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narrative-resonance-monitor

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Use when the user asks to "measure how our narrative is landing", "track echo rate against our canon lexicon", or "check how AI answer engines describe our brand"; produces a resonance report — echo rate (overlap of market language with the narrative-registry canon lexicon, method declared), AI-answer perception via tavily.py --answer (proxy-labeled), share-of-voice on a locked competitor panel (reusing share-of-voice-tracker), and resonance signals from bluesky.py / gdelt.py / pageviews.py — every number labeled Measured / proxy / User-provided, feeding the TALE E dimension and the upstream of the E1 evidence-integrity veto. Not for rebuilding share-of-voice machinery — use share-of-voice-tracker; not for own-site GA4/GSC analytics — use performance-monitor; not for scoring TALE profile result — use narrative-quality-auditor; not for adjudicating claims — use offer-claims-registry. 回声率/AI回答感知/份额之声/共鸣信号

narrative/evaluate/narrative-resonance-monitor/SKILL.md

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# Narrative Resonance Monitor

Measures whether the durable brand narrative is actually landing in the market — an **echo rate** (how much of the market's own language overlaps the narrative-registry canon lexicon, with the matching method declared), an **AI-answer perception** read (how answer engines describe the brand versus the canon, via `scripts/connectors/tavily.py --answer`, proxy-labeled), **share-of-voice** on a locked competitor panel, and public **resonance signals** from Bluesky / GDELT / Wikipedia-attention. It sits in the **Evaluate** phase of the TALE loop and is the resonance-evidence feed for the `E` dimension — specifically the upstream of the `E1` evidence-integrity veto: the *proxy-not-Measured* discipline, echo-rate-with-declared-method, and AI-answer-perception sub-items (see [tale-benchmark.md](../../../references/tale-benchmark.md)). It reads the canon lexicon but never edits it, and it never adjudicates a claim.

**Scope guard**: this skill produces the resonance report only. It does **not** rebuild share-of-voice tracking (it *reuses* [share-of-voice-tracker](../../../social/observe/share-of-voice-tracker/SKILL.md) — same locked-panel machinery, narrative/message query-term set swapped in), pull own-site GA4/GSC analytics ([performance-monitor](../../../seo-geo/evaluate/performance-monitor/SKILL.md) owns own-property telemetry), compute or cap the TALE profile result ([narrative-quality-auditor](../narrative-quality-auditor/SKILL.md) is the sole gate), design the message tests whose results it later reads ([message-test-designer](../message-test-designer/SKILL.md)), edit the canon lexicon ([narrative-registry](../../../protocol/narrative-registry/SKILL.md) is the sole writer of `memory/narrative-registry/`), or adjudicate a claim ([offer-claims-registry](../../../protocol/offer-claims-registry/SKILL.md)). It works one lever — resonance measurement — and hands off.

## Quick Start

```
Measure narrative resonance for [brand] against our canon lexicon. Competitor panel: [list]. Platforms: [Bluesky / news / all keyless].
```

```
Run the AI-answer perception check: how do answer engines describe [brand] vs our positioning statement? Use tavily.py --answer and label it proxy.
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