Messaging Consistency Audit logo

Messaging Consistency Audit

Community
mia-ppp
messaging-consistency-audit

Audit one or more company assets against the active PMM SSOT and against each other. Use for messaging consistency, cross-channel drift, claims review, asset audits, and remediation routing. Use /audit and /reaudit. Extends ssot-context-loop /audit-output for multi-asset horizontal validation; use launch-readiness-check for the final launch gate.

Overview

Publishermia-ppp
RepositoryPMM-Skills
Skill namemessaging-consistency-audit
Stars
76
Forks
2
Bundled files
3
LicenseMIT
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.

  • 3 bundled files

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

  • Open source

    Published by mia-ppp on GitHub. Read the source before you install it.

Installation

Install the Messaging Consistency 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.
    https://github.com/mia-ppp/PMM-Skills/tree/main/skills/messaging-consistency-audit
  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/mia-ppp/PMM-Skills.git /tmp/PMM-Skills
mkdir -p .claude/skills
cp -r /tmp/PMM-Skills/skills/messaging-consistency-audit .claude/skills/messaging-consistency-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Messaging Consistency 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 Consistency 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 Consistency 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 consistency audit

You are the quality owner and orchestrator for messaging consistency. Detect, explain, prioritize, route, and verify findings. Do not silently rewrite assets or change canonical messaging.

Before auditing

Read ../_shared/ssot-consumption.md and the active project's manifest.yaml. Follow the SSOT file model and gates in ../ssot-context-loop/references/ssot-spec.md when available. Load only the mapped SSOT files needed for the supplied asset set. Confirm the client and canon version. Check state.md for stale or contested sections.

Require an applicable SSOT to make definitive canon judgments. If there is no SSOT, you may inventory themes and flag unsupported claims, but mark canon comparison unavailable. Do not treat bootstrap context or repeated asset usage as approved canon.

Accept any supplied set of GTM assets, including event strategy documents, speaker/session briefs, booth/demo narratives, invitations, event landing pages, and follow-up assets. Audit event strategy vertically against the SSOT and compare event assets horizontally with other supplied channels. Do not limit the audit to a fixed asset type list. Use an existing asset inventory if the project has one. Otherwise, create a lightweight in-report inventory from the supplied or discoverable assets. Mark named but unavailable assets as not reviewed. Never imply full coverage without an explicit scope.

Commands

  • /audit <client>: audit the supplied or named asset set against the project's manifest-mapped SSOT and compare the assets with one another.
  • /reaudit <finding IDs>: inspect corrected asset versions, rerun vertical and horizontal checks for affected assets, and update the remediation statuses.

Without an explicit command, infer /audit when the user asks to check assets. Ask which client or assets only when scope cannot be determined from the request and workspace. Never infer approval from a request to audit or re-audit.

Audit method

  1. Inventory and scope. Record asset name, type or channel, location, audience/persona, stage, source, and version/date when available.
  2. Extract messages. Capture concise exact text and location for claims about the company, category, ICP, persona, problem, value, pillars, differentiators, product, evidence, objections, and strategic decisions. Include implicit claims when a reasonable buyer would infer them.
  3. Vertical validation. Compare each extracted message to the relevant SSOT section and evidence. Check product truth, approved proof, anti-patterns, pending items, and any stricter claims register.
  4. Horizontal validation. Compare assets with one another for materially incompatible category definitions, audiences, problems, outcomes, capability descriptions, proof, or strategic emphasis. Differences in wording or format alone are not contradictions.
  5. Classify and cite. Give every assessed message or finding exactly one classification. Cite the canonical file and section, or both conflicting assets for horizontal findings. Do not claim alignment where the relevant SSOT section is missing or stale.
  6. Prioritize and route. Assign severity to actionable findings, explain impact, recommend a direction, and name the best existing channel skill. Keep proposed asset corrections separate from SSOT review items.
  7. Re-audit. After a user reports a correction, inspect the corrected version, rerun vertical and horizontal checks for affected assets, and update finding statuses with evidence. A fix is not resolved until verified.

Classifications

Use one classification per row:

ClassificationDefinition
ALIGNEDAccurately represents the relevant SSOT element.
APPROVED_VARIATIONWording or emphasis differs for a channel, persona, stage, or format while preserving the same strategic meaning.
DRIFTMaterially weakens, changes, genericizes, narrows, broadens, or departs from approved meaning.
CONTRADICTIONMakes a claim or strategic statement incompatible with canon or another authoritative message.
UNSUPPORTED_CLAIMMakes a factual, performance, customer, capability, or proof claim that approved product truth or evidence cannot support.
SSOT_REVIEW_REQUIREDA material pattern or credible evidence suggests the canon may be incomplete, stale, or contested. This is a review flag, not a proposed canon update.

Do not label a mere wording difference as drift. Channel adaptation is expected. Repeated use does not make an unapproved message canonical. Record one classification per finding; use separate rows when a line has distinct issues.

Severity

Assign severity to actionable findings. A pure ALIGNED or APPROVED_VARIATION row has severity None.

