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 finsilabs/awesome-ecommerce-skills --skill product-launch-campaignsgit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-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/finsilabs/awesome-ecommerce-skills/product-launch-campaigns)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/product-launch-campaigns"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-launch-campaigns/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/finsilabs/awesome-ecommerce-skills/product-launch-campaigns"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-launch-campaigns.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 | $0.00034 | $0.02378 |
| Opus 5 | $0.00017 | $0.01189 |
| Sonnet 5 | $0.00007 | $0.00476 |
| Haiku 4.5 | $0.00003 | $0.00238 |
Grade A, and why
product-launch-campaigns 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 9d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Launch Campaigns
Overview
A successful product launch orchestrates email, SMS, paid social, influencer seeding, and organic content into a coordinated sequence that builds anticipation, converts pre-launch interest into day-one sales, and sustains momentum afterward. The difference between a flat launch and a sellout launch is rarely the product itself — it is the pre-launch waitlist size, VIP early access timing, and the velocity of day-one social proof. Most of the mechanics (waitlists, email sequences, paid campaigns) are configured in your existing tools without custom code.
When to Use This Skill
- When launching a new product and needing a structured multi-channel campaign plan
- When previous product launches were underwhelming and lacked pre-launch buildup
- When building a waitlist or early-access mechanic to create demand signaling
- When needing to coordinate influencer seeding, email sequences, and paid ads into a single timeline
- When wanting to measure the incremental revenue lift of launch campaigns vs. organic listings
Core Instructions
Step 1: Plan your launch timeline
Run three phases for every product launch:
| Phase | Timing | Key Activities |
|---|---|---|
| Pre-launch | T-21 to T-1 | Teaser email, waitlist open, influencer seeding, product reveal |
| Launch day | T-0 | VIP early access, public launch email + SMS, paid social activation |
| Post-launch | T+1 to T+30 | Review collection, social proof email, retargeting campaigns |
Minimum viable timeline:
- T-14: Open waitlist + send reveal email
- T-7: Seed influencers with product
- T-1: Send "tomorrow is the day" email + SMS
- T-0 (8am): VIP early access email (24h before public)
- T+24h: Public launch email to full list + paid social goes live
- T+7: "X units sold in first week" social proof email
Step 2: Build your waitlist
Shopify
Option A: Klaviyo embedded form (recommended):
- Go to Klaviyo → Sign-up Forms → Create Form
- Build an embedded form with email + "What's your interest in this product?" question
- Tag submitters with
product-launch-[product-name]so you can email them as a segment - Embed the form on a dedicated landing page (use a Shopify page template)
- Create a Klaviyo segment:
"Has tag" = "product-launch-[product-name]"— this is your waitlist
What ships with it
7 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.
- evals/email-sequence-and-paid-social-campaign-/criteria.json 3.2 KB
- evals/email-sequence-and-paid-social-campaign-/task.md 1.7 KB
- evals/launch-campaign-timeline-planning/criteria.json 2.9 KB
- evals/launch-campaign-timeline-planning/task.md 1.7 KB
- evals/waitlist-and-early-access-implementation/criteria.json 2.8 KB
- evals/waitlist-and-early-access-implementation/task.md 1.6 KB
- tile.json 318 B
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.
- 9d ago First seen · 202 lines · 34 tokens per session scan A 986bbeab496a
product-launch-campaigns is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 2,378 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 2026-09-03.
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