Agent skill

Comp Title Finder

by Ckokoski in Ckokoski/AuthorAgent

Find and analyze comparable titles for query letters, marketing, and positioning strategy

MITAuto-check passedMarketing & SEO

Install Comp Title Finder

skills CLI
$ npx skills add Ckokoski/AuthorAgent --skill comp-title-finder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Ckokoski/AuthorAgent comp-title-finder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Ckokoski/AuthorAgent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/_archived/premium/comp-title-finder .claude/skills/comp-title-finder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
comp-title-finder
GitHub stars
124
Token cost
~1.4k tokens
SKILL.md length
313 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Find and analyze comparable titles for query letters, marketing, and positioning strategy

  • Works in 5 steps: Primary BISAC — Where your book belongs… → Strategic BISAC — Where you'll rank… → Amazon Browse Categories — Up to 10… → …
  • Tasks that involve Positioning and messaging
  • SKILL.md covers Comp Title Discovery, Comp Validation, Market Positioning Map and Category Strategy, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Comp Title Finder is an agent skill from Ckokoski/AuthorAgent. Find and analyze comparable titles for query letters, marketing, and positioning strategy

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Positioning and messaging. The repository describes itself as: The Autonomous AI Writing Agent — a secure, author-focused AI for fiction and nonfiction authors (Planning, Revision, Promotion, and more). The licence is MIT.

When your agent uses it

  • Tasks that involve Positioning and messaging

Example prompts

  • “/comp-title-finder”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Primary BISAC — Where your book belongs by content
  2. Strategic BISAC — Where you'll rank fastest
  3. Amazon Browse Categories — Up to 10 specific categories
  4. Keyword Strategy — 7 backend keywords for KDP
  5. Category rank analysis — Competition density per category

What it can do on your machine

Read from SKILL.md and the folder at commit 47e9570. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Comp Title Finder loads about 1.4k tokens when it runs. Until then it costs about 27 tokens; SKILL.md has 313 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~27
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Ckokoski/AuthorAgent at commit 47e9570, republished under its MIT licence (© Ckokoski). 313 words, ~1,367 tokens.

Download SKILL.mdSave it as .claude/skills/comp-title-finder/SKILL.md (or your agent's skills folder).
name
comp-title-finder
description
Find and analyze comparable titles for query letters, marketing, and positioning strategy
author
Writing Secrets
version
1.0.0
triggers
comp title, comp titles, comparable, comparison title, query letter comp, market position, books like mine, similar books
permissions
file:read, web:search

Comp Title Finder — Premium Skill

Find perfect comparable titles for query letters, Amazon categories, marketing copy, and positioning strategy. Stop guessing — know exactly where your book fits in the market.

Comp Title Discovery

Feed it your manuscript details and get strategic comp title recommendations:

Input
yaml
book:
  title: "The Silent Hour"
  genre: "Psychological Thriller"
  subgenre: "Domestic Suspense"
  themes:
    - "gaslighting"
    - "unreliable narrator"
    - "small-town secrets"
  tone: "Dark, atmospheric, slow burn"
  protagonist: "Woman uncovering her husband's hidden life"
  setting: "Pacific Northwest, present day"
  word_count: 82000
  target_audience: "Readers of Gillian Flynn and Ruth Ware"
  unique_elements:
    - "Dual timeline (present/10 years ago)"
    - "Epistolary elements (found journals)"
Output
Comp Title Analysis: "The Silent Hour"

═══ PRIMARY COMPS (Best for Query Letters) ═══

1. "The Wife Between Us" by Greer Hendricks & Sarah Pekkanen (2018)
   Match Score: 92%
   Why: Domestic suspense, unreliable narrator, husband's secrets
   Market proof: NYT Bestseller, 500K+ copies
   ⚠️ Freshness: 2018 — still relevant but agents prefer 2-5 year window
   Pitch angle: "For readers who loved the twist in The Wife Between Us"

2. "The Last Thing He Told Me" by Laura Dave (2021)
   Match Score: 87%
   Why: Woman uncovering husband's hidden past, atmospheric, slow burn
   Market proof: #1 NYT, Apple TV+ adaptation
   ✅ Freshness: Perfect window
   Pitch angle: "The atmospheric dread of The Last Thing He Told Me meets..."

3. "The Maid" by Nita Prose (2022)
   Match Score: 71%
   Why: Mystery, distinctive voice, slow reveal
   ⚠️ Note: Different subgenre — use only if emphasizing voice/style

═══ SECONDARY COMPS (Marketing & Categories) ═══

4-6. [Additional comps for Amazon categories, BookBub, social media]

═══ COMPS TO AVOID ═══
❌ "Gone Girl" — Too obvious, agents will eye-roll
❌ "The Girl on the Train" — Overused as comp, signals lazy research
❌ Any book 10+ years old (unless a classic touchstone)

═══ QUERY LETTER COMP FORMULA ═══
"THE SILENT HOUR is a 82,000-word psychological thriller —
The Last Thing He Told Me meets The Wife Between Us
with the atmospheric Pacific Northwest setting of Megan Miranda's
The Last House Guest."

