Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AgriciDaniel/claude-emailnpx agentmods add skills/agricidaniel/claude-email/email-reviewWrote 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/agricidaniel/claude-email/email-review)<a href="https://agentmods.dev/skills/agricidaniel/claude-email/email-review"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-email/email-review/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/agricidaniel/claude-email/email-review"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-email/email-review.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 | $0.03344 |
| Opus 5 | $0.00042 | $0.01672 |
| Sonnet 5 | $0.00017 | $0.00669 |
| Haiku 4.5 | $0.00008 | $0.00334 |
Grade A, and why
email-review 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.
How it starts
The opening of the file, as written. The whole thing — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Pasted text - Subject line + body in conversation
- File path - Path to HTML email file
- Gmail draft ID - Fetch via
gmail_get_draftMCP tool - Raw HTML - HTML email source code
Scoring Framework
Overall Score Calculation
Total score = weighted sum of 5 categories:
| Category | Weight | Focus |
|---|---|---|
| Subject Line | 25% | Length, power words, spam triggers, personalization |
| Copy Quality | 25% | Word count, CTA clarity, framework structure, readability |
| Technical/HTML | 20% | Size, responsiveness, dark mode, alt text, preheader |
| Deliverability | 15% | Spam signals, link count, sender reputation factors |
| Compliance | 15% | 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
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 · 409 lines · 84 tokens per session scan A 8be39a8c9f57
email-review is a skill published in the GitHub repository AgriciDaniel/claude-email (121 stars, last pushed 3mo ago), licensed MIT. It adds 84 tokens to every session and 3,344 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
ads-audit
Run a source-grounded paid-advertising audit for one or more of Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for full ad checks, account health reviews, paid-media diagnostics, partial audits after authentication or worker failure, missing-platform…
ads-amazon
Audit Amazon Ads profiles, regions, Sponsored Products, Sponsored Brands, Sponsored Display, DSP, portfolios, targeting, search terms, retail readiness, creative, budgets, ACOS, TACOS, reporting, and policy. Use for Amazon Ads, sponsored ads, Amazon PPC, ACOS, TACOS, ASIN advertising, Amazon DSP, or retail-media…
ads-apple
Audit Apple Ads measurement, AdServices and AdAttributionKit, campaign and keyword structure, Search Match, App Store placements, custom product pages, bidding, budgets, MMP reconciliation, and policy. Use for Apple Ads, Apple Search Ads, App Store ads, Search Match, custom product pages, AdServices, or Apple…
ads-google
Audit Google Ads measurement, Search, Shopping, Performance Max, Demand Gen, YouTube-linked inventory, keywords and search terms, negative-keyword generation or review, creative assets, bidding, budgets, settings, and policy. Use for Google Ads, AdWords, Search campaigns, search terms reports, broad negatives…
ads-landing
Audit paid-ad landing pages for message match, mobile experience, performance, accessibility, trust, forms, consent, tracking, security, and conversion friction. Use for landing-page audit, post-click experience, LP audit, conversion-rate optimization, form optimization, ad-to-page message match, redirects, blocked…
ads-research
Refresh Claude Ads platform, API, policy, regulation, benchmark, issue, pull-request, fork, and repository evidence. Use for ads research refresh, expired refreshdue dates, stale API or platform claims, reverify-or-demote decisions, release-current claim validation, ecosystem review, current platform changes, or…