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Messaging Audit

Community
davekilleen
messaging-audit

Use when comparing product or campaign messaging across content against a cited canonical baseline and its supporting evidence.

Overview

Publisherdavekilleen
RepositoryDex
Skill namemessaging-audit
Stars
481
Forks
130
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by davekilleen on GitHub. Read the source before you install it.

Installation

Install the Messaging Audit 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/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/_available/marketing/messaging-audit .claude/skills/messaging-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Messaging Audit 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 Messaging Audit 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 Messaging Audit 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.

Messaging audit

When to use

Use this skill when multiple artifacts need comparison for positioning, value proposition, audience, terminology, or product claims and a canonical baseline can be cited. It also handles an audit that discovers no usable baseline, provided that limitation is explicit.

Do not use it to invent positioning, approve legal or factual claims, or silently rewrite brand copy. Not for publishing edits, changing the canonical baseline, or deciding that a variation is intentional without evidence and human authority.

Inputs and source discipline

  • Identify the canonical baseline by source path or ID, version, source date, and as-of date/time. If no authority or version is available, record “no canonical baseline” as an unknown rather than choosing the most polished artifact.
  • Build a source/date matrix for every compared artifact: title or ID, version, source, publication or update date, retrieval/as-of date, audience/channel, and scope.
  • Extract claims, value propositions, audience descriptions, problem statements, benefits, and terms verbatim. Attach a source and date to each extracted item.
  • Normalize terms only through an explicit, cited mapping. Preserve the original wording and do not treat similar words as equivalent when the source does not support that.

Method

  1. Confirm the audit question, scope, canonical baseline, versions, timezone if relevant, and as-of boundary.
  2. Populate the source/date matrix and exclude inaccessible or out-of-scope artifacts from conclusions while reporting them as coverage limits.
  3. Extract the baseline's claims and terminology, then extract the same fields from each artifact without paraphrasing away meaningful qualifiers.
  4. Apply only cited term normalization and compare each artifact with the canonical baseline and with other in-scope artifacts.
  5. Classify each difference as aligned, an intentional variation only when its purpose and authority are evidenced, contradictory, stale copy, missing, or an unsupported claim. Do not call a difference intentional merely because it sounds appropriate.
  6. Trace every finding to its source/date entry, mark observed versus inferred meaning, state confidence, and surface unknowns and contradictions side by side.
  7. Offer proposed refinements or questions. Show a preview before any edit, baseline change, or saved report and obtain explicit confirmation from the human authority.

Truth and uncertainty rules

  • Observed: exact copy, term, claim, baseline version, or source date present in an artifact or canonical source.
  • Inferred: a semantic relationship or likely audience effect derived from observed wording; explain the inference and confidence.
  • Unknown: intent, authority, claim support, version, or equivalence not evidenced.
  • Stale: copy based on an older dated baseline or artifact outside the requested as-of boundary; do not call it a current contradiction without checking versions.
  • Contradictory: sources make incompatible claims or definitions; preserve both wording, sources, dates, and any known scope difference.

An unsupported claim means “support was not found in the permitted sources”; it does not prove the claim false. Never invent dates, metrics, percentages, owners, intent, money, causes, status, or evidence. Recommendations are not human decisions.

Output contract

Return an audit with:

  • canonical baseline citation, version, source/date, as-of date/time, and authority status;
  • the complete source/date matrix and declared artifact coverage;
  • a normalized-terms table that preserves original wording and cited mappings;
  • findings classified as aligned, intentional variation, contradiction, stale copy, missing, or unsupported claim, each with source, date, evidence, and confidence;
  • unknowns, contradictions, coverage limits, and recommendations clearly separated from approved messaging decisions.

Safety and write boundaries

Default to read-only. Do not rewrite, publish, approve, or replace canonical messaging; do not edit content systems or claim authority over legal, product, or brand decisions. A requested write requires a precise preview, explicit confirm, and human authority. Apply only that confirmed scope; a recommendation remains a recommendation.

Verification and recovery

Read back every finding against the cited artifact and reconcile wording, version, source, date, and classification before delivery. After an authorized edit or saved report, read back the destination and reconcile it with the confirmed preview and canonical baseline. If a baseline or artifact cannot be read, mark dependent findings unknown; if a newer version appears, mark prior comparisons stale and re-check. If a write fails or is partial, stop, preserve the draft and error, report what changed, and wait for human authority before retrying or recovering.

Frequently asked questions

What does the Messaging Audit AI skill do?

Use when comparing product or campaign messaging across content against a cited canonical baseline and its supporting evidence.

Why use Messaging Audit on TypingMind?

Because you install it once and use it with any model. Messaging Audit 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 Messaging Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/_available/marketing/messaging-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Messaging Audit?

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 Messaging Audit?

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

Is the Messaging Audit AI skill free?

It is published on GitHub by davekilleen. Check the repository for licensing terms. 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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