
Narrative Quality Auditor
FreeEnsure your brand narrative is coherent and effective.
Free · Opens the source repo
What Narrative Quality Auditor does
The Narrative Quality Auditor is designed to assess the integrity and effectiveness of your brand's narrative by running separate evaluations for truth, coherence, and effectiveness. Unlike other auditing tools, this skill does not average results into a single score; instead, it provides distinct profiles for each aspect of your narrative. This allows for a more nuanced understanding of how well your messaging aligns with your brand's canon and market positioning.
When using the Narrative Quality Auditor, you can expect to receive detailed insights into your brand's differentiation, message consistency, and the integrity of evidence supporting your claims. It is particularly useful when preparing for a launch or reviewing existing messaging to ensure that it resonates with your target audience and adheres to your brand's established guidelines. The skill is structured to run specific profiles based on the needs of your narrative review, ensuring that each aspect is thoroughly examined.
This tool is ideal for marketers, brand strategists, and content creators who need a reliable method to validate their messaging before public release. By focusing on individual profile results rather than a composite score, users can identify specific areas that require attention or improvement, making it a valuable asset in the narrative development process. The skill emphasizes the importance of maintaining a consistent brand voice and message across various platforms and materials.
However, it's important to note that the Narrative Quality Auditor is not suitable for launch readiness assessments or social media operations. For those purposes, other specialized auditors should be used. This skill is specifically tailored for in-depth narrative analysis, making it an essential tool for ensuring that your brand's communication is both effective and aligned with its core values.
When to use it
Use this skill when you need to audit your brand's narrative for truth, coherence, and effectiveness, especially before a launch.
When not to use it
Avoid using this skill for general launch readiness or social media content audits; it is focused solely on narrative quality.
What you can build with it
Pre-Publish Narrative Review
Run the Narrative Quality Auditor before launching new brand messaging to ensure it aligns with your established narrative.
Assessing Marketing Claims
Use the skill to validate the integrity of claims made in your marketing materials against your brand's canon.
Evaluating Message Experiment Outcomes
After conducting message experiments, utilize the auditor to assess the effectiveness of the messaging based on collected data.
How to install Narrative Quality Auditor
View source1. Install with the skills CLI
npx skills add aaron-he-zhu/aaron-marketing-skills/narrative-quality-auditor --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by aaron-he-zhuNarrative Quality Auditor
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
| Need | Preferred evidence |
|---|---|
| Truth | Named alternatives, interviews/win-loss, product reality, claims projection |
| Architecture | Exact canon/version, message hierarchy, voice/naming/version history |
| Landing | Declared rendered flagship surfaces linked to canon version |
| Effectiveness | Preregistered comprehension/recall/behavior evidence and locked panels |
| Public resonance | Dated 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.mdreferences/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
Next Best Skill
- Truth repair: positioning-truth-tracer
- Architecture repair: message-system-architect
- Landing repair: narrative-cascade-planner
- Effectiveness evidence: message-test-designer
Frequently asked questions about Narrative Quality Auditor
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