dark-social-attributor
dark-social-attributor is a Marketing analytics & attribution skill for AI agents, published by aaron-he-zhu in the aaron-marketing-skills repository on GitHub. Skillspace rates dark-social-attributor B (81/100) and it passed the safety scan; the rating is based on static analysis only.
Use when the user asks to "figure out where our direct traffic really comes from", "measure dark social", "add a how-did-you-hear-about-us field", or "show social drives signups without click data"; produces a share-link/UTM hygiene spec for owned share surfaces, a self-reported attribution field design that replaces an existing form field (free-text first, coded later), a GA4 direct-traffic decomposition read (deep-URL directs, mobile-app skew, private-push correlation) with every derived number hard-labeled Estimated/proxy, and a branded-search-lift proxy from GSC plus Wikipedia pageviews — the declared dark-social method behind ECHO O2. Not for paid-channel attribution reconciliation (platform-claimed vs analytics conversions) — use attribution-reconciler. 暗社交归因/直接流量分解/自报来源字段/分享链路UTM
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 2,393
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 89 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 1 collections
- Scope
- Portable: works in any project. General dark-social measurement and attribution 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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