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.
npx skills add cdeistopened/skill-stack --skill amazon-category-researchgit clone --depth 1 https://github.com/cdeistopened/skill-stackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cdeistopened/skill-stack/amazon-category-research)<a href="https://agentmods.dev/skills/cdeistopened/skill-stack/amazon-category-research"><img src="https://agentmods.dev/badge/skills/cdeistopened/skill-stack/amazon-category-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cdeistopened/skill-stack/amazon-category-research"><img src="https://agentmods.dev/badge/skills/cdeistopened/skill-stack/amazon-category-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.00670 |
| Opus 5.5 | $0.00013 | $0.00268 |
| Sonnet 5.5 | $0.00007 | $0.00134 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
amazon-category-research 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Category Research
Guided workflow for finding profitable Amazon book categories with low competition.
Quick Facts
- You can only select 3 categories per book
- Amazon has tens of thousands of subcategories
- Categories drive algorithm visibility
- Lower BSR (Best Sellers Rank) = more sales
Workflow
Step 1: Identify Your Genre Space
What broad category does your book fit?
| Fiction | Nonfiction |
|---|---|
| Romance | Self-help |
| Mystery/Thriller | Business |
| Sci-Fi/Fantasy | Religion/Spirituality |
| Literary Fiction | Health/Fitness |
| Children's/YA | Biography |
Step 2: Research Subcategories
Go to Amazon Best Sellers and drill down:
- Click your main category
- Keep drilling into subcategories until you find a niche
- Example:
Fiction > Fantasy > Short Stories > Coming of Age
Step 3: Analyze #1 and #100
For your target subcategory:
#1 Bestseller:
- Find the book's product page
- Scroll to "Product Details"
- Note the BSR (Best Sellers Rank)
- Plug into Kindlepreneur Calculator
#100 Bestseller:
- Repeat the process
- Compare daily sales estimates
Step 4: Interpret Results
| Signal | Meaning |
|---|---|
| #1 has high sales, #100 has decent sales | Healthy category with room |
| #1 has high sales, #100 has very low sales | Top-heavy, hard to break in |
| Both have low sales | Small market, easy to rank but limited upside |
| Large gap between #1 and #100 | Competition concentrated at top |
Step 5: Competitor Analysis
For top 10 books in your target category, note:
- Price point
- Number of reviews
- Cover design style
- Keywords in title/subtitle
- Page count
Output Template
## Category Research: [Book Title]
### Target Categories (pick 3)
1. [Category path] - BSR range: X-Y, Est. daily sales: X-Y
2. [Category path] - BSR range: X-Y, Est. daily sales: X-Y
3. [Category path] - BSR range: X-Y, Est. daily sales: X-Y
### Competition Analysis
- Avg price: $X
- Avg reviews: X
- Cover style: [description]
- Common keywords: [list]
### Recommendation
[Which 3 categories to select and why]
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.
- 1mo ago First seen · 94 lines · 33 tokens per session scan A 222978b4146e
amazon-category-research is a skill published in the GitHub repository cdeistopened/skill-stack (30 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 670 once invoked, about $0.0001 per session on Opus 5.5. 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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