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 ur-grue/autopunk-media-skills --skill newsletter-audience-segmentergit clone --depth 1 https://github.com/ur-grue/autopunk-media-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/ur-grue/autopunk-media-skills/newsletter-audience-segmenter)<a href="https://agentmods.dev/skills/ur-grue/autopunk-media-skills/newsletter-audience-segmenter"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/newsletter-audience-segmenter/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/ur-grue/autopunk-media-skills/newsletter-audience-segmenter"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/newsletter-audience-segmenter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.02569 |
| Opus 5 | $0.00018 | $0.01285 |
| Sonnet 5 | $0.00007 | $0.00514 |
| Haiku 4.5 | $0.00004 | $0.00257 |
Grade A, and why
newsletter-audience-segmenter 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 8d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newsletter Audience Segmenter
What This Skill Does
Produces a practical audience segmentation brief for a newsletter, identifying 3-5 distinct subscriber groups with tailored content recommendations and messaging strategies for each segment.
When To Use This Skill
- You are launching a new newsletter and need to define your audience segments before writing your first edition
- Your subscriber list has grown and a one-size-fits-all approach is no longer working — open rates or engagement are declining
- You are introducing paid tiers, sponsored content, or targeted editions and need to understand which subscribers get which content
- You are preparing a pitch to advertisers or sponsors and need to articulate who your audience segments are and why they are valuable
What You Need To Provide
Required: The newsletter's topic and focus; a description of your current audience (who subscribes, what industries or roles they represent, what you know about why they signed up); any available engagement data (even rough — e.g., "about 40% open every edition, another 30% open occasionally").
Optional: Subscriber count; the newsletter's format and frequency; any survey data, reader feedback, or demographic information; the business model (free, freemium, paid, sponsor-supported); specific segmentation goals (e.g., "I want to identify who would pay for a premium tier").
How the Assistant Approaches This
-
Analyzes the audience signals. Reviews all provided information — topic, engagement data, reader feedback, stated demographics — and identifies the natural fault lines in the audience. These are the characteristics along which subscribers differ meaningfully: their expertise level, their primary use case for the content, their engagement pattern, or their professional context.
-
Defines 3-5 segments with clear boundaries. Each segment gets a descriptive label, a one-paragraph profile, an estimated share of the audience (based on the data provided or reasonable inference), and the defining characteristic that separates it from adjacent segments. Segments are mutually exclusive — a subscriber belongs to one primary segment.
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.
- 8d ago First seen · 142 lines · 36 tokens per session scan A 31235d31fe20
newsletter-audience-segmenter is a skill published in the GitHub repository ur-grue/autopunk-media-skills (32 stars, last pushed 12d ago), licensed MIT. It adds 36 tokens to every session and 2,569 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 2026-09-04.
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