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Agent skill

Email Review

by AgriciDaniel in AgriciDaniel/claude-email

Pre-send email quality review and scoring across 5 dimensions (subject line, copy quality, technical/HTML, deliverability, compliance).

MITAuto-check: notesFrontend & Design

Install Email Review

skills CLI
$ npx skills add AgriciDaniel/claude-email --skill email-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AgriciDaniel/claude-email email-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AgriciDaniel/claude-email.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/email-review .claude/skills/email-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
email-review
GitHub stars
130
Token cost
~3.3k tokens
SKILL.md length
1,573 words
Files
1
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

Pre-send email quality review and scoring across 5 dimensions (subject line, copy quality, technical/HTML, deliverability, compliance).

  • Works in 5 steps: Subject Line Scoring (25% weight) → Copy Quality Scoring (25% weight) → Technical/HTML Scoring (20% weight) → …
  • User wants to review an email before sending
  • SKILL.md covers Purpose, When to Activate, Input Formats and Scoring Framework, plus 3 more sections
  • Calls python

What it does

Email Review is an agent skill from AgriciDaniel/claude-email. Pre-send email quality review and scoring across 5 dimensions (subject line, copy quality, technical/HTML, deliverability, compliance). Analyzes subject lines, body copy, HTML structure, spam triggers, and CAN-SPAM compliance. Scores 0-100 with detailed recommendations for improvement. Use when user wants to review an email before sending, check email quality, or validate marketing email best practices.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Frontend & Design, covering Transactional email. It works with Gmail. The repository describes itself as: AI-powered email management and marketing skill for Claude Code. Inbox triage, composition, quality review, deliverability audit, automation sequences, and marketing strategy. The licence is MIT.

When your agent uses it

  • User wants to review an email before sending
  • Check email quality
  • Validate marketing email best practices

Example prompts

  • “/email-review”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Bash, Grep, Glob

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Subject Line Scoring (25% weight)
  2. Copy Quality Scoring (25% weight)
  3. Technical/HTML Scoring (20% weight)
  4. Deliverability Signals (15% weight)
  5. Compliance Scoring (15% weight)

What it can do on your machine

Read from SKILL.md and the folder at commit 182270a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash
    • Grep
    • Glob

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Email Review loads about 3.3k tokens when it runs. Until then it costs about 105 tokens; SKILL.md has 1,573 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~105
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash, Grep, Glob

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from AgriciDaniel/claude-email at commit 182270a, republished under its MIT licence (© AgriciDaniel). 1,573 words, ~3,250 tokens.

Download SKILL.mdSave it as .claude/skills/email-review/SKILL.md (or your agent's skills folder).
name
email-review
description
Pre-send email quality review and scoring across 5 dimensions (subject line, copy quality, technical/HTML, deliverability, compliance). Analyzes subject lines, body copy, HTML structure, spam triggers, and CAN-SPAM compliance. Scores 0-100 with detailed recommendations for improvement. Use when user wants to review an email before sending, check email quality, or validate marketing email best practices.
allowed-tools
Read, Bash, Grep, Glob
user-invocable
false

Email Review Sub-Skill

Purpose

Reviews email content before sending and provides a comprehensive quality score (0-100) across 5 weighted dimensions. Identifies critical issues, suggests improvements, and provides a rewritten version for low-scoring emails.

When to Activate

  • User asks to "review this email"
  • User provides email content (subject + body) for feedback
  • User requests email quality check or score
  • User wants to validate email before sending
  • User provides Gmail draft ID for review
  • User asks about email deliverability or spam risk

Input Formats

Accept email content in any of these formats:

  1. Pasted text - Subject line + body in conversation
  2. File path - Path to HTML email file
  3. Gmail draft ID - Fetch via gmail_get_draft MCP tool
  4. Raw HTML - HTML email source code

Scoring Framework

Overall Score Calculation

Total score = weighted sum of 5 categories:

CategoryWeightFocus
Subject Line25%Length, power words, spam triggers, personalization
Copy Quality25%Word count, CTA clarity, framework structure, readability
Technical/HTML20%Size, responsiveness, dark mode, alt text, preheader
Deliverability15%Spam signals, link count, sender reputation factors
Compliance15%CAN-SPAM, unsubscribe, physical address, RFC 8058

1. Subject Line Scoring (25% weight)

Base Score: 0-100, then weighted at 25%

Length Check (0-25 points)
  • Optimal: 6-10 words OR 30-50 characters = 25 points
  • Acceptable: 5 or 11 words = 20 points
  • Marginal: 4 or 12 words = 10 points
  • Poor: <4 or >12 words = 0 points
Spam Trigger Detection (-5 points each)

