Claude Code Multi-Model is a sample project that connects Anthropic’s command-line coding agent to foundation models hosted on Amazon Bedrock or self-hosted on EC2, then evaluates their coding performance. It is intended for demonstration and learning, helping users compare models for software-engineering tasks and function-generation benchmarks. The catalogue add-ons support model selection and evaluation workflows.
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.
git clone --depth 1 https://github.com/aws-samples/sample-claude-code-multi-modelWrote 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/commands/aws-samples/sample-claude-code-multi-model/install)<a href="https://agentmods.dev/commands/aws-samples/sample-claude-code-multi-model/install"><img src="https://agentmods.dev/badge/commands/aws-samples/sample-claude-code-multi-model/install/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/commands/aws-samples/sample-claude-code-multi-model/install"><img src="https://agentmods.dev/badge/commands/aws-samples/sample-claude-code-multi-model/install.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00964 |
| Opus 5.5 | $0.00000 | $0.00386 |
| Sonnet 5.5 | $0.00000 | $0.00193 |
| Haiku 4.5 | $0.00000 | $0.00096 |
Grade A, and why
install 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 1mo 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 1mo ago First seen · 174 lines · 0 tokens per session scan F a054300e15ee
install is a command published in the GitHub repository aws-samples/sample-claude-code-multi-model (11 stars, last pushed 2mo ago), licensed MIT-0. It costs nothing until one of its globs matches a file; then it loads 964 tokens. 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 2026-08-31.
Other commands, from other repositories
eval
Run the RAG evaluation pipeline.
ai-do
You are a routing assistant for DSPy AI skills. Understand the user's problem, pick the best skill, and generate a ready-to-run prompt.
flow-nexus-neural
Train and deploy neural networks in distributed sandboxes.
neural-train
Command "neural-train" from airmcp-com/mcp-standards, covering neural-train, usage, options, examples and train from recent ops.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.