amazon

amazon is a skill for Claude Code, Codex from vellum-ai/vellum-assistant. It costs 11 tokens per session (1,103 once invoked), scanned A, original, MIT.

A browser-based shopping tool for Amazon and Amazon Fresh, Amazon’s grocery delivery service.

In plain words
What is it for?
Use it to find Amazon products, select variants, add items to a cart, review purchases, and choose a delivery slot for Amazon Fresh.
Why use it?
It reduces the work of searching for products, choosing options, reviewing a cart, and moving through checkout. Orders still require confirmation before they are submitted.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to find Amazon products, select variants, add items to a cart, review purchases, and choose a delivery slot for Amazon Fresh.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vellum-ai/vellum-assistant/amazon
About the project

Vellum Assistant is a personal AI assistant that remembers information about users, learns their preferences, and takes actions across connected apps. It is intended for people who want an assistant that can manage conversations, unfinished work, and proactive notifications over time. The catalogue skills, hooks, instruction, and setting configure or extend how the assistant works.

vellum-ai/vellum-assistant · 1,204 stars · on GitHub · vellum.ai

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.

Any agent
npx skills add vellum-ai/vellum-assistant --skill amazon
Clone the repo
git clone --depth 1 https://github.com/vellum-ai/vellum-assistant

Made for: Claude Code, Codex.

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 amazon

README.md
[![agentmods](https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/amazon/github.svg)](https://agentmods.dev/skills/vellum-ai/vellum-assistant/amazon)
Your own site
<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/amazon"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/amazon/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 amazon

Your own site · 80×15
<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/amazon"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/amazon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 11 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,103 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00011 $0.01103
Opus 5 $0.00005 $0.00551
Sonnet 5 $0.00002 $0.00221
Haiku 4.5 $0.00001 $0.00110

Measured 9d ago against content hash 4296bf5c0a98, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

amazon 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 9d ago.

The scan reads SKILL.md. This mod also ships 11 executable files (scripts/__tests__/amazon-checkout-sanity.test.ts, scripts/__tests__/amazon-intent.test.ts, scripts/__tests__/amazon-parse-cart.test.ts, …), 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.

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.

skills/amazon/SKILL.md · 154 lines

How it starts

The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Use browser automation for all Amazon actions. All browser operations are executed through the assistant browser CLI, invoked via host_bash. Use helper scripts with host_bash to normalize extraction results and decide the next step.

Required tools

  • host_bash for assistant browser CLI commands and deterministic helper scripts under scripts/.

Hard constraints

  • Do not call assistant browser chrome relay.
  • Do not use legacy relay-backed scripts.
  • Always require explicit user confirmation before final order submission.

Step graph (state machine)

Step 1: Classify workflow state

Run this early in each turn when intent is unclear:

bun {baseDir}/scripts/amazon-intent.ts --request "<latest user request>" --checkout-reviewed <true|false> --has-cart-items <true|false>

Use the returned step to route to one of: search, variant_select, cart_review, checkout_review, fresh_slot, place_order.

Step 2: Product discovery (search)

  1. Navigate to search results page:
assistant browser --session amazon navigate --url "https://www.amazon.com/s?k=<urlencoded query>"
  1. Capture current state:
assistant browser --session amazon --json snapshot
assistant browser --session amazon --json extract --include-links
  1. Parse candidates deterministically:
bun {baseDir}/scripts/amazon-parse-search.ts --query "<query>" --input-json '<json payload with extracted text/links>'
  1. Present top options with title, price, ASIN (if present), Prime/Fresh hints.

Step 3: Product detail + variant resolution (variant_select)

  1. Open product result.
  2. Re-snapshot + re-extract.
  3. Parse product details:
bun {baseDir}/scripts/amazon-parse-product.ts --input-json '<json payload with extracted text/links>'
  1. If variation hints are present, resolve user choice before add-to-cart.

Step 4: Add to cart + verify (cart_review)

  1. Click Add to Cart on product page.
  2. Navigate to cart page and extract:

Read the full file on GitHub · 154 lines

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. 9d ago First seen · 154 lines · 11 tokens per session scan A 4296bf5c0a98

Subscribe to this mod's changes

amazon is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,204 stars, last pushed today), licensed MIT. It adds 11 tokens to every session and 1,103 once invoked, about $0.0001 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 2026-08-30.

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