narrative-baseline-mapper

name	narrative-baseline-mapper
slug	aaron-narrative-baseline-mapper
displayName	Narrative Baseline Mapper · 叙事基线盘点
summary	现状叙事盘点/各触点口径/意图差距/漂移基线
description	Use when the user asks to "map what our surfaces say today", "inventory our current messaging", or "find the gap between what we say and what we mean"; produces the narrative baseline — a surface-by-surface inventory of what every owned touchpoint (homepage, pricing, docs, decks, social bios, email footers) claims RIGHT NOW, each line labeled Measured / User-provided / Estimated, plus a per-surface gap read vs the intended message and the drift-baseline snapshot the Evaluate phase measures future drift against. Not for authoring the canon — use message-system-architect; not for scoring the surfaces or running the vetoes — use narrative-quality-auditor. 现状叙事盘点/各触点口径/意图差距/漂移基线
version	20.1.0
license	Apache-2.0
compatibility	Claude Code and compatible agent-skill hosts
homepage	https://github.com/aaron-he-zhu/aaron-marketing-skills
when_to_use	Use as the first move of the TALE Trace phase, before any canon exists or before a repositioning: inventory what every owned surface (homepage, pricing, docs, decks, social bios, emails) says today, capture the gap vs the intended message, and freeze the drift baseline the Evaluate phase measures against. The before snapshot — not the canon itself and not the score.
argument-hint	<brand / product> [surface URLs or paste] [intended message, if known]
metadata	{"author":"aaron-he-zhu","version":"20.1.0","discipline":"narrative","phase":"trace","geo-relevance":"low","hermes":{"tags":["marketing","narrative","trace"],"category":"narrative"},"openclaw":{"emoji":"📖","homepage":"https://github.com/aaron-he-zhu/aaron-marketing-skills"}}

Narrative Baseline Mapper

Inventories what every owned surface says today — the homepage headline, the pricing page value line, the docs intro, the pitch-deck one-liner, the social bios, the email footer — and reads each against the intended message to expose the gap. It is the first move of the TALE Trace phase and the "before" snapshot the rest of the narrative work is measured against. It feeds the TALE T (Truth) dimension — specifically the positioning matches shippable reality and surface-truth reads — and freezes the drift baseline that the Evaluate phase (narrative-drift-monitor) measures future surface drift against. It never scores and never authors: it records the current state so the gap is visible.

Scope guard: this skill produces the surface inventory + gap read only. It does not author the canon or the message house (use message-system-architect), reconcile the positioning canvas against shippable reality (use positioning-truth-tracer), map the category's or competitors' stories (use category-narrative-mapper), compute the TALE profile result or run the vetoes (only narrative-quality-auditor scores TALE), or adjudicate any claim it surfaces (unverifiable ones are marked [needs source] and submitted to memory/events/claims.ndjson via an authorized operation: propose request to registry-events.py). It works one lever — the current-state inventory — and hands off.

Quick Start

Map what our surfaces say today for [brand]. Surfaces: [homepage / pricing / docs / deck / bios / emails — URLs or paste].

Inventory our current messaging and show the gap vs our intended message: "[intended one-liner]".

Freeze a narrative drift baseline before we reposition — snapshot every owned surface as-of today.

Skill Contract

Expected output: a narrative baseline document — a surface-by-surface inventory (surface · current headline/value line/claim · as-of date · label Measured / User-provided / Estimated), a per-surface gap read vs the intended message (aligned / drifted / contradictory / silent), a list of any unverifiable claim found on a live surface, the frozen drift-baseline snapshot, and the standard handoff summary.