implement

A workflow for implementing software from a detailed low-level design (LLD) specification in the current repository.

In plain words
What is it for?
Use it to find the best LLD, implement the specified change, produce a unified diff, and record token, cache, timing, and tool-use information.
Why use it?
It connects the chosen design to actual code changes and records the resulting diff and execution metrics, making the implementation easier to review.

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

Made for: Claude Code, Codex.

Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,653 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00095 $0.01653
Opus 5 $0.00048 $0.00826
Sonnet 5 $0.00019 $0.00331
Haiku 4.5 $0.00010 $0.00165

Measured yesterday against content hash 830628f6b22d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

implement 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 yesterday.

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.

.claude/skills/implement/SKILL.md · 208 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. yesterday First seen · 208 lines · 95 tokens per session scan A 830628f6b22d

Subscribe to this mod's changes

implement 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 95 tokens to every session and 1,653 once invoked, about $0.0005 per session on Opus 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.