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-growthgit 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-growth)<a href="https://agentmods.dev/skills/wdavidturner/product-skills/product-led-growth"><img src="https://agentmods.dev/badge/skills/wdavidturner/product-skills/product-led-growth/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-growth"><img src="https://agentmods.dev/badge/skills/wdavidturner/product-skills/product-led-growth.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 | $0.00083 | $0.01305 |
| Opus 5 | $0.00042 | $0.00652 |
| Sonnet 5 | $0.00017 | $0.00261 |
| Haiku 4.5 | $0.00008 | $0.00130 |
Grade A, and why
product-led-growth 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product-Led Growth (PLG)
What It Is
Product-Led Growth is a go-to-market strategy where the product itself drives acquisition, activation, retention, and monetization. Instead of relying on sales to close deals before users can try the product, PLG lets users experience value first and buy later.
The core insight: In PLG, the product does the selling. Users sign up, experience value through self-serve, and either convert themselves or become qualified leads for sales.
PLG is fundamentally Data-Led Growth (DLG). When you give away a free product, you get two things in exchange: broader reach (lower barrier to entry) and usage data that tells you which features correlate with conversion and retention. Without this data foundation, you're giving away your product for nothing.
When to Use It
Use PLG frameworks when you need to:
- Design a freemium or free trial model for a B2B SaaS product
- Add self-serve to a sales-led product to expand reach
- Optimize conversion from free to paid users
- Define product-qualified leads (PQLs) for your sales team
- Reduce customer acquisition cost through self-serve
- Build a hybrid PLG + sales motion (product-led sales)
- Diagnose why free users aren't converting to paid
- Decide between freemium vs. free trial models
When Not to Use It
PLG is not always the right motion:
- Highly complex products requiring customization — If users can't see value without significant setup or professional services, PLG struggles
- Very small addressable market — If you have 50 potential customers (e.g., defense contractors), sales-led is more efficient
- No individual use case exists — PLG requires an individual problem that one person can solve; if value only emerges at team/company scale, start with sales
- You lack data infrastructure — Without product analytics, you're flying blind
- You want instant revenue impact — PLG takes 12+ months to generate meaningful pipeline; it's a long-term play
What ships with it
17 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/confusing-pls-with-plg.md 1.9 KB
- patterns/freemium-cannibalizes-revenue.md 1.6 KB
- patterns/growth-team-too-early.md 1.6 KB
- patterns/ignoring-behavioral-signals.md 2.0 KB
- patterns/monetization-awareness-gap.md 1.9 KB
- patterns/no-activation-focus.md 1.8 KB
- patterns/plg-in-marketing.md 1.9 KB
- patterns/plg-without-individual-use-case.md 1.8 KB
- patterns/pql-without-buyer.md 1.9 KB
- patterns/product-not-accountable.md 1.9 KB
- patterns/sales-spam-on-signup.md 2.1 KB
- patterns/skipping-profiling-questions.md 2.0 KB
- patterns/time-to-value-too-long.md 1.6 KB
- patterns/trial-vs-freemium-wrong-choice.md 2.2 KB
- patterns/wrong-pqa-definition.md 1.9 KB
- references/product-led-growth-playbook.md 14 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 · 100 lines · 83 tokens per session scan A 2a23cf4dfcf4
product-led-growth is a skill published in the GitHub repository wdavidturner/product-skills (20 stars, last pushed 7mo ago), licensed MIT. It adds 83 tokens to every session and 1,305 once invoked, about $0.0004 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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