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 san-npm/skills-ws --skill email-sequencegit clone --depth 1 https://github.com/san-npm/skills-wsWrote 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/san-npm/skills-ws/email-sequence)<a href="https://agentmods.dev/skills/san-npm/skills-ws/email-sequence"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/email-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/san-npm/skills-ws/email-sequence"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/email-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 | $0.00080 | $0.00536 |
| Opus 5.5 | $0.00032 | $0.00214 |
| Sonnet 5.5 | $0.00016 | $0.00107 |
| Haiku 4.5 | $0.00008 | $0.00054 |
Grade A, and why
email-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 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.
What it actually says
Email Sequences — Expert Playbook
Reference guide
Read only the references needed for the current request:
- When to Use This Skill: references/when-to-use-this-skill.md
- Welcome Sequence — 7-Email Template: references/welcome-sequence-7-email-template.md
- Onboarding Drip Sequence: references/onboarding-drip-sequence.md
- Event-Driven Lifecycle Automation (Implementation): references/event-driven-lifecycle-automation-implementation.md
- Re-Engagement Campaign: references/re-engagement-campaign.md
- Cart Abandonment Sequence: references/cart-abandonment-sequence.md
- Subject Line Formulas (25+): references/subject-line-formulas-25.md
- Preview Text Optimization: references/preview-text-optimization.md
- Send Time Optimization: references/send-time-optimization.md
- Deliverability Checklist: references/deliverability-checklist.md
- Segmentation Strategies: references/segmentation-strategies.md
- Metrics Benchmarks by Industry: references/metrics-benchmarks-by-industry.md
- Email Copy Best Practices: references/email-copy-best-practices.md
- ESP / Lifecycle Platform Selection Guide: references/esp-lifecycle-platform-selection-guide.md
- Quick-Start Implementation: references/quick-start-implementation.md
What ships with it
16 files 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.
- agents/openai.yaml 160 B
- references/cart-abandonment-sequence.md 1.1 KB
- references/deliverability-checklist.md 11 KB
- references/email-copy-best-practices.md 1.0 KB
- references/esp-lifecycle-platform-selection-guide.md 3.5 KB
- references/event-driven-lifecycle-automation-implementation.md 4.8 KB
- references/metrics-benchmarks-by-industry.md 2.6 KB
- references/onboarding-drip-sequence.md 2.1 KB
- references/preview-text-optimization.md 1.3 KB
- references/quick-start-implementation.md 1.1 KB
- references/re-engagement-campaign.md 1.6 KB
- references/segmentation-strategies.md 2.4 KB
- references/send-time-optimization.md 1.2 KB
- references/subject-line-formulas-25.md 2.6 KB
- references/welcome-sequence-7-email-template.md 3.3 KB
- references/when-to-use-this-skill.md 282 B
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 · 26 lines · 80 tokens per session scan A 423c981dc4b7
email-sequence is a skill published in the GitHub repository san-npm/skills-ws (4 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 536 once invoked, about $0.0003 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 2026-08-31.
email-sequence compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| email-sequencesan-npm/skills-ws | Skill | 4 | 1mo ago | MIT | A | 80 | Codex |
| 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 |