Content performance
Skill nahiddotai/operator-powers/plugins/operator-powers/skills/content-performance
AI skills to operate and grow. Twenty-eight practical powers for non-technical operators.
npx -y skills add nahiddotai/operator-powers --skill content-performanceAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 19 days oldThe repository was created 19 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
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What its author says it does
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Analyze content analytics, traffic, conversions, attribution, audience growth, or published assets to explain performance and choose evidence-based next experiments.
SKILL.md
2.4 KB, as published. Nobody here has run it
Content Performance
Turn available content results into decisions while separating reach, engagement, conversion, and speculation.
Job contract
- Owns: diagnosis from performance evidence.
- Does not own: refreshing one asset without metric analysis (
content-refresher). - Finished deliverable: normalized scorecard, supported findings, uncertainty, and next experiments.
Required data disclosure
If no real performance metrics are available, say so at the top of the response before offering any interpretation:
No real performance data was available. This review uses [name the alternative evidence] instead, so it can assess [what that evidence supports] but not reach, engagement, conversion, revenue, or causality.
Name the result a Content Evidence Review, not a performance review. Do not fill the scorecard with invented proxies. If the user needs performance conclusions, list the smallest data export or fields required to produce them.
Workflow
- Establish the business goal, period, channels, content set, audience size, distribution, and conversion definition.
- Validate units, dates, denominators, attribution windows, missing values, and duplicates.
- Separate reach, attention, engagement, intent, conversion, and revenue.
- Compare like with like. Use rates and baselines where raw totals mislead.
- Look for patterns across topic, format, opening, proof, CTA, timing, and distribution.
- Distinguish confirmed findings, directional indications, and unsupported stories.
- Recommend a small experiment queue with decision rules.
Output
# Content Performance Review
## Goal And Data Quality
[Objective, coverage, gaps]
## Scorecard
[Comparable metrics and baselines]
## What The Evidence Supports
[Findings with numbers]
## What It Does Not Prove
[Attribution and sample limits]
## Keep, Change, Stop, Test
[Specific decisions]
## Next Experiments
[Hypothesis, asset, metric, threshold, duration]
Guardrails
- Empty attribution is not evidence that reach caused sales or subscribers.
- Do not compare raw counts across unequal audience sizes without noting the bias.
- Never invent missing metrics or causal explanations.