Penalize for each occurrence:

  • ALL CAPS words
  • Multiple exclamation marks (!!!)
  • Phrases: "FREE", "Act Now", "Limited Time", "Guaranteed", "Winner", "Click Here", "Buy Now", "Order Now"
  • Excessive punctuation ($$$, ???)
Power Words (+5 points each, max +15)

Bonus for strategic use of:

  • "New", "Exclusive", "Proven", "Secret", "Discover"
  • "Ultimate", "Essential", "Complete", "Breakthrough"
  • Industry-specific power words
Personalization (+10 points)
  • Contains merge tags like {{first_name}} or {{company}}
  • Behavioral personalization indicators
Emoji Usage (+5 or -5)
  • 1 emoji = +5 points (increases open rates)
  • 2+ emojis = -5 points (reduces professionalism)
  • No emoji = 0 points (neutral)

2. Copy Quality Scoring (25% weight)

Base Score: 0-100, then weighted at 25%

Word Count (0-20 points)
  • 150-300 words = 20 points (optimal)
  • 100-149 or 301-400 = 15 points
  • 50-99 or 401-500 = 10 points
  • <50 or >500 = 0 points
CTA Count (0-25 points)
  • 1 primary CTA = 25 points (best conversion)
  • 2 CTAs = 15 points (acceptable)
  • 3+ CTAs = 5 points (diluted focus)
  • 0 CTAs = 0 points (no action path)
CTA Clarity (0-15 points)

Score based on:

  • 15 points: Action verb + benefit ("Get My Free Guide", "Start Your Trial")
  • 10 points: Generic action ("Learn More", "Download")
  • 5 points: Vague ("Submit", "Click Here")
  • 0 points: No CTA or unclear
Framework Structure (0-15 points)

Check if email follows proven framework:

  • PAS (Problem-Agitate-Solution): 15 points
  • AIDA (Attention-Interest-Desire-Action): 15 points
  • BAB (Before-After-Bridge): 15 points
  • FAB (Features-Advantages-Benefits): 15 points
  • 4Ps (Picture-Promise-Proof-Push): 15 points
  • No clear structure: 0 points
Readability (0-15 points)
  • Short paragraphs (2-3 lines max): +5
  • Bullet points or numbered lists: +5
  • Scannable with subheadings: +5
  • No walls of text: +5
  • Deduct -5 for each readability issue
Personalization Beyond Name (0-10 points)
  • Behavioral triggers (past purchase, browsing): +5
  • Location/timezone awareness: +3
  • Industry/role customization: +2

3. Technical/HTML Scoring (20% weight)

Base Score: 100, deduct points for violations

Total Size (-20 if fail)
  • Must be under 102 KB (Gmail clips emails over this)
  • Check HTML + embedded images (if data URIs)
Text-to-Image Ratio (-15 if fail)
  • Minimum 60% text / 40% images
  • Image-heavy emails trigger spam filters
Responsive Design (-10 if fail)

Check for:

  • Viewport meta tag: <meta name="viewport" content="width=device-width">
  • Media queries for mobile breakpoints
  • Fluid tables or container widths
Dark Mode Support (-5 if fail)

Check for:

  • <meta name="color-scheme" content="light dark">
  • CSS with @media (prefers-color-scheme: dark)
  • Dark mode color overrides
Alt Text on Images (-5 per missing)
  • All <img> tags must have alt attribute
  • Alt text should be descriptive, not just filename
Table-Based Layout (-5 if fail)
  • Email should use <table> for layout (HTML email best practice)
  • Deduct if using CSS Grid/Flexbox (poor email client support)
Font Safety (-5 if fail)
  • Must use web-safe fonts with fallbacks
  • Example: font-family: Arial, Helvetica, sans-serif;
  • Avoid custom web fonts without fallbacks
CTA Button Size (-5 if fail)
  • Minimum 44x44 pixels (mobile touch target)
  • Check all <a> styled as buttons
Plain-Text Version (-10 if fail)
  • Must have plain-text MIME part OR
  • Text-only fallback within HTML
Preheader Text (-5 if missing)
  • 30-80 character preview text
  • Located immediately after <body> tag
  • Often hidden with CSS for HTML view

4. Deliverability Signals (15% weight)

Base Score: 100, deduct points for red flags

Spam Word Density (-5 per word)

Count occurrences of spam triggers in body:

