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 brainbytes-dev/everything-claude-marketing --skill email-segmentationgit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWrote 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/brainbytes-dev/everything-claude-marketing/email-segmentation)<a href="https://agentmods.dev/skills/brainbytes-dev/everything-claude-marketing/email-segmentation"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/email-segmentation/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/brainbytes-dev/everything-claude-marketing/email-segmentation"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/email-segmentation.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 | $0.00026 | $0.02905 |
| Opus 5.5 | $0.00010 | $0.01162 |
| Sonnet 5.5 | $0.00005 | $0.00581 |
| Haiku 4.5 | $0.00003 | $0.00291 |
Grade A, and why
email-segmentation 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 2mo 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Segmentation
When to Activate
Use this skill when:
- Segmenting an email list for targeted campaigns
- Building personalization strategies beyond first-name merge tags
- Setting up behavioral segmentation based on user actions
- Implementing RFM (Recency, Frequency, Monetary) analysis
- Deciding between dynamic content and separate sends
- Collecting data for future segmentation
- Planning progressive profiling strategies
First Questions
Before segmenting, clarify:
- What data is currently available? (Demographics, behavior, purchase history, engagement)
- What email platform is being used? (Segmentation capabilities vary significantly)
- What is the total list size? (Segments must be large enough to be actionable)
- What is the business model? (B2B, B2C, e-commerce, SaaS, services)
- What campaigns or sequences will use these segments?
- Is there CRM or analytics data that can be connected to email data?
- What level of personalization is the team equipped to maintain? (More segments = more content to create)
Core Rules
- Relevance is the goal, segments are the means. The point is sending the right message to the right person, not creating complex segment logic for its own sake.
- Start with 3-5 segments. Not 50. Over-segmentation creates maintenance burden and content gaps. Begin simple, refine over time.
- Every segment needs its own message. If you segment but send the same email, you've wasted the effort. Different segment = different content or offer.
- Behavioral data > Demographic data. What someone DOES predicts intent better than who they ARE.
- Segments are living, not static. People move between segments. Set up rules for segment transitions.
- Minimum viable segment size: 500-1,000. Segments smaller than this are hard to learn from and not statistically reliable for testing.
- Data you collect but don't use is a privacy liability. Only collect data you will actually segment on.
Segmentation Types
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.
- 2mo ago First seen · 288 lines · 26 tokens per session scan A 5ddf4e2428ff
email-segmentation is a skill published in the GitHub repository brainbytes-dev/everything-claude-marketing (10 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 2,905 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
email-segmentation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| email-segmentationbrainbytes-dev/everything-claude-marketing | Skill | 10 | 7mo ago | MIT | A | 26 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |