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 ai-product-teardowngit 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/ai-product-teardown)<a href="https://agentmods.dev/skills/aroyburman-codes/pm-skills/ai-product-teardown"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/ai-product-teardown/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/ai-product-teardown"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/ai-product-teardown.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 | $0.01026 |
| Opus 5 | $0.00023 | $0.00513 |
| Sonnet 5 | $0.00009 | $0.00205 |
| Haiku 4.5 | $0.00005 | $0.00103 |
Grade A, and why
ai-product-teardown 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Product Teardown Skill
Perform a structured, opinionated teardown of any AI product — analyzing the product decisions, UX, technical architecture, business model, and competitive positioning from a PM lens.
When to Use
- User asks "Tear down [AI product]" or "Analyze [AI product]"
- User wants to understand the product thinking behind an AI feature
- User wants to build product intuition about AI products
- User says
/ai-product-teardownfollowed by a product name - Great for: ChatGPT, Claude, Gemini, Perplexity, Copilot, Midjourney, Cursor, v0, NotebookLM, etc.
Framework: AI Product Teardown (7 Sections)
Section 1: Product Overview
- What it is: One-sentence description
- Company: Who built it, their mission, and strategic context
- Launch date & trajectory: When launched, key milestones, current scale
- Target users: Primary and secondary audiences
- Business model: How it makes money (or plans to)
Section 2: Core Value Proposition
- Job to be Done: What fundamental job does this product do for users?
- 10x moment: What's the moment where users think "this is magic"?
- Switching cost: What would it take to switch away?
- Network effects: Does it get better with more users? How?
Section 3: UX & Product Decisions
Walk through the key product decisions and evaluate each:
- Onboarding flow: How does a new user go from zero to value?
- Core interaction model: Chat? Canvas? Structured output? Multi-modal?
- Information architecture: How is functionality organized?
- Personalization: How does it adapt to different users?
- Error handling: What happens when the AI is wrong?
For each decision, evaluate:
- What they got RIGHT and why
- What they got WRONG or could improve
- What trade-off they're making (and whether you'd make the same one)
Section 4: Technical Architecture (PM Lens)
Analyze the technical choices from a product perspective:
- Model strategy: Which model(s)? Why that capability level?
- Latency vs. quality trade-off: Where do they sit on the spectrum?
- Context & memory: How does it handle conversation history?
- Safety & guardrails: What's their content policy approach?
- Tool use / plugins / integrations: How extensible is it?
- Pricing architecture: How do technical costs map to pricing?
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 · 89 lines · 46 tokens per session scan A 7729262e0b10
ai-product-teardown is a skill published in the GitHub repository aroyburman-codes/pm-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 46 tokens to every session and 1,026 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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