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 post-purchase-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/post-purchase-sequence)<a href="https://agentmods.dev/skills/coleschaffer/copywritingskills-rmbc/post-purchase-sequence"><img src="https://agentmods.dev/badge/skills/coleschaffer/copywritingskills-rmbc/post-purchase-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/post-purchase-sequence"><img src="https://agentmods.dev/badge/skills/coleschaffer/copywritingskills-rmbc/post-purchase-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 | $0.00041 | $0.01856 |
| Opus 5 | $0.00020 | $0.00928 |
| Sonnet 5 | $0.00008 | $0.00371 |
| Haiku 4.5 | $0.00004 | $0.00186 |
Grade A, and why
post-purchase-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.
post-purchase-sequence
Purpose
Generate a post-purchase email sequence (4-5 emails) that maximizes customer lifetime value by guiding buyers from order confirmation through product mastery to repeat purchase. The post-purchase window is the most underleveraged moment in email marketing — the buyer just trusted you with their money and attention is at peak. This sequence reduces refund rates, increases product usage (which drives results, which drives reviews), generates social proof, and primes the next sale. RMBC applies as value delivery architecture: Research identifies usage barriers, Mechanism explains how to get results, Brief structures the onboarding arc, Copy executes with support and warmth.
Inputs
| Input | Required | Description |
|---|---|---|
product_name |
Yes | Name and core promise of the purchased product |
target_audience |
Yes | Who the buyer is — demographics, goals, experience level |
usage_instructions |
Yes | Key steps to use the product effectively — the "how to get results" guide |
complementary_products |
Yes | 1-3 products that pair with the purchase for cross-sell in final email |
sequence_length |
No | Number of emails: 4 or 5 (default: 5) |
review_platform |
No | Where to leave reviews — Amazon, Trustpilot, website, Google (default: website) |
delivery_timeline |
No | Expected shipping/delivery window (default: 3-5 business days) |
Execution Protocol
Step 1 — Load Framework Context
Read rmbc-context/SKILL.md to load RMBC framework definitions. Post-purchase sequences flip RMBC from persuasion to fulfillment — the sale is made, now the framework ensures the product delivers on its promise. Mechanism explains HOW to get results. Proof is generated (reviews), not deployed.
Step 2 — Map the Post-Purchase Arc
| Timing | Role | Focus | |
|---|---|---|---|
| 1 — Confirmation | Day 0 | Validate the purchase, set expectations | Order details, delivery timeline, what to expect, immediate quick win |
| 2 — Usage Tips | Day 3-5 | Teach them how to get results | Top 3 usage tips, common mistakes to avoid, "do this first" action |
| 3 — Results Check-In | Day 10-14 | Ask how it's going, provide support | Check on their experience, offer help, deepen engagement |
| 4 — Review Request | Day 21-30 | Ask for a review at peak satisfaction | Specific review prompt, make it easy (direct link), show gratitude |
| 5 — Cross-Sell | Day 30-45 | Introduce complementary product | "Based on your purchase, you might also like..." — value-first bridge |
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 · 41 tokens per session scan A f1dd3223ede9
post-purchase-sequence is a skill published in the GitHub repository coleschaffer/copywritingskills-rmbc (30 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,856 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.
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…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…