Comp Validation

Already have comp titles? Validate them:

  • Relevance check — Does this comp actually match your book?
  • Freshness check — Is this comp too old? (Agents want 2-5 years)
  • Sales check — Did this comp sell well enough to reference?
  • Overuse check — Is every query letter citing this book?
  • Audience overlap — Do readers of this comp want YOUR book?
  • Agent/editor perception — What does citing this comp signal?

Market Positioning Map

Visual map of where your book sits in the competitive landscape:

Market Position Map: Psychological Thriller (Domestic)

DARKER ←────────────────────────→ LIGHTER
  │                                    │
  │  "Behind        ★ YOUR BOOK       │
  │   Closed        "The Silent       │
  │   Doors"         Hour"            │
SLOW │                                │ FAST
BURN │    "The Wife          "The     │ PACED
  │     Between Us"     Maid"         │
  │                                    │
  │  "Gone           "The Last        │
  │   Girl"           Thing He        │
  │                    Told Me"        │
  │                                    │

Your Niche: Dark + Slow Burn quadrant
Competition density: MODERATE (good — not oversaturated)
Reader appetite: HIGH (this quadrant is trending up)

Category Strategy

Recommend optimal Amazon/BISAC categories based on comp analysis:

  1. Primary BISAC — Where your book belongs by content
  2. Strategic BISAC — Where you'll rank fastest
  3. Amazon Browse Categories — Up to 10 specific categories
  4. Keyword Strategy — 7 backend keywords for KDP
  5. Category rank analysis — Competition density per category

Agent Research Integration

When used with comp titles, enhance your query letter research:

  • Which agents repped your comp titles?
  • Which editors acquired them?
  • Which imprints publish in this space?
  • Recent deals in your comp zone (from Publishers Marketplace style analysis)
  • Submission strategy based on comp alignment

Trend Analysis

Analyze whether your book's positioning is trending up or down:

  • Subgenre trajectory — Is domestic suspense growing or saturating?
  • Theme trends — Are unreliable narrators still fresh?
  • Format trends — Are dual timelines hot or tired?
  • Audience sentiment — What are readers asking for on Goodreads/BookTok?

Commands

  • find comps — Full comp title discovery from your book details
  • validate comps [title1, title2] — Check if your comps work
  • market position — Visual positioning map
  • category strategy — Amazon/BISAC category recommendations
  • comp for query — Generate query-letter-ready comp formula
  • trend check [subgenre] — Is your positioning trending up or down?

© Ckokoski, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/_archived/premium/comp-title-finder of Ckokoski/AuthorAgent.

Open the folder on GitHubat commit 47e9570

Compare with similar skills

Comp Title Finder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Comp Title Finder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Comp Title Finder this skillCkokoski/AuthorAgent124—~1.4kAutomated safety check: PassMIT
Marketing OsYuzzyuk/marketing-os540—~2.5kAutomated safety check: PassMIT
Revenue Centric Designheliocosta-dev/revenue-centric-design740—~1.6kAutomated safety check: PassCustom licence
Startup Positioningferdinandobons/startup-skill1.2k—~4.6kAutomated safety check: PassMIT
Stanley Druckenmiller Investmenttradermonty/claude-trading-skills3k1 repos~2kAutomated safety check: PassMIT
B2b Playbookweilun88313/B2B-Playbook203—~3.1kAutomated safety check: PassProprietary

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Categories

Questions about Comp Title Finder

What does Comp Title Finder do?

Find and analyze comparable titles for query letters, marketing, and positioning strategy. Comp Title Finder is an agent skill from Ckokoski/AuthorAgent.

When should I use Comp Title Finder?

Comp Title Finder fits situations like: tasks that involve Positioning and messaging.

How do I install Comp Title Finder in Claude Code?

Run `npx skills add Ckokoski/AuthorAgent --skill comp-title-finder -a claude-code`. Or copy the skill folder (skills/_archived/premium/comp-title-finder in Ckokoski/AuthorAgent) into .claude/skills/comp-title-finder in your project. Claude Code loads it when a task matches its description.

How do I install Comp Title Finder in Codex?

Run `npx skills add Ckokoski/AuthorAgent --skill comp-title-finder -a codex`. Or copy the skill folder (skills/_archived/premium/comp-title-finder in Ckokoski/AuthorAgent) into .agents/skills/comp-title-finder in your project. Codex loads it when a task matches its description.

Can I use Comp Title Finder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Ckokoski/AuthorAgent --skill comp-title-finder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/comp-title-finder, .gemini/skills/comp-title-finder, .github/skills/comp-title-finder and .opencode/skills/comp-title-finder in your project.

What does Comp Title Finder need to run?

SKILL.md names no scripts, command-line tools or credentials: Comp Title Finder is instructions for the agent only.

Does Comp Title Finder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Comp Title Finder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Comp Title Finder use?

Comp Title Finder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Comp Title Finder use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Comp Title Finder?

Skills that share tags, products or a category with Comp Title Finder: Marketing Os (Yuzzyuk/marketing-os, 540 stars), Revenue Centric Design (heliocosta-dev/revenue-centric-design, 740 stars), Startup Positioning (ferdinandobons/startup-skill, 1.2k stars) and Stanley Druckenmiller Investment (tradermonty/claude-trading-skills, 3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Comp Title Finder?

Ckokoski (a GitHub user) maintains it in Ckokoski/AuthorAgent, which has 124 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on July 11, 2026.

Source: Ckokoski/AuthorAgent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.