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
VERIFIEDOptional — add the marketplace to browse and install other bOpen plugins.
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.