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Email Quality Auditor

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aaron-he-zhu
email-quality-auditor

Use when the user asks to "audit an email program" or "is this campaign safe to send"; runs a typed 20-item SEND profile with authentication, consent, opt-out, and claim veto checks on own evidence. Not for building deliverability setup — use deliverability-qa; not for designing lifecycle flows — use email-sequence-designer. 邮件质量审计/EQS评分/发送前放行

Overview

Publisheraaron-he-zhu
Repositoryaaron-marketing-skills
Skill nameemail-quality-auditor
Stars
2.8K
Forks
361
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aaron-he-zhu on GitHub. Read the source before you install it.

Installation

Install the Email Quality Auditor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git /tmp/aaron-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/aaron-marketing-skills/email/deliver/email-quality-auditor .claude/skills/email-quality-auditor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Email Quality Auditor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Email Quality Auditor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Email Quality Auditor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Email Quality Auditor

Audit one email program/profile and observation window with SEND. Open rate is MPP-sensitive proxy evidence; direct action and the program's declared outcome truth set carry the outcome read.

When This Must Trigger

  • Before a material broadcast/sequence release when channel safety is uncertain.
  • When authentication, consent, suppression, complaints, frequency, claims, or attribution need a gate.
  • When the user requests an EQS/SEND baseline or rerun.

Quick Start

text
Audit this newsletter using the last 90 days, provider split, MPP share, and subscription truth set.
Check this promotional send against DMARC, consent events, live suppressions, claims, and order IDs.

Skill Contract

Reads: one program/profile, normalized window, provider evidence, live consent/suppression state, rendered messages, and outcome truth. Writes: only a permissioned v3 artifact. Done when: all expected SEND states are explicit and the scorer result is reported without sending email or changing provider settings.

Use deliverability-qa to repair authentication, consent-registry for lawful-basis/suppression facts, email-sequence-designer for journeys, and send-experiment-designer for preregistered tests.

Data Sources

NeedPreferred evidence
AuthenticationDNS, message headers, DMARC aggregate evidence
Consent/suppressionAppend-only consent events plus current live projection
Placement/reputationProvider/seed panel and dated ESP/provider reports
EngagementCohort/provider/MPP-segmented ESP export
LifecycleTrigger/flow configuration and event export
OutcomeEcommerce, CRM, subscription, sponsorship, or named equivalent truth set
ContentRendered message/destination and approved claim/disclosure state

Instructions

Runtime Reads

  • ../../../references/auditor-runbook.md
  • ../../../references/scoring-semantics.md
  • ../../../references/send-benchmark.md
  • ../../../references/runtime-invocation.md
  • references/auditor-runtime.md

Runtime and Setup

Read ../../../references/auditor-runbook.md, scoring-semantics.md, send-benchmark.md, and the SEND catalog entry. Standalone installs use bundled immutable references/auditor-runtime.md; never fetch mutable main. Before deterministic calls, follow runtime-invocation.md, resolve AARON_SKILLS_ROOT="${CLAUDE_PLUGIN_ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || true)}", and require the scorer, validator, and typed catalogs. If unavailable, return score_state: NOT_SCORED / score_confidence: not_scored with no gate verdict or persistent artifact.

Declare profile (promotional|retention|cold-outbound|newsletter), target/program, provider, market, normalized window, list age, MPP share, and observation date.

Evidence and Scoring

  1. Freeze evidence and reconcile provider cohorts/windows before comparing rates.
  2. Score all 20 S1..D5 criteria. Every observed state requires source/date/type/confidence.
  3. E2 is N/A with reason when opens/CTOR are not used. N3/N5 are conditional by program design. Missing records or exports are Unknown, not N/A.
  4. Verify vetoes:
    • SEND-S1: required authentication is demonstrably broken/unaligned.
    • SEND-S2: a purchased/scraped/unlawful list is verified; missing provenance is Unknown.
    • SEND-N1: opt-out is broken/absent or a recorded suppression is not honored.
    • SEND-D1: material claim/disclosure/offer term fails approved evidence.
  5. Run the typed scorer. Use clicks/replies/downstream actions as primary engagement evidence where available; opens/CTOR remain caveated proxy evidence.

For a send-only review without enough program evidence, report the verified red-line checks and exact gaps but return NOT_SCORED/UNDECIDED; “no blocker observed in supplied evidence” is not a full SEND SHIP verdict.

§2 SEND Worked Examples

  • Complete newsletter profile, raw 81, no veto/fail: DONE/SHIP, final 81.
  • Complete promotional profile, raw 76, one verified S1 failure: DONE_WITH_CONCERNS/FIX, final 59.
  • Complete profile, verified S2 and N1 failures: DONE/BLOCK, no final score.
  • Consent provenance absent: S2 Unknown, NEEDS_INPUT/UNDECIDED, no score.

§3 SEND Guardrails

  • DMARC p=none with aligned SPF/DKIM and active monitoring is not automatically an S1 failure.
  • Provider one-click-unsubscribe policy and statutory duties must be named separately.
  • Opens and CTOR require MPP segmentation/proxy caveat; they cannot establish human attention alone.
  • A newsletter need not have cart/post-purchase flows; score only journeys applicable to its declared program.
  • Over-frequency is a serious E4/E5 finding, not an automatic veto.

§5 SEND Translation

Explain channel and recipient risk in plain language. On trace request, qualify SEND-S1/S2/N1/D1 and show the underlying DNS/event/rendered evidence.

Report and Verdict

Begin with the auditor-runbook's exact typed conversation header. Never replace status, verdict, or score_state with prose; list each explicitly missing qualified item as ``ID: `unknown``` before findings.

Show verdict, profile/context, score or coverage/interval, confidence, S/E/N/D detail, outcome truth set, verified critical controls, Unknown inputs, and fix owners. Do not claim deliverability/inbox placement from DNS alone and do not execute a send.

Validation Checkpoints

  • Program/profile/provider/window/list age/market/MPP share are declared.
  • Live suppression state was verified by replay, not a stale projection or pending proposal.
  • All 20 states are valid; conditional N/A has a reason.
  • Provider metrics and reconciled outcome truth are separated.
  • No email/provider mutation occurred without separate explicit approval.

Persistence

Persist only after explicit authorization to memory/audits/email/YYYY-MM-DD-<topic>.md. Preserve the scorer's orthogonal status and verdict; validate the complete v3 draft with validate-audit-artifact.py against the intended --relative-path, persist only through one full-content Write, and revalidate the target per the auditor runbook. Edit/shell/MCP mutations of the reserved sink are unsupported. Do not autonomously modify consent, claims, provider settings, or hot cache.

Reference Materials

Next Best Skill

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Email Quality Auditor AI skill do?

Use when the user asks to "audit an email program" or "is this campaign safe to send"; runs a typed 20-item SEND profile with authentication, consent, opt-out, and claim veto checks on own evidence. Not for building deliverability setup — use deliverability-qa; not for designing lifecycle flows — use email-sequence-designer. 邮件质量审计/EQS评分/发送前放行

Why use Email Quality Auditor on TypingMind?

Because you install it once and use it with any model. Email Quality Auditor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Email Quality Auditor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/email/deliver/email-quality-auditor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Email Quality Auditor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Email Quality Auditor?

As many as you like. As long as a model supports skills, you can use Email Quality Auditor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Email Quality Auditor AI skill free?

Yes. It is published on GitHub by aaron-he-zhu under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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