Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/robroyhobbs/marketing-skillsnpx agentmods add skills/robroyhobbs/marketing-skills/email-sequencesWrote 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/robroyhobbs/marketing-skills/email-sequences)<a href="https://agentmods.dev/skills/robroyhobbs/marketing-skills/email-sequences"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/email-sequences/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/robroyhobbs/marketing-skills/email-sequences"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/email-sequences.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 | $0.00257 | $0.12927 |
| Opus 5 | $0.00129 | $0.06463 |
| Sonnet 5 | $0.00051 | $0.02585 |
| Haiku 4.5 | $0.00026 | $0.01293 |
Grade A, and why
email-sequences 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 11d 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 — 1,738 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Sequences
Most lead magnets die in the inbox. Someone downloads your thing, gets one "here's your download" email, and never hears from you again. Or worse -- they get blasted with "BUY NOW" emails before you've earned any trust.
The gap between "opted in" and "bought" is where money is made or lost. This skill builds sequences that bridge that gap.
Read ./brand/ per _system/brand-memory.md
Follow all output formatting rules from _system/output-format.md
Brand Memory Integration
This skill reads brand context to make every email sound like your brand and align with your positioning. It also checks whether a lead magnet has already been created, so it can build the sequence around specific deliverable details rather than generic placeholders.
Reads: voice-profile.md, positioning.md, audience.md, creative-kit.md (all optional)
On invocation, check for ./brand/ and load available context:
-
Load
voice-profile.md(if exists):- Match the brand's tone, vocabulary, sentence rhythm in every email
- Apply the voice DNA: sentence length patterns, jargon level, formality register
- A "direct, proof-heavy" voice writes different emails than a "warm, story-driven" voice
- Show: "Your voice is [tone summary]. All emails will match that register."
-
Load
positioning.md(if exists):- Use the chosen angle as the narrative spine of the sequence
- The positioning angle determines how the bridge emails frame the gap
- Show: "Your positioning angle is '[angle]'. Building the sequence around that frame."
-
Load
audience.md(if exists):- Know who is receiving these emails: their awareness level, sophistication, pain points
- Match sophistication level to email complexity and jargon tolerance
- Use audience data to inform send timing recommendations (B2B vs B2C, timezone, habits)
- Show: "Writing for [audience summary]. Awareness: [level]."
-
Load
creative-kit.md(if exists):- Pull brand colors for HTML email templates if ESP integration is active
- Reference visual identity for any image or banner suggestions
- Show: "Creative kit loaded -- brand colors and visual identity available for templates."
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.
- 11d ago First seen · 1,738 lines · 257 tokens per session scan A 4749c144991c
email-sequences is a skill published in the GitHub repository robroyhobbs/marketing-skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 257 tokens to every session and 12,927 once invoked, about $0.0013 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 2026-08-31.
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