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 coleschaffer/copywritingskills-rmbc --skill welcome-sequencegit clone --depth 1 https://github.com/coleschaffer/copywritingskills-rmbcWrote 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/coleschaffer/copywritingskills-rmbc/welcome-sequence)<a href="https://agentmods.dev/skills/coleschaffer/copywritingskills-rmbc/welcome-sequence"><img src="https://agentmods.dev/badge/skills/coleschaffer/copywritingskills-rmbc/welcome-sequence/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/coleschaffer/copywritingskills-rmbc/welcome-sequence"><img src="https://agentmods.dev/badge/skills/coleschaffer/copywritingskills-rmbc/welcome-sequence.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 | $0.01694 |
| Opus 5 | $0.00017 | $0.00847 |
| Sonnet 5 | $0.00007 | $0.00339 |
| Haiku 4.5 | $0.00003 | $0.00169 |
Grade A, and why
welcome-sequence 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
welcome-sequence
Purpose
Generate a complete welcome email sequence (5-7 emails) that converts new subscribers or first-time buyers into engaged, trusting audience members ready to purchase. The welcome sequence is the highest-ROI automated email flow — open rates are 2-3x higher than any other sequence because the subscriber just opted in. Every email must capitalize on this attention window. RMBC applies here as compressed orientation: Research drives personalization, Mechanism introduces your unique approach, Brief structures the arc from stranger to buyer, Copy executes with warmth and authority.
Inputs
| Input | Required | Description |
|---|---|---|
brand_name |
Yes | Brand or sender name the subscriber will recognize |
product_line |
Yes | Core product(s) or service(s) the brand sells |
target_audience |
Yes | Who the subscriber is — demographics, pain points, desires |
primary_offer |
Yes | The first offer to present — product, price, discount, or lead magnet follow-up |
brand_voice |
Yes | One of: founder, expert, friend, authority |
sequence_length |
No | Number of emails: 5, 6, or 7 (default: 7) |
opt_in_source |
No | How they subscribed — lead magnet, quiz, purchase, homepage (default: lead_magnet) |
Execution Protocol
Step 1 — Load Framework Context
Read rmbc-context/SKILL.md to load RMBC framework definitions. Welcome sequences deploy RMBC across a trust-building arc — Research informs audience-specific messaging, Mechanism differentiates the brand, Brief structures the emotional journey, Copy converts attention into relationship.
Step 2 — Map the Welcome Arc
| Role | Emotional State | Focus | |
|---|---|---|---|
| 1 — Welcome | Deliver promised value, set expectations | Curious, high attention | Fulfill opt-in promise, introduce brand voice |
| 2 — Value | Teach something immediately useful | Engaged, evaluating | One actionable insight that delivers a quick win |
| 3 — Story | Share origin story or founding insight | Curious about the person behind the brand | Build connection through narrative |
| 4 — Credibility | Stack proof — results, testimonials, credentials | Weighing trust | Social proof and authority markers |
| 5 — Soft Offer | Introduce the product as a natural next step | Warm, considering | Mechanism tease + value proposition, low-pressure CTA |
| 6 — Hard Offer | Full pitch with urgency | Ready to decide | Complete offer: mechanism, proof, guarantee, deadline |
| 7 — Recap | Summary of value delivered + final CTA | Last chance | Recap the journey, restate the offer, clean close |
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 · 148 lines · 34 tokens per session scan A a4171c68f8de
welcome-sequence is a skill published in the GitHub repository coleschaffer/copywritingskills-rmbc (30 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 1,694 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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