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-listengit 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-listen)<a href="https://agentmods.dev/skills/robisson/build-like-amazon-agent-skills/wb-listen"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/wb-listen/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-listen"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/wb-listen.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 | $0.00039 | $0.03037 |
| Opus 5 | $0.00019 | $0.01519 |
| Sonnet 5 | $0.00008 | $0.00607 |
| Haiku 4.5 | $0.00004 | $0.00304 |
Grade A, and why
wb-listen 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 10d 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WB-Listen: Who Is the Customer and What Insights Do We Have?
Overview
Listen is the foundation of Working Backwards. Before you can solve a problem, you must deeply understand who experiences it and how it affects them. This stage produces a Customer Insight Summary that documents: who the customer is (specific segment), what their current experience looks like, what data supports your understanding, and what gaps remain in your knowledge.
The output of this stage is not a persona document or a market analysis slide. It is a written narrative (1-2 pages) that makes any reader feel the customer's pain or see their opportunity. If the reader doesn't feel a sense of urgency after reading it, you haven't listened deeply enough.
When to Use
- Starting any new product, feature, or initiative
- When the team disagrees on who the target customer is
- When assumptions about customer needs haven't been validated with data
- When entering a new market or customer segment
- After a product launch that underperformed expectations (re-listen)
- Annually, to refresh understanding of how customer needs have evolved
Amazon Context
Amazon's Leadership Principle #1 is Customer Obsession: "Leaders start with the customer and work backwards." This is not aspirational — it is operational. Teams are expected to cite specific customer data (support contacts, usage patterns, interview quotes, behavioral analytics) in their PR/FAQ documents. "We believe customers want X" is not acceptable without evidence.
At Amazon, customer listening takes many forms:
- CSAT/NPS surveys with verbatim analysis
- Contact driver analysis from customer support tickets
- Voice of the Customer (VOC) meetings where leaders listen to actual customer calls
- Customer Advisory Boards for enterprise products
- Usability studies observing customers attempting tasks
- Behavioral data from instrumented products
- Social media and review mining for unsolicited feedback
The discipline is: never assume you know what the customer wants. Always verify with data. The most dangerous product decisions come from teams that are "too close" to their product and project their own preferences onto customers.
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.
- 10d ago First seen · 275 lines · 39 tokens per session scan A 1bee71ef7d6b
wb-listen is a skill published in the GitHub repository robisson/build-like-amazon-agent-skills (15 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 3,037 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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