  • "Free", "Winner", "Guarantee", "Risk-free", "Act now"
  • "Cash", "Bonus", "Extra income", "Work from home"
  • "Unsecured credit", "Viagra", "Pharmacy"
  • 0-3 links = 0 penalty
  • 4-5 links = -5 points (warning threshold)
  • 6+ links = -15 points (spam signal)
Image-Only Email (-20 if fail)
  • Emails with only images and no text are spam signals
  • Must have substantive text content
  • Required for marketing emails
  • Must be clearly visible and functional
  • Check for <a> with "unsubscribe" keyword
Sender Name (-10 if generic)
  • Deduct for: "info@", "noreply@", "admin@", "support@"
  • Personal or company name preferred
  • Penalize use of: bit.ly, tinyurl, goo.gl, ow.ly
  • Spam filters distrust shortened URLs

5. Compliance Scoring (15% weight)

Base Score: 100, deduct points for violations

Physical Address (-20 if missing)
  • CAN-SPAM requires postal address
  • Check footer for street address or P.O. Box
Unsubscribe Mechanism (-25 if missing)
  • Must have working unsubscribe link
  • Should process within 10 business days (CAN-SPAM)
Honest Subject Line (-15 if misleading)
  • Subject must accurately reflect email content
  • No deceptive "Re:" or "Fwd:" if not a reply
RFC 8058 Headers (-10 if missing for bulk)

For bulk/marketing emails, check for:

List-Unsubscribe: <mailto:unsub@example.com>
List-Unsubscribe-Post: List-Unsubscribe=One-Click
Show full SKILL.md (635 more words)Show less
Sender Identification (-10 if missing)
  • Must clearly state who is sending the email
  • Check for "From" name and footer company info

Score Interpretation

ScoreRatingStatusRecommendation
90-100Excellent✅ Ready to sendMinor tweaks optional
75-89Good⚠️ Review suggestedAddress medium-priority issues
60-74Fair⚠️ Fix before sendingResolve high-priority issues
40-59Poor❌ Needs reworkSignificant changes required
0-39Critical❌ DO NOT SENDMajor compliance/deliverability issues

Output Format

Structure the review report as:

  1. Header: ## Email Quality Review with overall score, rating, status
  2. Score table: 5 categories with raw score, weight, and weighted score
  3. Issues Found: Grouped by severity (Critical/High/Medium/Low) with specific fixes
  4. Detailed Breakdown: Per-category analysis (subject length/triggers/power words, word count/CTA/framework, HTML size/responsive/dark mode, spam words/links, address/unsubscribe/RFC 8058)
  5. Improved Version (if score < 75): Rewritten subject line, copy fix recommendations, HTML fixes

Use status badges: ✅ Ready to send (90+), ⚠️ Review suggested (75-89), ⚠️ Fix before sending (60-74), ❌ Needs rework (40-59), ❌ DO NOT SEND (0-39).

Workflow

Step 1: Intake

Identify input format and extract:

  • Subject line
  • Body copy (text or HTML)
  • HTML source (if applicable)
Step 2: Run Analysis Scripts (if available)

Check for and execute:

bash
# Subject line scoring
python scripts/score_subject_line.py --subject "[subject]"

# HTML email analysis
python scripts/analyze_email_html.py --file [path]
Step 3: Manual Scoring

For each category:

  1. Start with base score
  2. Apply positive adjustments
  3. Apply penalties
  4. Calculate weighted score
Step 4: Reference Checks

Consult knowledge files for edge cases:

  • references/technical-standards.md - HTML/CSS email rules
  • references/compliance.md - CAN-SPAM, GDPR, CASL requirements
  • references/deliverability-rules.md - Deliverability scoring and spam signal reference
Step 5: Generate Report

Format output per template above, ensuring:

  • Clear categorization of issues by severity
  • Actionable fix recommendations
  • Improved version if score < 75
Step 6: User Confirmation

Ask user if they want to:

  • Apply suggested improvements
  • Re-review after manual edits
  • Send as-is (if score >= 75)

Quality Gates

Hard Rules (Automatic Failure)

These issues result in "DO NOT SEND" status regardless of total score:

  1. Missing unsubscribe link (for marketing emails)
  2. Missing physical address (CAN-SPAM violation)
  3. Email size > 102 KB (Gmail will clip)
  4. Image-only email (no text content)
  5. Deceptive subject line (FTC violation)

Edge Cases

Gmail Drafts

If user provides draft ID:

  1. Use gmail_get_draft MCP tool to fetch
  2. Extract subject from payload.headers
  3. Extract HTML body from payload.parts (mimeType = text/html)
  4. Proceed with normal review
Plain-Text Emails

