Reader Experience Edit

SkillDev tools

Audit and revise practical nonfiction drafts for reader experience, value pacing, front-loaded insight, value-per-page, boredom, and beta-readiness. Use when editing, restructuring, shortening, or reviewing a book, guide, manual, course-like manuscript, or long-form educational draft so readers receive useful value quickly and keep reading.

Use Reader Experience Edit in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add Reader Experience Edit and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use the Reader Experience Edit skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Reader Experience EditStart free

What this skill tells your AI

The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/LVTD-LLC/skills/skills/reader-experience-edit/SKILL.md and read by Ahel’s review.

Core Lens

Treat reader experience as value received over time spent. Practical nonfiction does not keep readers engaged through suspense; it keeps them engaged by regularly delivering the outcome promised by the book.

Use this skill to diagnose and improve:

  • Slow starts.
  • Long stretches of theory before payoff.
  • Low value-per-page.
  • Sections whose value is unclear.
  • Drafts that need to be coherent enough for beta readers, not polished enough for publication.

Reference Routing

NeedRead
Core concepts and terminologyreferences/core/knowledge.md
Revision rules and decision criteriareferences/core/rules.md
Before/after patternsreferences/core/examples.md
Fast audit checklistreferences/core/checklist.md
Step-by-step manuscript auditworkflows/audit-manuscript.md

Workflow

1. Anchor On The Promise

Identify:

  • Target reader.
  • Book promise.
  • First meaningful piece of value.
  • What the reader should be able to do, decide, or understand after each major section.

If the promise is unclear, use book-toc-lab before editing the manuscript.

2. Create A Value Map

Build a table:

Section | Word count | Reader takeaway | Value type | Risk

Use it to find:

  • Slow start: too many words before first value.
  • Long slog: back-to-back sections without strong takeaways.
  • Fluff: high word count relative to reader value.

3. Front-Load Value

Look for ways to deliver the first useful result earlier:

  • Cut front matter.
  • Insert real value into theory sections.
  • Start with the main insight, tool, checklist, example, or result.
  • Move background after the reader has a reason to care.

4. Increase Value-Per-Page

Prefer deletion before addition.

  • Cut anything outside the target reader's scope.
  • Replace general motivation with concrete examples.
  • Compress explanations that do not change reader behavior.
  • Save deleted material in a separate cutting-room-floor note when useful.

5. Prepare For Beta Readers

Do not aim for perfect prose yet. Aim for a draft that is coherent enough for motivated readers to identify real problems.

Return:

  1. Main reader-experience diagnosis.
  2. Value map or section-by-section risk table.
  3. Highest-leverage cuts, moves, and rewrites.
  4. Revised opening or TOC recommendations when relevant.
  5. Beta-readiness note: what must be fixed before readers see it.

Quality Bar

Recommendations should be structural and specific. Prefer "move this section after X", "cut this setup", or "turn this theory into a worked example" over generic writing advice.

Signals

GitHub stars
1k
Forks
316
Last commit
Oct 2026
Advanced
Item type
skill
Key
reader-experience-edit
Source
github.com/hashgraph-online/awesome-codex-plugins