swe

An end-to-end software-engineering workflow for benchmarking how well a language model handles a problem from idea to design package.

In plain words
What is it for?
Use it to create a GitHub issue specification, low-level design, expert review, and related benchmark documentation for a software problem.
Why use it?
It gives a consistent structure for evaluating the quality of a model's engineering work instead of judging only the final answer.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/aws-samples/sample-claude-code-multi-model/swe
Any agent
npx skills add aws-samples/sample-claude-code-multi-model --skill swe
Clone the repo
git clone --depth 1 https://github.com/aws-samples/sample-claude-code-multi-model

Made for: Claude Code, Codex.

Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,749 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin unknown 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 $0.00100 $0.07749
Opus 5 $0.00050 $0.03875
Sonnet 5 $0.00020 $0.01550
Haiku 4.5 $0.00010 $0.00775

Measured 2d ago against content hash 3c1b34ac8e14, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

swe scanned grade A with 1 finding 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.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

| Functional Tests (CLI / curl) | Change adds/modifies any HTTP endpoint or CLI command |
.claude/skills/swe/SKILL.md · 730 lines

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.

Read it on GitHub

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 First seen · 730 lines · 100 tokens per session scan A 3c1b34ac8e14

Subscribe to this mod's changes

swe is a skill published in the GitHub repository aws-samples/sample-claude-code-multi-model (10 stars, last pushed 27d ago), licensed MIT-0. It adds 100 tokens to every session and 7,749 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.