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 wdavidturner/product-skills --skill product-led-seogit clone --depth 1 https://github.com/wdavidturner/product-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/wdavidturner/product-skills/product-led-seo)<a href="https://agentmods.dev/skills/wdavidturner/product-skills/product-led-seo"><img src="https://agentmods.dev/badge/skills/wdavidturner/product-skills/product-led-seo/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/wdavidturner/product-skills/product-led-seo"><img src="https://agentmods.dev/badge/skills/wdavidturner/product-skills/product-led-seo.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 | $0.00090 | $0.01140 |
| Opus 5 | $0.00045 | $0.00570 |
| Sonnet 5 | $0.00018 | $0.00228 |
| Haiku 4.5 | $0.00009 | $0.00114 |
Grade A, and why
product-led-seo 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product-Led SEO
What It Is
Product-Led SEO is a framework that treats SEO as a product discipline, not just a marketing tactic. The core insight: SEO success comes from building products that serve searcher intent, not from creating content optimized for keywords.
The traditional approach to SEO is "find keywords, write content, build links." Product-Led SEO asks: "What is the user searching for, and what product experience will satisfy that search?"
This framework was developed by Eli Schwartz, who helped companies like Zapier, Tinder, Quora, and SurveyMonkey build SEO strategies that generated hundreds of millions in revenue. The key shift: Move from "How do we rank for this keyword?" to "What product does this searcher need?"
When to Use It
Use Product-Led SEO when you need to:
- Decide if SEO is worth investing in for your business model
- Develop an SEO strategy that actually converts (not just drives traffic)
- Build programmatic pages at scale (like Zapier, Zillow, or TripAdvisor)
- Evaluate your SEO approach in the age of AI Overviews and LLMs
- Shift from top-of-funnel content to mid-funnel conversion
- Get product and marketing aligned on search strategy
- Understand the real ROI of your SEO investment
When Not to Use It
- No SEO journey exists: If users don't search for your solution (most B2B SaaS), SEO may not be the right channel
- Committee decisions: Enterprise sales with long buying cycles rarely convert from SEO
- Brand-driven discovery: If your product requires brand awareness first, invest in brand, not SEO
- You need fast results: SEO takes months to years; if you need revenue now, look elsewhere
- Local businesses with no online transaction: A pizza shop may not need a website at all
Patterns
Detailed examples showing how to apply Product-Led SEO correctly. Each pattern shows a common mistake and the correct approach.
Critical (get these wrong and you've wasted your time)
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- patterns/_template.md 460 B
- patterns/aeo-citation-strategy.md 1.7 KB
- patterns/ai-content-as-strategy.md 1.9 KB
- patterns/blog-for-blog-sake.md 1.9 KB
- patterns/copying-competitors-programmatic.md 2.0 KB
- patterns/keyword-tool-truth.md 1.8 KB
- patterns/link-building-vs-brand-building.md 1.9 KB
- patterns/no-seo-journey.md 1.8 KB
- patterns/programmatic-without-use-case.md 1.9 KB
- patterns/ranking-as-kpi.md 1.7 KB
- patterns/seo-as-product.md 1.9 KB
- patterns/technical-seo-overkill.md 1.8 KB
- patterns/top-funnel-trap.md 1.8 KB
- patterns/traffic-not-conversion.md 1.6 KB
- patterns/wrong-funnel-position.md 1.9 KB
- references/product-led-seo-playbook.md 9.6 KB
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 · 88 lines · 90 tokens per session scan A 8dacbee65724
product-led-seo is a skill published in the GitHub repository wdavidturner/product-skills (20 stars, last pushed 7mo ago), licensed MIT. It adds 90 tokens to every session and 1,140 once invoked, about $0.0005 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…