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 agentmods add skills/aitytech/agentkits-marketing/email-sequencenpx skills add aitytech/agentkits-marketing --skill email-sequencegit clone --depth 1 https://github.com/aitytech/agentkits-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/aitytech/agentkits-marketing/email-sequence)<a href="https://agentmods.dev/skills/aitytech/agentkits-marketing/email-sequence"><img src="https://agentmods.dev/badge/skills/aitytech/agentkits-marketing/email-sequence.svg" alt="Measured on agentmods" 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 | $0.00084 | $0.05920 |
| Opus 5 | $0.00042 | $0.02960 |
| Sonnet 5 | $0.00017 | $0.01184 |
| Haiku 4.5 | $0.00008 | $0.00592 |
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 3d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- email-sequence — 95% identical, 30 lines differ
- email-sequence — 95% identical, 30 lines differ
How it starts
The opening of the file, as written. The whole thing — 956 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 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.
- 3d ago First seen · 956 lines · 84 tokens per session scan A b0919692ccff
email-sequence is a skill published in the GitHub repository aitytech/agentkits-marketing (594 stars, last pushed 5d ago), licensed MIT. It adds 84 tokens to every session and 5,920 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
content-calendar
Plan a weekly editorial calendar by mapping company goals to publishable topics, owners, status, and verification notes.
oss-fuzz
Run Tika's OSS-Fuzz Jazzer targets locally against a working-tree checkout — build the image, build fuzzers from local source, fuzz a target, run a corpus as a regression pass, reproduce a crash, and add seeds. Use for "fuzz the OneNote parser", "run OneNoteParserFuzzer against these files", "reproduce an OSS-Fuzz…
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
tika-eval-encoding-regression
Condensed tika-eval pattern for charset-detector regression hunts ("A picks encoding X, B picks Y") using one build and two configs — encoding-pair flip queries, OOV/languageness/FFFD signals, per-file detector attribution.
tika-eval-h2-query
Query the tika-eval H2 database directly for counts and joins the canned reports do not compute — connection gotchas, key tables, example queries. Use when the xlsx/summary.md reports are not enough.
feature-workflow
Taking a multi-PR feature from "shape unknown" to merged without five review rounds per PR: spike until interfaces stop moving, write the contract, cut PRs along contract seams, one review per PR. Use when starting a feature that touches more than one lifecycle object or public interface, when a PR review keeps…