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Domain Authority Auditor

CommunityPopular
aaron-he-zhu
domain-authority-auditor

Use when auditing domain authority, trust, or citation credibility; runs a peer-relative 40-item CITE profile with evidence coverage and verified manipulation/penalty veto checks. Not for page-level content quality — use content-quality-auditor; not for backlink profiling alone — use offsite-signal-analyzer. 域名权威/网站可信度

Overview

Publisheraaron-he-zhu
Repositoryaaron-marketing-skills
Skill namedomain-authority-auditor
Stars
2.9K
Forks
373
Bundled files
2
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.

  • 2 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 Domain Authority 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.
    https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/seo-geo/evaluate/domain-authority-auditor
  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/seo-geo/evaluate/domain-authority-auditor .claude/skills/domain-authority-auditor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Domain Authority 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 Domain Authority 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 Domain Authority 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.

Domain Authority Auditor

Audit one domain's citation-trust signals relative to a declared peer cohort. Produce a complete CITE score only when all applicable evidence and comparison context are present. CITE is advisory; it does not predict whether an answer engine will cite the domain.

When This Must Trigger

  • The user asks whether a domain is trustworthy, authoritative, or citation-ready.
  • A campaign needs a peer-relative CITE baseline or rerun.
  • Link, entity, penalty, or citation evidence may materially change a domain gate.

Quick Start

text
Audit example.com against these five same-stage U.S. SaaS peers.
Run the CITE content-publisher profile; mark every private-data gap Unknown.

Skill Contract

Use offsite-signal-analyzer for link diagnosis without a gate, content-quality-auditor for one page, and entity-registry for canonical entity repair. Compare multiple domains as separate same-profile runs; never score them as one pooled target.

Reads: one domain, one locked cohort, and dated citation/identity/trust/eminence evidence. Writes: only a permissioned v3 artifact. Done when: context is complete, all 40 states are explicit, the scorer result is preserved, and any saved artifact validates.

Instructions

Runtime Reads

  • ../../../references/auditor-runbook.md
  • ../../../references/scoring-semantics.md
  • ../../../references/cite-domain-rating.md
  • ../../../references/framework-catalog.json
  • ../../../references/runtime-invocation.md
  • references/auditor-runtime.md

Runtime Contract

Read ../../../references/auditor-runbook.md, scoring-semantics.md, cite-domain-rating.md, and the CITE entry in framework-catalog.json. For standalone installs, use bundled immutable references/auditor-runtime.md; never fetch mutable main or guess missing policy. 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.

Required Setup

Declare:

  • one canonical domain and observation date;
  • market and entity stage;
  • domain type/profile: default, content-publisher, product-service, ecommerce, community-ugc, tool-utility, or authority-institutional;
  • a locked peer cohort with inclusion rules and evidence window.

Without peer cohort, market, stage, or domain type, return NEEDS_INPUT/UNDECIDED; the inherited numeric anchors are diagnostic priors, not universal thresholds.

Data Sources

NeedPreferred evidence
LinksDated backlink export/index with known coverage
IdentityStructured data, canonical profiles, knowledge-graph/public entity evidence
TrustLink-history comparison, security checks, authorized console evidence
EminenceSearch/analytics visibility and locked peer/query panels
CohortNamed domains plus inclusion/exclusion, market, stage, type, and window

Evidence Procedure

  1. Normalize the domain and verify scope (host/subdomain/protocol treatment).
  2. Freeze backlink, entity, security, search visibility, citation-panel, and reputation evidence with sources/dates.
  3. Evaluate all 40 IDs against the declared cohort. Tool estimates remain estimated or proxy; user exports remain user-provided unless independently verified.
  4. Use Unknown for absent backlink indexes, private console data, or comparison universes. Do not mark link-dependent items N/A simply because data is unavailable.
  5. Apply catalog-authorized N/A only with a reason, for example an AI-citation item when no locked panel is in scope.
  6. Verify vetoes positively:
    • CITE-T03: material link/traffic incoherence plus corroborating manipulation evidence relative to the cohort.
    • CITE-T05: verified manipulation network after common-source/ecosystem adjustment.
    • CITE-T09: verified active manual action or material unresolved deindexing.