If email has no HTML:

  1. Skip technical/HTML scoring (default to 80/100)
  2. Focus on copy quality, subject line, compliance
  3. Adjust weighting: Subject 30%, Copy 35%, Deliverability 20%, Compliance 15%
Non-English Emails
  1. Note language in report
  2. Skip spam word detection (English-specific)
  3. Focus on structural/technical issues
  4. Provide caveats about readability scoring
Transactional Emails

If email is transactional (receipts, confirmations):

  1. Relax compliance rules (unsubscribe not required)
  2. Focus on technical delivery and clarity
  3. Note in report: "Transactional email - relaxed marketing rules"

Tools Integration

Available Scripts

Run these scripts from the scripts/ directory:

score_subject_line.py

  • Input: python scripts/score_subject_line.py --subject "Subject line text" --json
  • Output: JSON with length, spam triggers, power words, score
  • Use: Auto-score subject line component

analyze_email_html.py

  • Input: python scripts/analyze_email_html.py --file path/to/email.html --json
  • Output: JSON with size, responsiveness, alt text, preheader, issues
  • Use: Auto-score technical/HTML component
MCP Tools

gmail_get_draft

  • Fetch draft email from Gmail by ID
  • Extract subject and HTML body

gmail_send_message

  • Send reviewed email (after user approval)

Examples

High-scoring email (92/100): Subject "New guide: 5 proven SEO tactics for 2026" -- 8 words, power word, no spam triggers, 280 words, 1 CTA, responsive HTML, all compliance elements present. Status: Ready to send.

Low-scoring email (23/100): Subject "FREE MONEY!!! ACT NOW GUARANTEED WINNER!!!" -- all caps, 4 spam triggers, 50 words, 5 CTAs, missing unsubscribe and address. Status: DO NOT SEND (CAN-SPAM violations).

Notes

  • Always run available scripts before manual scoring
  • Reference knowledge files for edge cases
  • Provide specific, actionable recommendations
  • Never approve emails with compliance violations
  • Weight user industry context (B2B vs B2C vs transactional)
  • Update scoring criteria based on evolving best practices

© AgriciDaniel, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/email-review of AgriciDaniel/claude-email.

Open the folder on GitHubat commit 182270a

Compare with similar skills

Email Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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49 HTML Email Templateminhnv0807/ai-business-skills609—~2kAutomated safety check: PassMIT
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Kindleheartleo/zlib674—~1.4kAutomated safety check: NotesMIT
Gmail Emailopen-gitagent/gitagent714—~235Automated safety check: NotesMIT

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Works with

Categories

Questions about Email Review

What does Email Review do?

Pre-send email quality review and scoring across 5 dimensions (subject line, copy quality, technical/HTML, deliverability, compliance). Email Review is an agent skill from AgriciDaniel/claude-email. Pre-send email quality review and scoring across 5 dimensions (subject line, copy quality, technical/HTML, deliverability, compliance).

When should I use Email Review?

Email Review fits situations like: user wants to review an email before sending; check email quality; validate marketing email best practices.

How do I install Email Review in Claude Code?

Run `npx skills add AgriciDaniel/claude-email --skill email-review -a claude-code`. Or copy the skill folder (skills/email-review in AgriciDaniel/claude-email) into .claude/skills/email-review in your project. Claude Code loads it when a task matches its description.

How do I install Email Review in Codex?

Run `npx skills add AgriciDaniel/claude-email --skill email-review -a codex`. Or copy the skill folder (skills/email-review in AgriciDaniel/claude-email) into .agents/skills/email-review in your project. Codex loads it when a task matches its description.

Can I use Email Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AgriciDaniel/claude-email --skill email-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/email-review, .gemini/skills/email-review, .github/skills/email-review and .opencode/skills/email-review in your project.

What does Email Review need to run?

Going by SKILL.md and its folder, Email Review needs the command-line tools its instructions call (python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Bash, Grep, Glob.

Does Email Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Email Review safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Email Review use?

Email Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Email Review use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Email Review?

Skills that share tags, products or a category with Email Review: 49 HTML Email Template Global (minhnv0807/ai-business-skills, 609 stars), 49 HTML Email Template (minhnv0807/ai-business-skills, 609 stars), Pinme (glitternetwork/pinme, 3.8k stars) and Kindle (heartleo/zlib, 674 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Email Review?

AgriciDaniel (a GitHub user) maintains it in AgriciDaniel/claude-email, which has 130 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on May 23, 2026.

Source: AgriciDaniel/claude-email on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.