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.
Borrowing it
Nothing to install: this file belongs to aws-samples/sample-claude-code-multi-model. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aws-samples/sample-claude-code-multi-model/main/.claude/skills/summarize/SKILL.mdgit 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/skills/aws-samples/sample-claude-code-multi-model/summarize)<a href="https://agentmods.dev/skills/aws-samples/sample-claude-code-multi-model/summarize"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-claude-code-multi-model/summarize/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-samples/sample-claude-code-multi-model/summarize"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-claude-code-multi-model/summarize.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 | $0.00081 | $0.04456 |
| Opus 5.5 | $0.00032 | $0.01782 |
| Sonnet 5.5 | $0.00016 | $0.00891 |
| Haiku 4.5 | $0.00008 | $0.00446 |
Grade A, and why
summarize 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 · 375 lines · 81 tokens per session scan A df78f96a0b0c
summarize is a skill published in the GitHub repository aws-samples/sample-claude-code-multi-model (11 stars, last pushed 2mo ago), licensed MIT-0. It adds 81 tokens to every session and 4,456 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
demo-6-article-kg
Demo skill that web-fetches two Decoding AI knowledge-graph articles, has the agent itself distill them into a typed entity/relation graph, and renders an interactive dark-themed force-directed KG into one self-contained kg.html — no graph library, no CDN.
demo-5-sandbox-feature-pr
Demo skill for the meta "decode improves decode" flow — launch decode in a sandboxed clone of the course repo, plan and build one small feature inside the Workspace, let Hand-back push the Session Branch, then open a draft PR against the course repo.
demo-3-repo-pulse
Demo skill that pulls live GitHub API data for a repo, analyses a full year of weekly commit activity and top contributors, and renders a single-file dashboard.html with stat tiles and inline SVG charts — no chart library.
demo-1-terminal-arcade
Demo skill that builds a colorful, playable terminal Snake game in a single pure-stdlib Python curses file — score HUD, speed ramp, eat/death animations, and a game-over screen.
demo-4-review-swarm
Demo skill that fans out three parallel read-only Explore subagents to review three decode modules, then merges their findings into one severity-ranked verdict with health scores and a diagram per module, written to review-verdict.md.
commit
Stage the appropriate changes and commit them with a Conventional Commits message.