Domain youth, privacy-protected WHOIS, low absolute volume, or missing private-console access cannot by themselves fail a veto.

Create a typed audit-run.schema.json input and execute python3 "$AARON_SKILLS_ROOT/scripts/rubric-score.py" score <run.json> when the verified runtime is available. Never renormalize weights around Unknown items.

Report

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.

Lead with verdict, profile/cohort/target/date, score or NOT_SCORED coverage/interval, and confidence. Then provide:

  • C/I/T/E score and coverage table;
  • peer-relative evidence and cohort definition;
  • verified risks versus unresolved Unknowns;
  • top fixes with owner, evidence needed, and rerun condition;
  • provenance appendix, including qualified IDs when detailed traceability is requested.

Use SHIP/FIX/BLOCK/UNDECIDED as the canonical artifact verdict. User-facing prose may say trusted/cautious only as a descriptive translation, never as a separate decision system.

Verdict Rules

  • Complete, no veto, healthy score/no failures: DONE + SHIP.
  • Complete, remediation needed or one veto: DONE_WITH_CONCERNS + FIX; one veto caps final at 59.
  • Complete, 2+ verified vetoes: DONE + BLOCK; no final score.
  • Missing applicable/cohort evidence: NEEDS_INPUT + UNDECIDED; no score.

Route link investigation to offsite-signal-analyzer, technical/index evidence to technical-seo-checker, entity repair to entity-registry, and page quality to content-quality-auditor.

§2 CITE Worked Examples

  • Complete product-service profile, raw 78, one verified T09 failure: DONE_WITH_CONCERNS/FIX, final 59.
  • Complete profile, raw 45, one verified T05 failure: final remains 45; the ceiling never raises a score.
  • Verified T03 and T05 failures with complete evidence: DONE/BLOCK, raw retained, no final score.
  • Missing backlink universe or private-console evidence: affected items are Unknown and no total is emitted.

§3 CITE Guardrails

  • Young domain age and WHOIS privacy are neutral without contradictory evidence.
  • A press/launch spike can explain link velocity; test the explanation before labeling manipulation.
  • Absolute volume is interpreted within peer cohort, entity stage, market, and domain type.
  • Public index absence does not prove a private manual action; T09 requires positive evidence.

§5 CITE Translation

Use plain descriptions by default. On a trace request, show CITE-T03, CITE-T05, and CITE-T09 with source/date/cohort; never expose an unqualified ID.

Persistence

Persist only after explicit authorization. Assemble the complete v3 draft, validate it against the intended memory/audits/domain/YYYY-MM-DD-<topic>.md relative path, persist only through one full-content Write, and revalidate the target. Edit/shell/MCP mutations of the reserved sink are unsupported. Use:

bash
python3 "$AARON_SKILLS_ROOT/scripts/validate-audit-artifact.py" <draft> --relative-path <target>

The audit request itself does not authorize hot-cache, candidate, or registry writes.

Validation Checkpoints

  • Target/profile/market/stage/cohort are explicit and locked.
  • All 40 IDs have evidence-backed state, Unknown, or valid N/A.
  • Absolute anchors were not applied as cohort-independent laws.
  • Vetoes are verified failures, not absence-of-evidence shortcuts.
  • Scorer output, coverage, confidence, status, and verdict are preserved.
  • Persisted artifact is permissioned, path-correct, minimized, and validator-clean.

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 Domain Authority Auditor AI skill do?

Use when auditing domain authority, trust, or citation credibility; runs a peer-relative 40-item CITE profile with evidence coverage and verified manipulation/penalty veto checks. Not for page-level content quality — use content-quality-auditor; not for backlink profiling alone — use offsite-signal-analyzer. 域名权威/网站可信度

Why use Domain Authority Auditor on TypingMind?

Because you install it once and use it with any model. Domain Authority 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 Domain Authority Auditor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/seo-geo/evaluate/domain-authority-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 Domain Authority 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 Domain Authority Auditor?

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

Is the Domain Authority 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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