Agent Skillsaaron-he-zhu/aaron-marketing-skills › narrative-quality-auditor

narrative-quality-auditor

GitHub

品牌叙事质量审计工具,独立评估叙事真实性、一致性与有效性。适用于定位防御性检查、发布前一致性放行及证据完整性审查,禁止混合评分。

narrative/evaluate/narrative-quality-auditor/SKILL.md aaron-he-zhu/aaron-marketing-skills

Trigger Scenarios

用户要求审计品牌叙事 检查消息是否符合规范 验证定位差异化是否可辩护 发布前一致性放行

Install

npx skills add aaron-he-zhu/aaron-marketing-skills --skill narrative-quality-auditor -g -y
More Options

Non-standard path

npx skills add https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/narrative/evaluate/narrative-quality-auditor -g -y

Use without installing

npx skills use aaron-he-zhu/aaron-marketing-skills@narrative-quality-auditor

指定 Agent (Claude Code)

npx skills add aaron-he-zhu/aaron-marketing-skills --skill narrative-quality-auditor -a claude-code -g -y

安装 repo 全部 skill

npx skills add aaron-he-zhu/aaron-marketing-skills --all -g -y

预览 repo 内 skill

npx skills add aaron-he-zhu/aaron-marketing-skills --list

SKILL.md

Frontmatter
{
    "name": "narrative-quality-auditor",
    "slug": "aaron-narrative-quality-auditor",
    "class": "auditor",
    "license": "Apache-2.0",
    "summary": "叙事真实性\/系统一致性\/效果证据分层审计",
    "version": "19.2.0",
    "homepage": "https:\/\/github.com\/aaron-he-zhu\/aaron-marketing-skills",
    "metadata": {
        "phase": "evaluate",
        "author": "aaron-he-zhu",
        "hermes": {
            "tags": [
                "marketing",
                "narrative",
                "evaluate"
            ],
            "category": "narrative"
        },
        "version": "19.2.0",
        "openclaw": {
            "emoji": "📖",
            "homepage": "https:\/\/github.com\/aaron-he-zhu\/aaron-marketing-skills"
        },
        "discipline": "narrative",
        "geo-relevance": "low"
    },
    "description": "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 — use launch-readiness-auditor; not for social operations — use social-quality-auditor. 品牌叙事分层审计\/发布前一致性放行",
    "displayName": "Narrative Quality Auditor · 品牌叙事质量门",
    "when_to_use": "Use for narrative truth, message-system consistency, flagship pre-publish alignment, or measured message-effectiveness review. A full review runs linked profiles separately.",
    "argument-hint": "<canon\/surfaces\/experiment> [truth|system|effectiveness|full]",
    "compatibility": "Claude Code and compatible agent-skill hosts"
}

Narrative 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.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

Next Best Skill

Version History

  • 8ebe52f Current 2026-08-20 02:03

    修复路由以明确预发布审计门控;新增支持证据门控的上下文配置文件

  • bc7d62d 2026-07-25 08:09

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