hook-methodology
hook-methodology is a Paid ads & campaigns skill for AI agents, published by DV0x in the creative-ad-agent repository on GitHub. Skillspace rates hook-methodology C (73/100) and it passed the safety scan; the rating is based on static analysis only.
Generates conversion-focused ad copy through research-first extraction. Creates unique hook concepts by mining EVERY section of the research document. Use when creating Meta ads, conversion campaigns, or scroll-stopping copy.
- Grade
- C
- Quality
- 73 / 100
- Safety
- Passed
- GitHub stars
- 107
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
- 97 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 1 other repositories, in 0 collections
- Scope
- Portable: works in any project. General research-first ad hook writing methodology
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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