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/newsletterWrote 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/newsletter)<a href="https://agentmods.dev/skills/robroyhobbs/marketing-skills/newsletter"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/newsletter/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/newsletter"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/newsletter.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 | $0.00219 | $0.10884 |
| Opus 5 | $0.00110 | $0.05442 |
| Sonnet 5 | $0.00044 | $0.02177 |
| Haiku 4.5 | $0.00022 | $0.01088 |
Grade A, and why
newsletter 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 — 1,578 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newsletter Skill
Most newsletters are forgettable. Subscribers open them once, skim the first paragraph, delete.
The newsletters that build loyal audiences—and businesses—do something different. They have a format readers can rely on. A voice that's recognizable. Content worth opening.
This skill helps you create newsletters people actually look forward to.
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 ensure every newsletter edition sounds like the user's brand, speaks to their actual audience, and builds on what has worked before. It also checks the learnings journal for send-time data, subject line performance, and format preferences.
Reads: voice-profile.md, audience.md, learnings.md (all optional)
On invocation, check for ./brand/ and load available context:
-
Load
voice-profile.md(if exists):- Match the brand's tone, vocabulary, and sentence rhythm in every section
- Apply voice DNA to subject lines, hooks, body copy, and sign-offs
- A "direct, proof-heavy" voice writes different newsletters than a "warm, story-driven" voice
- Use vocabulary lists to stay on-brand: preferred words, banned words, signature phrases
- Show: "Your voice is [tone summary]. Newsletter will match that register."
-
Load
audience.md(if exists):- Know who is reading: their awareness level, sophistication, pain points, interests
- Match content depth to audience sophistication (technical vs general, insider vs newcomer)
- Use audience language in hooks and subject lines -- mirror how they talk
- Inform content selection: what topics, what level of detail, what format they prefer
- Show: "Writing for [audience summary]. Awareness: [level]."
-
Load
learnings.md(if exists):- Check for send-time data (e.g., "Tuesday 7am outperforms Thursday 10am by 23%")
- Check for subject line patterns that have worked or failed
- Check for format preferences (long vs short, curated vs original)
- Check for content topic performance data
- Show: "Found [N] newsletter learnings. Applying: [key insight]."
What ships with it
1 file 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.
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 · 1,578 lines · 219 tokens per session scan A ed86b32f8d2f
newsletter is a skill published in the GitHub repository robroyhobbs/marketing-skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 219 tokens to every session and 10,884 once invoked, about $0.0011 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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