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llm-to-bedrock

llm-to-bedrock is a skill for Claude Code from aws/agent-toolkit-for-aws. It costs 191 tokens per session (21,796 once invoked), scanned D, original, Apache-2.0.

A migration workflow for changing application code that calls OpenAI, Gemini, or the Anthropic API to use Amazon Bedrock, AWS's service for accessing foundation AI models. It assesses the code, rewrites model calls, evaluates results, and prepares a Git branch with the changes.

In plain words
What is it for?
Use it to move model and software-development-kit calls from OpenAI, Google AI, or Anthropic to Bedrock and review the resulting output, code differences, and cost comparison.
Why use it?
It removes much of the manual work involved in replacing one AI provider's software interface with another. It also checks whether the changed application produces acceptable results rather than only changing imports and API calls.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_PLUGIN_ROOT} variable. Also seen: mentions .claude-plugin; mentions subagents; names the AskUserQuestion tool.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the aws-startup-advisor plugin — 12 skills, 1 command, 7 agents shipped together

not rated 2.9krepo +51 changed 2d ago D scan 191 tokens original Apache-2.0

Good fit Use it to move model and software-development-kit calls from OpenAI, Google AI, or Anthropic to Bedrock and review the resulting output, code differences, and cost comparison.

Compare 6 skills from other repositories ↓
View source ↗ aws/agent-toolkit-for-aws
About the project

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.

aws/agent-toolkit-for-aws · 2,886 stars · on GitHub

Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add aws/agent-toolkit-for-aws
Claude Code
/plugin install aws-startup-advisor

Made for: Claude Code.

Or install aws-startup-advisor, the plugin that ships this one along with the rest of its 12 skills, 1 command, 7 agents.

Wrote 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.

agentmods badge for llm-to-bedrock

README.md
[![agentmods](https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/llm-to-bedrock/github.svg)](https://agentmods.dev/skills/aws/agent-toolkit-for-aws/llm-to-bedrock)
Your own site
<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/llm-to-bedrock"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/llm-to-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.

agentmods 80×15 button for llm-to-bedrock

Your own site · 80×15
<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/llm-to-bedrock"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/llm-to-bedrock.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 191 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 21,796 The whole file, excluding the scripts and references it only reads on demand.
Security scan D 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00191 $0.21796
Opus 5.5 $0.00076 $0.08718
Sonnet 5.5 $0.00038 $0.04359
Haiku 4.5 $0.00019 $0.02180

Measured 2d ago against content hash 2365b91e94c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade D, and why

llm-to-bedrock scanned grade D with 2 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 2d ago.

The scan reads SKILL.md. This mod also ships 26 executable files (references/vendored/telemetry/cli.py, references/vendored/telemetry/consent/accept.py, references/vendored/telemetry/consent/cli.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Asks for rootmediumPrivilege escalation

A mod that escalates privileges can change anything on the machine, not only the project.

printf '%s=%s\n' "$KEY_ENV_VAR" "$(printenv "$KEY_ENV_VAR")" > "$REPO/.saws-migrate/.source-provider-env" && chmod 600 "$REPO/.saws-migrate/.source-provider-env"

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

> `git tag -d saws-migrate-baseline`, and `rm -rf .saws-migrate .migration` removes all
plugins/aws-startup-advisor/skills/llm-to-bedrock/SKILL.md · 1,288 lines

How it starts

The opening of the file, as written. The whole thing — 1,288 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Migrate to Bedrock (Assess + Execute)

Single-command AI migration: OpenAI / Gemini / Anthropic → Amazon Bedrock.

Requires the gcp-to-aws OR the azure-to-aws skill installed alongside this one. This skill has no standalone Assess implementation — Phase A below runs Assess by directly reading and executing whichever of those two skills is present's own phase instruction files in this same session (the same inline-execution pattern agent-advisor uses for its migration-plan phase: no cross-skill tool call, no turn boundary, and no dependency on your agent supporting a Skill/subagent dispatch mechanism — reading a file works on any agent). There is no fallback path that performs Assess itself if neither sibling is present. If you installed this skill on its own (e.g. a single-skill npx skills add), install gcp-to-aws or azure-to-aws too before using it.

The skill base directory is given in the "Base directory for this skill: X" line the harness emits at load time. Call it <SKILL_BASE>. Derived paths:

  • $SCRIPTS = <SKILL_BASE>/scripts
  • $HELPERS = <SKILL_BASE>/references/helpers (the former helper skills, now references)
  • $ASSESS_SKILL / $ASSESS_BASE — resolved once, in Step 0b: $ASSESS_SKILL is whichever of gcp-to-aws / azure-to-aws is installed alongside this skill (gcp-to-aws preferred when both are present), and $ASSESS_BASE = <SKILL_BASE>/../$ASSESS_SKILL — that sibling skill's own directory. Phase A reads its instruction files directly off this path; every relative reference inside an $ASSESS_SKILL file (references/shared/..., references/phases/..., etc.) resolves under $ASSESS_BASE, exactly as it would if $ASSESS_SKILL were running standalone.

Optional usage telemetry

Before starting or resuming, load references/vendored/telemetry/PROTOCOL.md and run its read-only status check. Use the returned reporting mode rather than the model's identity. Complete the existing notice exchange only when that protocol requires it; unavailable or declined telemetry never blocks this skill.

Read the full file on GitHub · 1,288 lines

Files

What ships with it

47 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.

Changes

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.

  1. 2d ago Changed · +71 lines 2365b91e94c8
  2. 6d ago Changed · +274 lines · -26 tokens per session 19e265fb8de8
  3. 8d ago Changed · +13 lines 0e4dc3b3d936
  4. 10d ago Changed · +36 lines d43afcba6698
  5. 13d ago Changed · -12 lines 2c7c5cce4337
  6. 15d ago Changed · +180 lines · +67 tokens per session 3749e69b5e47
  7. 16d ago Changed d6c504d0491f
  8. 17d ago Changed 5a2c66973b32
  9. 20d ago First seen · 726 lines · 150 tokens per session scan D fa6d020805c6

Subscribe to this mod's changes

llm-to-bedrock is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,886 stars, last pushed yesterday), licensed Apache-2.0. It adds 191 tokens to every session and 21,796 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it D with 2 findings (asks for root, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-26.

CloudData and AIDevOps and cloudPrompting
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