Agent skill · Testing & QA

message-test-designer

Use when the user asks to "test our messaging before we scale it", "design a message-market-fit panel", or "run a 5-second comprehension test on our new tagline"; produces a message-test design spec — hypothesis, panel and recruit criteria, comprehension / 5-second / message-market-fit (Wynter-style) protocols, stimulus set drawn from the canon, success thresholds, and a stop/revise decision rule — for the TALE Evaluate phase so the message is validated before any paid scale. It designs the test; it never runs the experiment or adjudicates a claim. Not for running the panel or A/B experiment —

aaron-he-zhugithub.com/aaron-he-zhuGitHub ↗
claude-codeApache-2.0
Install
npx skills add aaron-he-zhu/aaron-marketing-skills --skill message-test-designer --agent claude-code

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

Facts
Files in the skill folder: 1
SKILL.md size: 14 KB
Bundled scripts: none
Version: 19.1.0
Requires: Claude Code and compatible agent-skill hosts
Path: narrative/evaluate/message-test-designer/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

# Message Test Designer Designs the pre-scale message validation for a candidate narrative — the hypothesis, the target panel and recruit criteria, the comprehension / 5-second / message-market-fit (Wynter-style) protocols, the stimulus set drawn from the canon, the success thresholds, and the stop/revise decision rule. It sits in the **Evaluate** phase of the TALE loop and feeds the `E` sub-item *the message is tested before scale* (comprehension / 5-second / message-market-fit panel) — see [tale-benchmark.md](../../../references/tale-benchmark.md). Its output is a **test design spec only**: this skill designs the test, hands execution to the experiment builders, and never runs the panel, analyzes results, or adjudicates a claim. It also encodes the `E1` discipline downstream — a message that fails its test triggers revision, not louder repetition (the narrative-whiplash guardrail's counter-move). **Scope guard**: this skill produces the test design document only. It does **not** run the panel or the A/B experiment (hand execution to [send-experiment-designer](../../../email/deliver/send-experiment-designer/SKILL.md) or [ad-test-designer](../../../ad/orchestrate/ad-test-designer/S

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 message-test-designer skill do?

Use when the user asks to "test our messaging before we scale it", "design a message-market-fit panel", or "run a 5-second comprehension test on our new tagline"; produces a message-test design spec — hypothesis, panel and recruit criteria, comprehension / 5-second / message-market-fit (Wynter-style) protocols, stimulus set drawn from the canon, success thresholds, and a stop/revise decision rule — for the TALE Evaluate phase so the message is validated before any paid scale. It designs the test; it never runs the experiment or adjudicates a claim. Not for running the panel or A/B experiment —

How do I install it?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill message-test-designer --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.

Keep going