# Prompt Set QA

Gate a prompt universe for schema and provenance completeness, target or campaign...

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

## Install

Claude Code: `npx skills add elvisun/newsjack --skill prompt-set-qa -g -a claude-code -y`
Codex: `npx skills add elvisun/newsjack --skill prompt-set-qa -g -a codex -y`
Cursor: `npx skills add elvisun/newsjack --skill prompt-set-qa -g -a cursor -y`
Gemini CLI: `npx skills add elvisun/newsjack --skill prompt-set-qa -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add elvisun/newsjack`

## Description

Gate a prompt universe for schema and provenance completeness, target or campaign contamination, evidence entailment, naturalness, one-concept clarity, architecture consistency, aided status, answer leakage, and semantic duplicates. Use after realistic prompt generation and before human panel selection.

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

# Prompt Set QA

Decide `pass`, `revise`, `quarantine`, or `reject`. Do not quietly repair upstream work.

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

## Inputs

Require:

- `prompt_universe.json`;
- `prompt_architecture.json`;
- evidence excerpts and source metadata;
- the versioned contamination register;
- deterministic normalization, lexical scan, exact-hash, and similarity-pair results;
- optional blind human decisions.

Reject inputs that include baseline visibility, current rankings, target performance, answer-derived target pages, or selectors' preferred outcomes.

## Run deterministic checks first

Check:

1. schema and provenance completeness;
2. unique IDs and resolved references;
3. target, product, domain, people, slogan, proprietary-category, campaign, flattering-claim, and competitor terms;
4. forbidden answer-derived fields;
5. Unicode normalization, language, length, and one-concept shape;
6. exact normalized hashes;
7. lexical similarity candidate pairs;
8. embedding pairs when a fixed model/version is available;
9. architecture coverage, budget, and aided/lane consistency.

Deterministic target or campaign matches are hard failures in unaided core prompts. B0 target aliases pass only through a declared allowed exception. Embedding similarity may nominate a pair; it may never auto-delete.

## Review semantically

For each candidate, judge:

- evidence-to-prompt entailment;
- naturalness and role/locale authenticity;
- whether both variants preserve one canonical intent;
- proximity, journey, act, expected-answer, and aided-status consistency;
- commercial leading or recommendation forcing;
- semantic slogan or flattering-claim leakage;
- whether answer-derived language entered core;
- whether a similar prompt changes a material constraint.

The rest: https://github.com/elvisun/newsjack/blob/HEAD/plugins/newsjack/skills/prompt-set-qa/SKILL.md