kdp-keyword-optimizer

A guide for choosing seven backend keyword phrases for Amazon KDP, Amazon's self-publishing service for books.

In plain words
What is it for?
Use it to create or refresh keyword metadata for a book, especially when improving how easily readers can find it.
Why use it?
It helps match a book with relevant reader searches when filling KDP's keyword fields, without covering categories or book descriptions.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/cdeistopened/skill-stack/kdp-keyword-optimizer
Any agent
npx skills add cdeistopened/skill-stack --skill kdp-keyword-optimizer
Clone the repo
git clone --depth 1 https://github.com/cdeistopened/skill-stack

Made for: Claude Code, Codex.

Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,035 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00062 $0.02035
Opus 5 $0.00031 $0.01018
Sonnet 5 $0.00012 $0.00407
Haiku 4.5 $0.00006 $0.00203

Measured 2d ago against content hash 2649208097e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

kdp-keyword-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/amazon-publishing/kdp-keyword-optimizer/SKILL.md · 252 lines

How it starts

The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.

KDP Keyword Optimizer

Fill your 7 backend keyword slots with semantically relevant phrases that help Amazon's algorithm understand what your book is about and match it to reader searches.

Purpose

Answer one question: What 7 keyword phrases should go in my KDP backend slots?

This skill does NOT cover categories (separate skill) or book descriptions (separate skill). Just the 7 keyword boxes.

When to Use This Skill

  • "What keywords should I use for my book?"
  • "How do I fill out the KDP keyword boxes?"
  • "My book isn't showing up in search results"
  • "I need to optimize my book's metadata"
  • "What keywords are my competitors using?"

Not for: Category selection, title/subtitle optimization, or book description writing.


The New Reality: Semantic Keywords (2024+)

Amazon's algorithm has fundamentally changed:

The A10 Algorithm

Amazon now prioritizes semantic relevance over exact keyword matches. The algorithm asks: "Does this book actually match the intent of the search?"

Rufus AI

Amazon's AI shopping assistant scans reviews, themes, and metadata for contextual matches. If a reader asks Rufus for "a lighthearted beach read for a mom who needs a break," it won't look for those exact words—it will understand the concept.

What This Means

  • Keyword stuffing is dead. Cramming variations of the same word doesn't help.
  • Semantic phrases win. Think reader intent, not exact matches.
  • Context matters. Your keywords should describe the experience of your book.

The 5-Step Method

Step 1: Extract Core Themes

From your book description and content, identify:

  • Primary topic (what is this book fundamentally about?)
  • Target reader (who is this for?)
  • Reader outcome (what will they gain?)
  • Emotional appeal (how will they feel?)
  • Comparable works (what's it similar to?)

Example (fasting book):

  • Primary topic: Christian fasting, Lent, OMAD
  • Target reader: Catholic, health-conscious, spiritually seeking
  • Reader outcome: Closer to God, better health, discipline
  • Emotional appeal: Peace, clarity, spiritual freedom
  • Comparable works: "like Eat Fast Feast," "for fans of Jason Fung"

Read the full file on GitHub · 252 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 252 lines · 62 tokens per session scan A 2649208097e8

Subscribe to this mod's changes

kdp-keyword-optimizer is a skill published in the GitHub repository cdeistopened/skill-stack (27 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 2,035 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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