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 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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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 | $0.00213 | $0.07890 |
| Opus 5.5 | $0.00085 | $0.03156 |
| Sonnet 5.5 | $0.00043 | $0.01578 |
| Haiku 4.5 | $0.00021 | $0.00789 |
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 3d 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
3 near-identical copies found in the catalogue:
- amazon-bedrock — 95% identical, 105 lines differ
- amazon-bedrock — 94% identical, 213 lines differ
- amazon-bedrock — 88% identical, 34 lines differ
How it starts
The opening of the file, as written. The whole thing — 398 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.
Guardrail — where this skill's own files live (MCP vs local install)
This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference or running a script:
- Loaded through the AWS MCP
retrieve_skilltool: The skill is not installed on the local filesystem. You MUST fetch each reference or script viaretrieve_skillwith thefileparameter (e.g.file="references/managed-knowledge-bases-setup.md"orfile="scripts/fetch_bedrock_agent.py"), and run the script from the returned content. Do NOTfile_readthese paths locally — they do not exist on disk. - Installed locally (e.g.
.kiro/skills/amazon-bedrock/or~/.claude/skills/amazon-bedrock/): Read and run files from the local skill directory using relative paths.
This distinction applies only to the skill's own packaged files. User data and
session artifacts are always read from and written to the user's working
directory. Never fetch or write customer data through retrieve_skill.
Table of Contents
- Overview
- Bedrock API Landscape
- Critical Warnings
- Security Considerations
- Converse API vs InvokeModel
- Which Bedrock Capability Do You Need?
- Knowledge Bases (retrieval, agentic retrieval and RAG; includes: migration to Managed KB)
- 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.
What ships with it
46 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 7.0 KB
- references/agentcore-harness.md 22 KB
- references/agentcore-memory-observability.md 5.6 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 6.9 KB
- references/agentcore-runtime-container-build.md 10 KB
- references/agentcore-runtime.md 7.7 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/bedrock-kb-migration/cost-analysis.md 53 KB
- references/bedrock-kb-migration/discovery.md 26 KB
- references/bedrock-kb-migration/iac-schema.md 25 KB
- references/bedrock-kb-migration/implement-iac.md 19 KB
- references/bedrock-kb-migration/implement-kb.md 30 KB
- references/bedrock-kb-migration/managed-kb-api.md 76 KB
- references/bedrock-kb-migration/mapping.md 60 KB
- references/bedrock-kb-migration/plan.md 19 KB
- references/bedrock-kb-migration/safety-and-iam.md 28 KB
- references/cost-tracking.md 4.5 KB
- references/guardrails.md 15 KB
- references/knowledge-bases-decision-guide.md 2.8 KB
- references/knowledge-bases-retrieval.md 9.2 KB
- references/knowledge-bases-setup.md 18 KB
- references/managed-knowledge-bases-retrieval.md 9.0 KB
- references/managed-knowledge-bases-setup.md 13 KB
- references/migrate-bedrock-agents-to-agentcore-harness.md 11 KB
- references/migrate-knowledge-base-to-managed.md 32 KB
- references/model-invocation.md 11 KB
- references/model-migration.md 4.3 KB
- references/model-selection-guide.md 7.4 KB
- references/prompt-caching.md 5.5 KB
- references/prompt-engineering-by-model.md 8.0 KB
- references/quota-health.md 4.3 KB
- references/sdk-converse-api-python.md 6.3 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.
- 3d ago Changed · +9 lines · -9 tokens per session 354f3a070e40
- 27d ago Changed · +16 lines · +16 tokens per session 4ab492890e6a
- 1mo 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 (3,299 stars, last pushed today), licensed Apache-2.0. It adds 213 tokens to every session and 7,890 once invoked, about $0.0009 per session on Opus 5.5. 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.
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