Narrative Quality Auditor

SkillCommunication

Audits your brand narrative for truth, consistency, and evidence quality across messaging surfaces.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Narrative Quality Auditor skill

About this capability

Use when the user asks to "audit our brand narrative" or "is this message on-canon"; runs separate typed TALE truth, system, or effectiveness profiles and never averages them into one composite. Checks differentiation, canon, landing consistency, and evidence integrity. Not for launch readiness — us

What this skill tells your AI

The instructions your AI receives, as published by aaron-he-zhu/aaron-marketing-skills in narrative/evaluate/narrative-quality-auditor/SKILL.md and read by ahel’s review.

Audit narrative truth, message-system coherence, or measured effectiveness as separate TALE profiles. There is no v18 overall composite: truth cannot be averaged away by coherence, and coherence cannot stand in for effectiveness evidence.

When This Must Trigger

  • The user asks whether positioning/differentiation is defensible.
  • A flagship surface needs a pre-publish canon/message-match gate.
  • A message experiment or resonance claim needs evidence-integrity review.
  • A full narrative review is requested; run linked profiles rather than one blended score.

Quick Start

Run TALE truth on canon v7 against named alternatives and approved claims.
Run TALE system on homepage/pricing/deck against canon v7 before release.
Run a full review as three linked profile results; do not compute an overall score.

Skill Contract

Reads: one canon/surface set or message experiment plus current narrative/claims truth. Writes: only permissioned v3 artifacts. Done when: each requested profile is independently complete or its Unknowns are explicit, with no canon, claims, or surface mutation.

narrative-registry owns canon/version state and offer-claims-registry owns claims. This skill judges; authoring/fixing belongs to Trace/Architect/Land skills.

Data Sources

NeedPreferred evidence
TruthNamed alternatives, interviews/win-loss, product reality, claims projection
ArchitectureExact canon/version, message hierarchy, voice/naming/version history
LandingDeclared rendered flagship surfaces linked to canon version
EffectivenessPreregistered comprehension/recall/behavior evidence and locked panels
Public resonanceDated own/public signals with explicit measured/proxy provenance

Instructions

Runtime Reads

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

Runtime and Setup

Read ../../../references/auditor-runbook.md, scoring-semantics.md, tale-benchmark.md, and the TALE 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, still collect the selected profile's typed observations and Unknowns, but return score_state: NOT_SCORED / score_confidence: not_scored with no gate verdict or persistent artifact; runtime absence blocks deterministic scoring, not the observation pass.

Declare target, profile/mode, brand scope, market, audience, canon version, observation date, and evidence window.

Profile Procedure

  • truth: score T1–T10 for material differentiation and factual grounding.
  • system: score A1–A10 and L1–L10 for canon coherence and landing consistency.
  • effectiveness: score E1–E10 for one experiment/locked panel/date.
  • full: run the three profiles independently and keep three artifacts/results. Aggregate release language conservatively: any BLOCK → block; otherwise any UNDECIDED → undecided; otherwise any FIX → fix; all SHIP → ship. Never average scores.

For a flagship pre-publish gate, always execute the system profile procedure and require a compatible current truth result. If no compatible truth result exists, run truth separately when its evidence is available; otherwise record the truth prerequisite as Unknown while still rendering the requested system-profile result. A missing scorer/runtime changes that result to NOT_SCORED/UNDECIDED; it does not justify skipping the profile. Run effectiveness separately only when the user requests it or the surface makes an effectiveness claim.

Every observed state needs source/date/type/confidence. A missing canon is Unknown, not N/A. A2/A4/A8 are conditional: three pillars, a change arc, and fixed boilerplate lengths are patterns only when deliberately chosen. Run the typed scorer per profile.

Verify profile-relevant vetoes: TALE-T1 false/contradictory/unsubstantiated material differentiation, TALE-A1 demonstrated canon contradiction, TALE-L1 material flagship/canon contradiction, and TALE-E1 unsupported effectiveness claim or proxy-as-measured.

§2 TALE Worked Examples

  • Complete truth profile, raw 86, no veto/fail: DONE/SHIP, final 86.
  • Complete system profile, raw 80, one verified L1 failure: DONE_WITH_CONCERNS/FIX, final 59.
  • Complete system profile with A1 and L1 failures: DONE/BLOCK, no final score.
  • Effectiveness profile before test results exist: NEEDS_INPUT/UNDECIDED, no score.

§3 TALE Guardrails

  • A literal “onlyness” sentence is not required; judge the material differentiation actually asserted.
  • Three pillars, a Raskin/change arc, and 25/50/100-word boilerplates are conditional patterns.
  • A governed draft can be audited as a draft; missing access is Unknown, not an A1 failure.
  • Share of voice, sentiment, answer-engine descriptions, comprehension, and behavior are distinct constructs.
  • Narrative change frequency is a drift signal, not an automatic veto.

§5 TALE Translation

Always name truth/system/effectiveness. On trace request, qualify TALE-T1/A1/L1/E1, especially TALE-E1 versus ECHO-E1 and TALE-A1 versus ROAS/RAMP.

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.

For each profile show verdict, target/canon/context/date, score or coverage/interval, confidence, evidence, Unknowns, and fixes. A full report shows three side-by-side results and no overall number. Do not claim market effectiveness from system coherence.

Validation Checkpoints

  • One profile/unit per score; full mode preserves three results.
  • Canon/surface/experiment versions and audience/market are explicit.
  • Conditional templates use N/A only with reason; missing evidence stays Unknown.
  • Current truth/claims projections are read, not candidate files.
  • No canon/claim/surface write or publish action occurred.

Persistence

Persist only after explicit authorization to memory/audits/narrative/YYYY-MM-DD-<topic>-<profile>.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. Never overwrite another profile or update canon/claims/hot cache autonomously.

Reference Materials

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Signals

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Source
github.com/aaron-he-zhu/aaron-marketing-skills