sealeap-amazon-keyword-ranking-experiment
sealeap-amazon-keyword-ranking-experiment is a Keyword research skill for AI agents, published by xjli360 in the sealeap-amazon-skills repository on GitHub. Skillspace rates sealeap-amazon-keyword-ranking-experiment C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and head-term tests. Use when the user asks which keywords to push, what an ad position may reveal about order potential, or why organic rank stalls despite paid orders. Do not equate sponsored placement with organic rank or guarantee movement.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 112
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 99 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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