Agent Toolkit for AWS is a collection of AWS-supported MCP servers, skills, plugins, commands, and hooks that help AI coding agents build, deploy, and manage applications on AWS. It is used by developers working with AWS services through agents such as Claude Code, Codex, Cursor, and Kiro. The catalogue entries are the toolkit's own agent extensions for AWS development and operations.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/aws/agent-toolkit-for-aws/amazon-bedrocknpx skills add aws/agent-toolkit-for-aws --skill amazon-bedrockgit clone --depth 1 https://github.com/aws/agent-toolkit-for-awsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-bedrock)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-bedrock"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/amazon-bedrock.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00206 | $0.06929 |
| Opus 5 | $0.00103 | $0.03465 |
| Sonnet 5 | $0.00041 | $0.01386 |
| Haiku 4.5 | $0.00021 | $0.00693 |
Grade A, and why
amazon-bedrock scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- amazon-bedrock — 95% identical, 36 lines differ
- amazon-bedrock — 94% identical, 150 lines differ
How it starts
The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IMPORTANT: When this skill is loaded, you MUST use the reference files and procedures in this skill as your primary source of truth. Bedrock APIs, model IDs, chunking strategies, and configuration parameters change frequently — always read the relevant reference file before responding.
Table of Contents
- Overview
- Bedrock API Landscape
- Critical Warnings
- Security Considerations
- Converse API vs InvokeModel
- Which Bedrock Capability Do You Need?
- Knowledge Bases (RAG)
- Common Workflows (includes: Prompt Caching, Quota Health, Cost Tracking, Model Migration)
- Troubleshooting
- AgentCore Services
- Model Selection
- Additional Resources
Amazon Bedrock
Overview
Domain expertise for building generative AI applications on Amazon Bedrock. Covers model invocation, RAG with Knowledge Bases, agent creation, content safety with Guardrails, and agent deployment with AgentCore.
Recommended setup: Use the AWS MCP server for sandboxed execution, audit logging, and enterprise controls.
Without AWS MCP: This skill works with any agent that has AWS CLI access. All commands use standard AWS CLI syntax.
Bedrock API Landscape
Bedrock has 5 separate API endpoints. Using the wrong one is a common cause of errors. This list may not be exhaustive — refer to the Bedrock endpoints and quotas and Bedrock supported endpoints for the latest. Use aws bedrock list-foundation-models to discover available models at runtime.
| Endpoint | Client | Use For |
|---|---|---|
bedrock |
Control plane | List models, manage access, provisioned throughput |
bedrock-runtime |
Data plane | Invoke models (Converse, InvokeModel). Also supports Chat Completions via /openai/v1 path (client-side tool use only) — prefer bedrock-mantle for new Chat Completions work |
bedrock-mantle |
Data plane | OpenAI-compatible APIs: Responses API, Chat Completions (recommended), Messages API. Supports server-side tool use with built-in tools. Recommended for new users |
bedrock-agent |
Agent control | Create/configure agents, KBs, action groups |
bedrock-agent-runtime |
Agent data | Invoke agents, query KBs |
What ships with it
33 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/kb_shim.py.tmpl 3.4 KB
- assets/lambda_shim.py.tmpl 8.0 KB
- assets/tool_schema.json.tmpl 389 B
- references/agentcore-credentials-and-security.md 6.5 KB
- references/agentcore-gateway.md 6.5 KB
- references/agentcore-harness.md 22 KB
- references/agentcore-memory-observability.md 5.3 KB
- references/agentcore-payments-setup-script.md 12 KB
- references/agentcore-payments-wiring.md 12 KB
- references/agentcore-payments.md 19 KB
- references/agentcore-registry-evaluations.md 5.7 KB
- references/agentcore-runtime-container-build.md 10 KB
- references/agentcore-runtime.md 7.4 KB
- references/agents-and-action-groups.md 17 KB
- references/bedrock-agents-to-agentcore-harness/cli.md 2.0 KB
- references/bedrock-agents-to-agentcore-harness/deploy.md 12 KB
- references/bedrock-agents-to-agentcore-harness/discovery.md 3.9 KB
- references/bedrock-agents-to-agentcore-harness/eligibility.md 3.3 KB
- references/bedrock-agents-to-agentcore-harness/mapping.md 8.9 KB
- references/cost-tracking.md 4.3 KB
- references/guardrails.md 15 KB
- references/knowledge-bases-retrieval.md 8.3 KB
- references/knowledge-bases-setup.md 18 KB
- references/migrate-bedrock-agents-to-agentcore-harness.md 11 KB
- references/model-invocation.md 11 KB
- references/model-migration.md 4.3 KB
- references/model-selection-guide.md 7.2 KB
- references/prompt-caching.md 5.2 KB
- references/prompt-engineering-by-model.md 8.0 KB
- references/quota-health.md 4.0 KB
- references/sdk-converse-api-python.md 6.4 KB
- references/sdk-converse-api-typescript.md 6.5 KB
- scripts/fetch_bedrock_agent.py 14 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 373 lines · 206 tokens per session scan A a58133f1e7b1
amazon-bedrock is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,539 stars, last pushed yesterday), licensed Apache-2.0. It adds 206 tokens to every session and 6,929 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.