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.
npx skills add robisson/build-like-amazon-agent-skills --skill wb-test-and-iterategit clone --depth 1 https://github.com/robisson/build-like-amazon-agent-skillsWrote 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/robisson/build-like-amazon-agent-skills/wb-test-and-iterate)<a href="https://agentmods.dev/skills/robisson/build-like-amazon-agent-skills/wb-test-and-iterate"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/wb-test-and-iterate/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/robisson/build-like-amazon-agent-skills/wb-test-and-iterate"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/wb-test-and-iterate.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 | $0.00046 | $0.04641 |
| Opus 5 | $0.00023 | $0.02320 |
| Sonnet 5 | $0.00009 | $0.00928 |
| Haiku 4.5 | $0.00005 | $0.00464 |
Grade A, and why
wb-test-and-iterate 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WB-Test & Iterate: How Will We Measure Success?
Overview
Test & Iterate defines HOW you'll know whether the product delivers on the promise made in the PR/FAQ. It establishes metrics, targets, and experiments BEFORE building — not after. This prevents the failure mode where teams build something, find metrics that make it look good in retrospect, and declare victory regardless of actual customer impact.
The key distinction in this stage: input metrics (what you control and do) vs output metrics (what customers experience and value). Input metrics are leading indicators — they move first. Output metrics are lagging indicators — they move later as a consequence of input metrics moving.
Your job is to identify the input metrics you can drive, predict which output metrics should respond, set specific numeric targets for both, and design experiments to validate the causal chain.
When to Use
- After PR/FAQ is approved (Stage 4) and before engineering begins
- When defining success criteria for a product or feature launch
- During annual planning to set measurable goals for initiatives
- When re-evaluating an existing product that isn't meeting expectations
- When leadership asks "how will you know this is working?"
Amazon Context
At Amazon, every significant initiative has a metrics deck that is reviewed weekly. Metrics are not chosen after launch to make the team look good — they are committed to before building, published in the PR/FAQ's Internal FAQ ("How will we measure success?"), and reviewed rigorously after launch.
Amazon's metrics philosophy:
- Input metrics over output metrics for operational control. You can't directly control "customer satisfaction" but you can control "page load time" which drives satisfaction.
- Controllable metrics over vanity metrics. "Total signups" is vanity if most users sign up and never return. "Weekly active users doing core action" is controllable.
- Leading indicators over lagging indicators. By the time revenue moves, it's too late to course-correct. Watch the metrics that predict revenue movement.
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.
- 11d ago First seen · 377 lines · 46 tokens per session scan A 9a551ff8191e
wb-test-and-iterate is a skill published in the GitHub repository robisson/build-like-amazon-agent-skills (15 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 4,641 once invoked, about $0.0002 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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