BeginnerClaude Projects

Email Campaign Writer

Email Campaign Writer is an ecommerce AI skill for Claude Projects, built for teams working with Shopify, WooCommerce, BigCommerce. Use it to make acquisition,…

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Supported Platforms
Shopify · WooCommerce · BigCommerce
System Prompt Original link: no public source found
01

What this Skill does

You are trying to make acquisition, post-purchase, or win-back outreach more deliberate. Do not change every step at once: test Generating a full month of email content in one session alongside A/B testing subject line variants, then consider making one clear contact path work… Start with a small test around “Generating a full month of email content in one session”, then check whether “A/B testing subject line variants” fits the way your team actually works.

02

What makes it different

01

Turns customer feedback into structured output

The source asks the model to group, classify, answer, or prioritize review feedback rather than treating every comment as an isolated case.

02

Plans lifecycle emails by customer moment

The instruction separates welcome, abandonment, post-purchase, win-back, and promotional messages into distinct lifecycle moments.

03

No installation needed

Copy and paste into a model chat
  1. Expand and copy the complete original Skill.md below.
  2. Open a new conversation in a compatible AI model, then paste it into the chat box.
  3. Add verified task details, run one low-risk example, and review the result before using it in store operations.
04

Original Skill.md

The complete source is shown below. Copy it from the top right to use it.

You are an ecommerce email marketing strategist and copywriter. When given a product category and brand description, you will:

1. Generate a 30-day email calendar covering:
   - Welcome series (3 emails: Day 0, 2, 5)
   - Browse abandonment (Day 1 after browse)
   - Cart abandonment (3 emails: 1h, 24h, 72h)
   - Post-purchase (2 emails: Day 7, 21)
   - Win-back (30+ days inactive)
   - Weekly newsletter / promotion

2. For each email, provide:
   - Subject line (with emoji)
   - Preview text
   - Full body copy
   - CTA button text
   - Recommended send time
   - Target segment

3. Apply these principles:
   - Personalization: use {first_name}, {product_name}, {cart_value}
   - Scarcity when genuine: "Only 3 left in your size"
   - Social proof: "Join 50,000+ happy customers"
   - A/B test subject line variants

4. Include performance benchmarks:
   - Expected open rate for each email type
   - Expected click rate
   - Industry average for comparison

Output format: Markdown calendar with clear email separators.
05

Get started

Starter prompts for the main use cases—copy and use them directly.

01

Start with one real task

Do not begin with a store-wide rollout. Pick one reversible task where Email Campaign Writer can help you plan campaigns, produce channel-ready material, and control advertising work. Use this when the input boundary, owner, and one primary measure from conversion rate, cost per acquisition, contribution margin, and unsubscribe rate are written down.

Show prompt and variablesHide prompt and variables
Use the Skill above to help me with this task: Start with one real task.

Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]

Do not begin with a store-wide rollout. Pick one reversible task where Email Campaign Writer can help you plan campaigns, produce channel-ready material, and control advertising work.

Return a practical result and clearly flag anything that needs human review.

Replace these variables

[TASK_DETAILS]
The facts, context, or source material for this task.
[CONSTRAINTS]
Replace this placeholder with your verified store-specific information.
02

Prepare the input and guardrails

Collect only the the offer, audience, approved claims, brand voice, channel limits, and budget needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.

Show prompt and variablesHide prompt and variables
Use the Skill above to help me with this task: Prepare the input and guardrails.

Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]

Collect only the the offer, audience, approved claims, brand voice, channel limits, and budget needed for this test. Remove unrelated personal data and state which actions must never run automatically.

Return a practical result and clearly flag anything that needs human review.

Replace these variables

[TASK_DETAILS]
The facts, context, or source material for this task.
[CONSTRAINTS]
Replace this placeholder with your verified store-specific information.
03

Inspect the source Skill, then run it

Read the source, installation method, and permission notes before adding Email Campaign Writer to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a campaign brief or marketing asset that a responsible operator can inspect, and it stayed inside the approved boundary.

Show prompt and variablesHide prompt and variables
Use the Skill above to help me with this task: Inspect the source Skill, then run it.

Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]

Read the source, installation method, and permission notes before adding Email Campaign Writer to a separate test project. Keep commands and Skill text exactly as published.

Return a practical result and clearly flag anything that needs human review.

Replace these variables

[TASK_DETAILS]
The facts, context, or source material for this task.
[CONSTRAINTS]
Replace this placeholder with your verified store-specific information.
04

Review it against a baseline

Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when conversion rate, cost per acquisition, contribution margin, and unsubscribe rate has a pre-test baseline, and errors and exceptions are logged separately.

Show prompt and variablesHide prompt and variables
Use the Skill above to help me with this task: Review it against a baseline.

Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]

Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time.

Return a practical result and clearly flag anything that needs human review.

Replace these variables

[TASK_DETAILS]
The facts, context, or source material for this task.
[CONSTRAINTS]
Replace this placeholder with your verified store-specific information.
06

Risks and operating notes

Use the minimum customer data needed

The workflow may need an order reference or case facts. Do not paste payment details, full addresses, or unrelated order history into a model conversation; redact them unless they are essential to the decision.

Keep claims tied to verified product facts

When this instruction drafts marketing or product content, review specifications, comparisons, performance claims, and testimonials against approved evidence before publishing.

Treat review patterns as hypotheses to test

The instruction can group and prioritize feedback, but repeated wording is not proof of a product defect or customer-wide preference. Check the underlying sample before changing a product, policy, or campaign.

Community rating results

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Task effectiveness
Setup and ease of use
Reliability and guardrails
Documentation clarity
Time to first useful result
Author / maintainer

Network-collected; original author unavailable.

The complete available Skill content is cataloged and reviewed; public web material does not currently name the original author. Attribution does not affect its directory visibility or content-based recommendation eligibility.

Risk, permissions, and limitations

Review third-party permission scopes before providing store data. Never paste payment credentials, customer passwords, or unnecessary personal data into a model. Outputs must be checked by the operator responsible for the workflow.

Content checked: