decorum-voice-judge

Score public prose on tone, brand voice, clarity, and credibility with content-pinned verdicts.

Updated May 31, 2026
One-click install
npx skills add https://github.com/4444J99/limen --skill decorum-voice-judge-4444j99
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: decorum-voice-judge
Source: https://github.com/4444J99/limen/tree/main/.agents/skills/decorum-voice-judge
Command: npx skills add https://github.com/4444J99/limen --skill decorum-voice-judge-4444j99

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Deterministic checks can catch typos and stale bios, but they cannot judge whether a public README or positioning doc actually reads professionally. This Skill fills that gap by scoring prose quality and recording a verdict pinned to the exact bytes reviewed, so edits automatically trigger re-review. ## Core Features & Use Cases - Four-Axis Voice Scoring: Grades prose 0-5 on tone, brand voice, clarity, and credibility from the perspective of investors, hiring managers, and customers. - Content-Pinned Verdicts: Records each judgment with the SHA-256 hash of the exact bytes reviewed, so any later edit invalidates the verdict and re-queues review. - Keeper Integration: Works with decorum-keeper.py, which detects changed prose surfaces, queues voice-judge findings, and clears them once a matching verdict is appended to decorum-judgments.yaml. - Use Case: After rewriting a repository README, run the keeper sweep, score the new prose against the four axes, and append a pass/fail verdict with a specific suggested fix for any defects. ## Quick Start Ask the agent to run the decorum keeper sweep, review the queued prose surface, and record a voice verdict pinned to its current content hash.

Frequently Asked Questions about decorum-voice-judge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review whether a README sounds professional?▼

Run the decorum keeper sweep to find queued prose surfaces, read the current file bytes, then score tone, brand voice, clarity, and credibility on a 0-5 scale. Append the verdict with the file's SHA-256 hash to decorum-judgments.yaml to clear the finding.

How does content-pinned review tracking work for documentation?▼

Each verdict records the SHA-256 hash of the exact bytes judged. When the file is edited, its hash changes, the old verdict no longer matches, and the keeper automatically re-queues the surface for a fresh voice review.

What is the difference between decorum-keeper.py and voice judging?▼

The keeper script handles deterministic checks like spelling, bio staleness, and narrative accuracy against contribution data. Voice judging is the model-in-the-loop complement that evaluates subjective qualities like tone and credibility that scripts cannot measure.

Why does a voice-judge finding persist after I recorded a verdict?▼

The finding persists when the content_sha256 in your verdict row does not match the file's current bytes, usually because the file changed after you computed the hash. Recompute the SHA-256 of the current file and update the row.

What are the limitations of automated prose voice scoring?▼

Voice scoring is a bounded, subjective judgment intended for Haiku-tier models and only evaluates how prose reads, not factual accuracy or spelling. It requires the decorum-keeper.py script and the judgments register to function within the wider workflow.