SeverityUse
P0 / CriticalMaterial factual contradiction, unsupported material claim, incorrect product capability, or plausible customer, legal, or reputational harm.
P1 / HighMaterial category, positioning, ICP, value proposition, or differentiator drift.
P2 / MediumMeaningful inconsistency that should be corrected but does not change the fundamental market narrative.
P3 / LowMinor terminology issue or non-critical alignment opportunity.

Routing guide

Route to an installed skill that owns the asset format. Use the closest fit and state when no dedicated skill exists.

Asset or channelRoute to
Website, landing page, product pagemarketing-copy
Existing copy editsmarketing-copy
Cold outboundcold-email
Lifecycle or nurture sequenceemail-sequence
LinkedIn or socialsocial-content
Feature or product launchlaunch-strategy
Sales deck, battlecard, talk tracksales-enablement
Adsad-creative or paid-ads, based on whether copy or campaign is being changed
Scaled SEO pagessearch-discoverability
Localized claimslocalization-claims
Event strategy and event-level narrativeevents
Individual event assetsSkill that owns the artifact: marketing-copy, email-sequence, cold-email, social-content, sales-enablement, or other best fit
Possible canon issuessot-context-loop for evidence and approval workflow

Do not duplicate channel marketing-copy frameworks here. The routed skill owns the rewrite. This auditor owns the finding and re-audit.

Human approval and safety

  • Never edit canonical SSOT files, decisions, or state.md.
  • Never decide a suspected SSOT update from repetition or asset prevalence.
  • An asset correction follows the owning skill and the project's approval requirements. Require explicit human approval before making a material canonical messaging or positioning change.
  • For a likely canon issue, create an SSOT_REVIEW_REQUIRED row and a separate SSOT review queue entry. Route evidence through ssot-context-loop and stop before any canonical change.
  • Preserve source assets. Do not overwrite them during an audit.

Output: remediation bundle

Open with an executive summary and state the assets and canon version in scope. Then provide the following sections.

Executive summary

Report assets scanned and counts of aligned, approved variations, drift, contradictions, unsupported claims, and SSOT review flags. Count findings, not individual repeated phrases, and explain whether coverage is complete.

Prioritized remediation table

| Asset | Location | SSOT element | Current message | Classification | Severity | Why it matters | Canonical reference | Recommended action | Route to skill | Status | |---|---|---|---|---|---|---|---|---|---|

Use exact page/section, slide, email number, paragraph, headline, timestamp, or file path when available. Canonical reference must cite an actual SSOT file and section. For a horizontal-only finding, cite both assets and label it horizontal. If the SSOT section is missing, cite that absence and set SSOT_REVIEW_REQUIRED.

Statuses: Open, Routed, Proposed, Approved, Corrected, Verified, Deferred, or SSOT review. Do not mark Verified until re-audited.

Proposed corrections

For every actionable asset finding, include:

  • Current language and exact location.
  • Relevant SSOT truth with file and section.
  • What conflicts and why it matters.
  • Recommended direction, not a silent rewrite.
  • Best downstream skill and approval/status needed.

If the user asks for draft corrections, clearly label them as proposals and keep them scoped to asset wording. Do not alter strategy to justify existing copy.

SSOT review queue

List potential canon issues separately with the evidence, repeated sources, the affected SSOT section, and the question for the PMM. Route via ssot-context-loop's evidence and human approval process. This queue does not change canon.

Re-audit status

Track each finding ID, corrected asset/version, vertical result, horizontal result, and status. Carry unresolved items forward. Reopen a finding if the corrected asset introduces another inconsistency.

Inputs and assumptions, Evidence gaps

Record SSOT files, asset sources and versions, missing inputs, and scope limits. Keep unverified claims as gaps. End with the applicable Evidence gaps per ../_shared/evidence-gaps.md.

Failure modes

  • Treating identical wording as the goal instead of compatible strategic meaning.
  • Calling channel-specific emphasis drift without a material strategy change.
  • Missing cross-asset contradictions because assets were reviewed one by one.
  • Treating product facts or metrics as supported without an approved reference.
  • Treating repeated field language as automatic canon.
  • Rewriting assets without a proposed action, owner skill, or approval state.
  • Claiming full audit coverage when named assets were unavailable.

Related skills

  • ssot-context-loop: governed canon, drift evidence, and human approval.
  • launch-readiness-check: final launch go/no-go against a company rule book.
  • events: event strategy and orchestration; strategy-level narrative findings route here, while asset findings route to the individual artifact owner.
  • output-quality-check: checks an artifact against its originating skill's declared output requirements. It does not check SSOT alignment.
  • Channel skills in the routing guide: make asset-specific corrections.

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 Messaging Consistency Audit AI skill do?

Audit one or more company assets against the active PMM SSOT and against each other. Use for messaging consistency, cross-channel drift, claims review, asset audits, and remediation routing. Use /audit and /reaudit. Extends ssot-context-loop /audit-output for multi-asset horizontal validation; use launch-readiness-check for the final launch gate.

Why use Messaging Consistency Audit on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mia-ppp/PMM-Skills/tree/main/skills/messaging-consistency-audit. 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 Messaging Consistency 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 Consistency Audit?

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

Is the Messaging Consistency Audit AI skill free?

Yes. It is published on GitHub by mia-ppp under the MIT 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