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 aroyburman-codes/pm-skills --skill product-sensegit clone --depth 1 https://github.com/aroyburman-codes/pm-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/aroyburman-codes/pm-skills/product-sense)<a href="https://agentmods.dev/skills/aroyburman-codes/pm-skills/product-sense"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/product-sense/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/aroyburman-codes/pm-skills/product-sense"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/product-sense.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 | $0.00028 | $0.01235 |
| Opus 5 | $0.00014 | $0.00617 |
| Sonnet 5 | $0.00006 | $0.00247 |
| Haiku 4.5 | $0.00003 | $0.00123 |
Grade A, and why
product-sense 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 12d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Sense Skill
Apply a structured framework to PM product sense / product design questions targeting AI product roles.
When to Use
- User asks a "Design a product for X" question
- User asks "How would you improve X"
- User asks "How would you productize X capability"
- User says
/product-sensefollowed by a question - Any product design, product sense, or "build a product" interview question
Context
- Tuned for: AI product roles at frontier AI companies
- What matters: First-principles thinking, ambition, structured clarity, and taste
- Common pitfall: Rushing to solutions without clarifying the problem first. Always start with clarifying questions.
Framework: Product Sense (6 Sections)
Generate the answer following this EXACT structure. Each section should be substantive - not just headers.
Section 1: Clarifications (ASK FIRST, ALWAYS)
Ask 3-5 clarifying questions before proceeding. Categories:
- Scope: What company are we? What's the form factor? Platform constraints?
- Users: Who is the primary audience? B2C vs B2B vs B2B2C?
- Business: What stage is the company? Revenue model? Strategic priorities?
- Technical: What capabilities exist? What's feasible in the timeframe?
- Constraints: Budget, timeline, regulatory, geographic?
After listing questions, state reasonable assumptions for each and proceed.
Section 2: Product Strategy & Rationale (WHY BUILD THIS)
- Company Mission: How does this align with the company's stated mission? Reference the specific company's mission statement and connect your product thinking to it.
- Trends & Tailwinds: What macro trends make this timely? (AI adoption curves, regulatory shifts, user behavior changes)
- Competition: Who else is doing this? What's the gap?
- Strategic Moat: What unique advantage does this company have here?
- Product Goal: One sentence on what we're building and why NOW
Section 3: User Segmentation (WHO)
Segment users along 3 dimensions and pick a primary:
- Reach: How many potential users in each segment?
- Frequency: How often would they use this?
- Underserved: How poorly served are they today?
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.
- 12d ago First seen · 117 lines · 28 tokens per session scan A 513fc750b434
product-sense is a skill published in the GitHub repository aroyburman-codes/pm-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 1,235 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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