fix(plugins): sync all 171 plugin SKILL.md files with fixed skills/ versions
Propagates Anti-Patterns sections, description rewrites, Required Inputs additions, and Quality Checks format fixes from skills/ to matching plugin SKILL.md copies. https://claude.ai/code/session_01MuGKn3a3Gbqoe8uM5Lmuqt
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@@ -117,6 +117,14 @@ Ask the user for these if not provided:
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- [ ] What the data cannot tell us is explicitly named
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- [ ] Recommended action includes an owner and timeline
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## Anti-Patterns
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- [ ] Do not present correlations as causation — always state the distinction explicitly
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- [ ] Do not report a metric movement without stating the time window and comparison baseline
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- [ ] Do not skip the "so what" — raw observations without recommended actions are incomplete analysis
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- [ ] Do not overstate confidence — label hypotheses clearly and note what data would be needed to confirm them
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- [ ] Do not ignore segment breakdowns — aggregate metrics can mask opposing trends in sub-segments
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## Guidelines
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- Always state what the data *cannot* tell you — never oversell confidence
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@@ -57,3 +57,11 @@ Analyse across four layers:
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- [ ] Every flagged metric has a root cause hypothesis, not just "it dropped"
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- [ ] Observations are written for a non-technical stakeholder (no raw query language or data jargon)
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- [ ] Overall health rating is justified with specific evidence
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## Anti-Patterns
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- [ ] Do not report a single aggregate metric without segment breakdowns — averages hide opposing trends
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- [ ] Do not flag a metric as healthy just because it is above the target — check if the target itself is meaningful
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- [ ] Do not list metric movements without root cause hypotheses — observations without explanations are not analysis
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- [ ] Do not mix product health metrics with business KPIs without explaining the relationship between them
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- [ ] Do not omit recommended actions — a health report that only describes problems without prioritised next steps is incomplete
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@@ -126,6 +126,14 @@ Ask the user for these if not provided:
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- [ ] Churned user interviews are recommended (not just data analysis)
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- [ ] Monitoring plan includes an alert threshold
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## Anti-Patterns
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- [ ] Do not recommend "improve onboarding" without specifying what specific step to change and why
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- [ ] Do not analyse retention without segmenting by cohort — aggregate retention curves hide cohort-specific patterns
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- [ ] Do not treat DAU/MAU below 5% as a retention problem — at that level, it is a product-market fit problem
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- [ ] Do not skip qualitative research — churned user interviews reveal reasons that quantitative data cannot
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- [ ] Do not set a monitoring alert without specifying the threshold that triggers it
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## Guidelines
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- Never recommend "improve onboarding" without specifying *what* to change and *why*
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