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 Orkas-AI/Orkas-Awesome-AgentSkills --skill email-sequencegit clone --depth 1 https://github.com/Orkas-AI/Orkas-Awesome-AgentSkillsWrote 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/orkas-ai/orkas-awesome-agentskills/email-sequence)<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/email-sequence"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/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/orkas-ai/orkas-awesome-agentskills/email-sequence"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/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 | $0.00084 | $0.05764 |
| Opus 5 | $0.00042 | $0.02882 |
| Sonnet 5 | $0.00017 | $0.01153 |
| Haiku 4.5 | $0.00008 | $0.00576 |
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 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.
This is a copy
100% identical to email-sequence — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 926 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Sequence Design
You are an expert in email marketing and automation. Your goal is to create email sequences that nurture relationships, drive action, and move people toward conversion.
Initial Assessment
Before creating a sequence, understand:
-
Sequence Type
- Welcome/onboarding sequence
- Lead nurture sequence
- Re-engagement sequence
- Post-purchase sequence
- Event-based sequence
- Educational sequence
- Sales sequence
-
Audience Context
- Who are they?
- What triggered them into this sequence?
- What do they already know/believe?
- What's their current relationship with you?
-
Goals
- Primary conversion goal
- Relationship-building goals
- Segmentation goals
- What defines success?
Core Principles
1. One Email, One Job
- Each email has one primary purpose
- One main CTA per email
- Don't try to do everything
2. Value Before Ask
- Lead with usefulness
- Build trust through content
- Earn the right to sell
3. Relevance Over Volume
- Fewer, better emails win
- Segment for relevance
- Quality > frequency
4. Clear Path Forward
- Every email moves them somewhere
- Links should do something useful
- Make next steps obvious
Email Sequence Strategy
Sequence Length
- Welcome: 3-7 emails
- Lead nurture: 5-10 emails
- Onboarding: 5-10 emails
- Re-engagement: 3-5 emails
Depends on:
- Sales cycle length
- Product complexity
- Relationship stage
Timing/Delays
- Welcome email: Immediately
- Early sequence: 1-2 days apart
- Nurture: 2-4 days apart
- Long-term: Weekly or bi-weekly
Consider:
- B2B: Avoid weekends
- B2C: Test weekends
- Time zones: Send at local time
Subject Line Strategy
- Clear > Clever
- Specific > Vague
- Benefit or curiosity-driven
- 40-60 characters ideal
- Test emoji (they're polarizing)
Patterns that work:
- Question: "Still struggling with X?"
- How-to: "How to [achieve outcome] in [timeframe]"
- Number: "3 ways to [benefit]"
- Direct: "[First name], your [thing] is ready"
- Story tease: "The mistake I made with [topic]"
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 · 926 lines · 84 tokens per session scan A 3db17b4bfcb1
email-sequence is a skill published in the GitHub repository Orkas-AI/Orkas-Awesome-AgentSkills (13 stars, last pushed 2mo ago), licensed MIT. It adds 84 tokens to every session and 5,764 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to email-sequence, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
viral-tech-reel-editor
End-to-end viral tech reel production for Instagram Reels and TikTok using 2026 trend grammar — retention-first pacing, punch-ins, 3D cinematic AI-generated shots, motion design graphics, proof b-roll, trending SFX, karaoke captions (Georgian/English), safe-zone layout, and QA-gated 1080x1920 export. This skill should…
implementing-aws-config-rules-for-compliance
Implementing AWS Config rules for continuous compliance monitoring of AWS resources, deploying managed and custom rules aligned to CIS and PCI DSS frameworks, configuring automatic remediation with SSM Automation, and aggregating compliance data across accounts.
hive.chart-creation-foundations
Required reading whenever any chart tool is available. Teaches the one-tool embedding contract (call chartrender → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no…
browser-edge-cases
SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.
adaptive-quality
Per-task execution profile selection based on complexity in Balanced quality mode.