google-cloud",{"name":464,"slug":465,"type":16},"Infrastructure","infrastructure",{"name":303,"slug":304,"type":16},"2026-08-27T13:05:14.908471",{"slug":469,"name":469,"fn":470,"description":471,"org":472,"tags":473,"stars":23,"repoUrl":24,"updatedAt":480},"heroku-to-aws","migrate workloads from Heroku to AWS","Migrate workloads from Heroku to AWS. Triggers on: migrate from Heroku, Heroku to AWS, move off Heroku, migrate Heroku app, migrate Heroku Postgres to RDS, migrate Heroku Redis to ElastiCache, migrate Heroku Kafka to MSK, migrate dynos to Elastic Beanstalk, migrate dynos to Fargate, Heroku migration, move from Heroku to AWS, migrate Heroku Private Space, Heroku to Elastic Beanstalk, Heroku to ECS, Heroku to Fargate, leave Heroku, migrate off Heroku platform, what-if workshop, reprice Heroku migration, compare migration scenarios, workshop mode. Runs a 6-phase process: discover Heroku resources live via the authenticated Heroku CLI (read-only, consent-gated) and\u002For from Terraform files, Procfile\u002Fapp.json, and optional billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. After Estimate, an optional what-if workshop can reprice region\u002FHA\u002Fcompute\u002FGraviton scenarios without re-discovery. Clarify must finish before Design, Estimate, or Generate. Uses a flat resource model (no clustering or dependency graphs) with deterministic mapping tables for core services (Dynos → Elastic Beanstalk by default, Postgres → RDS\u002FAurora, Redis → ElastiCache, Kafka → MSK) and a fast-path table for 13+ common add-ons. Cedar\u002FFir generation detection is detect-only in v1. Pipeline\u002FReview Apps are detect-only. Do not use for: GCP or Azure migrations to AWS, AWS-to-Heroku reverse migration, general AWS architecture advice without migration intent, Heroku-to-Heroku refactoring, or multi-cloud deployments that do not involve migrating off Heroku.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[474,475,478,479],{"name":250,"slug":251,"type":16},{"name":476,"slug":477,"type":16},"Heroku","heroku",{"name":464,"slug":465,"type":16},{"name":303,"slug":304,"type":16},"2026-08-27T13:05:14.188572",{"slug":482,"name":482,"fn":483,"description":484,"org":485,"tags":486,"stars":23,"repoUrl":24,"updatedAt":495},"knowledge-base-for-startups","retrieve AWS startup reference content","AWS Startups reference content — Activate FAQ, credits guide, programs, partner offers, sample architectures, and hundreds of learn articles spanning generative AI, cloud architecture, cost optimization, security, fundraising, go-to-market, and real-world startup case studies. Use when the user asks factual questions about AWS Activate (eligibility, credits, programs, providers), wants a sample architecture or solution guide, or needs an AWS-curated learn article on a specific startup topic. For copy-paste AI prompts (RAG chatbot, MVP scaffold, security baseline, GPU quota, etc.), see the prompt-library-for-startups skill. Do not use for: account-specific lookups (credits balance, Activate membership status, application status), real-time event listings beyond the events stub, or content not present in the bundled `references\u002F` tree.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[487,488,491,492],{"name":250,"slug":251,"type":16},{"name":489,"slug":490,"type":16},"Cloud","cloud",{"name":395,"slug":396,"type":16},{"name":493,"slug":494,"type":16},"Research","research","2026-08-23T03:33:23.478898",{"slug":497,"name":497,"fn":498,"description":499,"org":500,"tags":501,"stars":23,"repoUrl":24,"updatedAt":513},"llm-to-bedrock","migrate LLM calls to Amazon Bedrock","Use when the user wants to migrate code that calls OpenAI, Gemini\u002FGoogle AI, or the Anthropic API to Amazon Bedrock — a pure model\u002FSDK rewrite. End-to-end: assesses the codebase, then rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Not for: agent runtime selection, agentic architecture decisions, or agent migration planning — use agent-advisor for those. Not for standalone Bedrock cost estimates or infrastructure-only migration. The Assess phase is handled by this plugin's own gcp-to-aws skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[502,503,506,509,510],{"name":250,"slug":251,"type":16},{"name":504,"slug":505,"type":16},"Gemini","gemini",{"name":507,"slug":508,"type":16},"LLM","llm",{"name":303,"slug":304,"type":16},{"name":511,"slug":512,"type":16},"OpenAI","openai","2026-09-02T07:20:20.868971",11]