content-scorer
This skill should be used when the user asks to "score this marketing copy", "is this content good enough to ship", "recursively improve this until it scores 90+", "check against rejected patterns", "run a content quality gate", or wants deterministic, repeatable scoring of marketing or product copy against a rubric that remembers what got rejected before. Use it any time copy needs a PASS/REVISE verdict before shipping.

Complete plugin installation is recommended so this skill keeps its agents, hooks, commands, and runtime context.
- PUBLISHER
- b-open-io
- RELATIONSHIP
- authored
- VERSION
- 0.1.0
- BENCHMARK
- unknown
Install product-skills
The complete plugin is the supported path. It preserves everything the publisher designed to work alongside this skill.
Codex
VERIFIEDGrok Build
VERIFIED- Grok requires the full @b-open-io/claude-plugins marketplace qualifier when installing.
Install only this skill
Use this narrower path only when you intentionally want the portable SKILL.md without the plugin’s surrounding capabilities.
Skills CLI
VERIFIED- Installs only the portable skill; it omits plugin hooks, agents, commands, apps/MCP configuration, and unlisted companion skills.
Trace it to the source.
- DISTRIBUTED SOURCE
- skills/content-scorer/SKILL.md ↗
- UPSTREAM SOURCE
- No separate upstream declared
- DISTRIBUTED DIGEST
- sha256:f89183c2aa4105fe8c0a91cecd2f202ca6f915ae10deebb685d7ece077fdc628
- LOCK HASH
- Not applicable
Companion skills
No required companion skills are declared.
Agents using this skill
Share this skill.
Pass the canonical page to a teammate or keep it close for later.
experiment-stats
This skill should be used when the user asks 'is this A/B test significant', 'did the variant actually win', 'compute lift and confidence interval', 'score a marketing experiment', 'run the promote-gate', or wants a statistically defensible promote/hold/reject call on an A/B or multivariate marketing experiment. Computes lift, a bootstrap confidence interval, and a significance test (Mann-Whitney U or two-proportion z) using pure Python standard library, without scipy or numpy. Every p-value is computed from the input data, never estimated. Use before promoting any experiment variant to production.