llm-to-bedrock

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/gateway/identity/policy 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 polli

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 / programs / 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, Azure-to-AWS, or Heroku-to-AWS (see the migration skills: `gcp-to-aws`, `azure-to-aws`, `heroku-to-aws`, `llm-to-bedrock`).

Migrate workloads from Microsoft Azure to AWS. Triggers on: migrate from Azure, Azure to AWS, move off Azure, migrate Azure app to AWS, migrate AKS to EKS, migrate App Service to AWS, migrate App Service to Elastic Beanstalk, migrate Azure VMs to EC2, migrate Azure SQL to RDS, migrate Azure Database for PostgreSQL to RDS, migrate Azure Database for MySQL to RDS, migrate Cosmos DB to DynamoDB, migrate Azure Cache for Redis to ElastiCache, migrate Blob Storage to S3, migrate Azure Functions to Lambda, migrate Service Bus to SQS, migrate Event Hubs to Kinesis or MSK, migrate Azure OpenAI to Bedrock, migrate my Azure OpenAI app to AWS, move Azure AI workloads to AWS, migrate Azure-hosted agentic workloads to AWS, migrate Azure-hosted LangChain to Bedrock, migrate Azure-hosted LangGraph to AWS, leave Azure, estimate AWS costs for my Azure infrastructure, what-if workshop, reprice Azure migration, compare migration scenarios, workshop mode. Runs a 7-phase process: discover Azure resources from Terraform (Bicep and ARM template extraction, and a read-only consent-gated live `az` CLI capture, are advertised triggers whose extractors are not yet on this branch — a Bicep/ARM/live-only workspace halts rather than guessing; they land as sequenced follow-ups), application code, and optional billing exports; clarify migration requirements via an assumption sheet; design AWS architecture; estimate costs (both a 1:1 lift and a right-sized target); optionally reprice what-if scenarios in a workshop sidebar; generate migration artifacts when the user opts in; and collect optional feedback. Clarify must finish before Design, Estimate, or Generate, and Generate is opt-in — it runs only after the user chooses Execute at the post-Estimate decision gate. Uses resource-group-seeded clustering refined by typed edges from ARM resource IDs, canonical `Microsoft.*` ARM resource types as the mapping key, a deterministic fast-path table for architecture-invariant primitives, and a pattern catalo

Surfaces a single, genuinely relevant AWS Activate partner offer as optional context AFTER another AWS Startup Advisor skill has finalized its recommendation, plan, or build — never influencing the technical advice. Consulted by architect-for-startups, start-building-for-startups, agent-advisor, gcp-to-aws, heroku-to-aws, and llm-to-bedrock: once their output is final, this skill checks whether a relevant Activate offer exists for what the founder is building or migrating and, if so, appends one quiet, dismissible line with a tracked redeem link. Merit-first (offers never change the recommendation), relevance-gated to what the founder actually surfaced, at most one per response and often none, muteable. Offer content is read live from the sibling knowledge-base-for-startups skill (references/offers.md). Do NOT use for account-specific lookups (credits balance, Activate membership, application status) — send those to https://aws.amazon.com/startups.

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/quality 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 migrations to AWS (see azure-to-aws), 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.

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/or from Terraform files, Procfile/app.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/HA/compute/Graviton 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/Aurora, Redis → ElastiCache, Kafka → MSK) and a fast-path table for 13+ common add-ons. Cedar/Fir generation detection is detect-only in v1. Pipeline/Review Apps are detect-only. Do not use for: GCP migrations to AWS (see gcp-to-aws), Azure migrations to AWS (see azure-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.

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/` tree.

AWS-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly detection, GPU quota requests, EKS deployment, Well-Architected review, etc.) plus downloadable installable agents (Multi-Account Transition Advisor, Bill Shock Preventer, Service Quota Agent, Bedrock Model Availability Agent, AWS DB Advisor). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management / Bedrock model availability / database selection, or asks how to use AWS prompts. For migration intent (GCP to AWS, Azure to AWS, OpenAI/Gemini to Bedrock), route to the migration skills in this plugin (`gcp-to-aws`, `azure-to-aws`, `heroku-to-aws`, `llm-to-bedrock`). Do not use for: factual AWS Activate / programs / credits questions, learn articles, sample architectures, or for prompts that are not in the bundled `references/prompt-library/` tree.

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

Set up Claude Code hooks to block dangerous git commands (push, reset --hard, clean, branch -D, etc.) before they execute. Use when user wants to prevent destructive git operations, add git safety hooks, or block git push/reset in Claude Code.

Wire dependency-cruiser into a TypeScript repo so each package is a deep module, with implementation hidden in subfolders and reachable only through its entry-point files. User-invoked.

Conduct a retrospective on a coding session.

Use when completing tasks, implementing major features, or before merging to verify work meets requirements