Agent skill

audience-belief-mapper

Use when the user asks to "map what our buyers believe", "capture the objections we keep hearing", or "find the switching forces that move the beachhead"; produces a belief map of the beachhead — held beliefs and mental models, the recurring objections and their reframes, and the JTBD four forces (push of the problem, pull of the new, anxiety of switching, habit of the present) — each item sourced from interviews or win-loss notes (User-provided) and labeled Measured / User-provided / Estimated, with any unverified quote or comparative claim marked "[needs source]" and routed to the claims can

aaron-he-zhu2,508★ · 1 repos on radarProfile →
claude-codeApache-2.0
Install
npx skills add aaron-he-zhu/aaron-marketing-skills --skill audience-belief-mapper --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 12 KB
Bundled scripts: none
Version: 19.1.0
Requires: Claude Code and compatible agent-skill hosts
Path: narrative/trace/audience-belief-mapper/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,508
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Audience Belief Mapper Captures the beachhead's narrative raw material — the beliefs and mental models buyers already hold, the objections that recur in every deal, each objection's reframe, and the JTBD **four forces** (push of the problem, pull of the new solution, anxiety of the switch, habit of the status quo) that decide whether they move. It is the third move of the TALE **Trace** phase and its output feeds three [TALE](../../../references/tale-benchmark.md) dimensions: **T** (beachhead/ICP truth — the narrative targets a segment scored on serviceability / pain / reachability, not "everyone"), **A** (the objection reframes the message house answers), and **E** (win-loss and objection language written back to the canon candidates). It never scores TALE profile result and never adjudicates a claim — unverified quotes or comparative statements are marked `[needs source]` and routed to `memory/events/claims.ndjson` via an authorized `operation: propose` request to `registry-events.py`. **Scope guard**: this skill maps *beliefs, objections, and switching forces* only. It does **not** build demographic or firmographic persona profiles (reuse [audience-mapper](../../../influencer/

What's inside
Steps it walks through
  1. Quick Start
  2. Skill Contract
  3. Handoff Summary
  4. Data Sources
  5. Instructions
  6. Save Results
  7. Reference Materials
  8. Next Best Skill
More from aaron-marketing-skills
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About this skill
What does the audience-belief-mapper skill do?

Use when the user asks to "map what our buyers believe", "capture the objections we keep hearing", or "find the switching forces that move the beachhead"; produces a belief map of the beachhead — held beliefs and mental models, the recurring objections and their reframes, and the JTBD four forces (push of the problem, pull of the new, anxiety of switching, habit of the present) — each item sourced from interviews or win-loss notes (User-provided) and labeled Measured / User-provided / Estimated, with any unverified quote or comparative claim marked "[needs source]" and routed to the claims can

How do I install it?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill audience-belief-mapper --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From aaron-he-zhu/aaron-marketing-skills, a repository with 2,508 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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