# AI Visibility Panel Design

Select QA-approved canonical intent cells into a versioned AI-visibility tracking panel...

- Repository: https://github.com/elvisun/newsjack (1668 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-25
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add elvisun/newsjack --skill ai-visibility-panel-design -g -a claude-code -y`
Codex: `npx skills add elvisun/newsjack --skill ai-visibility-panel-design -g -a codex -y`
Cursor: `npx skills add elvisun/newsjack --skill ai-visibility-panel-design -g -a cursor -y`
Gemini CLI: `npx skills add elvisun/newsjack --skill ai-visibility-panel-design -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add elvisun/newsjack`

## Description

Select QA-approved canonical intent cells into a versioned AI-visibility tracking panel with partitions, variants, lanes, surfaces, locales, repetitions, separate exposure and priority weights, randomization, uncertainty, refresh rules, and campaign controls. Use after prompt QA or when revising an existing panel.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# AI Visibility Panel Design

Turn accepted cells into a defensible measurement plan. Do not generate prompts or invent precision.

This skill inherits the ethical floor from `skills/ETHICS.md`. It enforces anti-hallucination, explicit denominators, and decay-aware versioning. Anti-spray and human-send are not applicable.

## Inputs

Require:

- measurement charter;
- `prompt_architecture.json`;
- QA-approved candidates and complete rejection ledger;
- evidence-backed weight inputs, if any;
- run and review budget;
- variance-pilot observations, when available;
- prior panel version and campaign registry, when applicable.

Never inspect target baseline performance during selection.

## Select by strata

Use the canonical intent cell as the sampling unit. Variants and repeated runs are nested observations, not extra buyers.

Allocate across:

- proximity band;
- job, journey, and information act;
- ICP/role and locale/language;
- evidence grade/source type;
- measurement lane and surface;
- `core` (tracked set), `rotating` (discovery set), `sentinel` (tripwire), `control`
  (false-positive check), and `aided` (prompted set) partitions.

Select within a stratum by evidence strength, language authenticity, decision relevance, and diversity. Preserve declared minimums or emit a waiver. Do not select by current target strength, weakness, gap size, or campaign desirability.

## Separate lanes

- `closed_model`: no external search/tools/files/RAG/history; fixed system, model/version, and sampling; fresh session.
- `retrieval`: record `required`, `allowed`, or `unavailable`, whether retrieval ran, queries when exposed, live/cached state, and citation metadata.
- `consumer_surface`: explicit clean or account archetype, device, locale, history/personalization state; never merge with API rollups.
- `campaign_experiment`: pre-registered frozen evergreen, unaided resonance, aided association, and matched unaffected controls.

Never mix aided statuses or lanes in a denominator.

The rest: https://github.com/elvisun/newsjack/blob/HEAD/plugins/newsjack/skills/ai-visibility-panel-design/SKILL.md