fix: sync all skill updates and new skills into plugin bundles
- Synced 97 existing skill SKILL.md files from skills/ to their plugin bundle copies - Added 7 new skills to plugin bundles: - seo-content-brief, media-pitch -> pm-gtm - tax-planning-checklist -> pm-finance - change-management-plan -> pm-hr - sales-forecasting-model -> pm-sales - workshop-facilitation-guide -> pm-operations - teaching-lesson-plan -> pm-cross Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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mohitagw15856
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---
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name: data-analysis-standard
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description: Structures product data analysis, metric deep-dives, funnel analysis, and cohort studies. Use when asked to analyse product metrics, investigate a drop in conversion, build a dashboard spec, or explain data to stakeholders. Triggers on "analyse metrics", "funnel analysis", "cohort analysis", "data deep dive", "why did X drop".
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description: "Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action."
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---
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# Data Analysis Standard Skill
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---
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## Required Inputs
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Ask the user for these if not provided:
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- **Metric or question** being investigated
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- **Time period** (what changed, from when to when)
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- **Data available** (which segments, sources, or queries you have access to)
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- **Business context** (what decision this analysis informs)
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- **Audience** (who will read this — exec / team / data team)
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## Quality Checks
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- [ ] Analysis answers all 4 questions: what changed, why, so what, now what
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- [ ] Root cause has evidence (not just hypothesis)
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- [ ] Confidence level is stated and justified
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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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## Guidelines
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- Always state what the data *cannot* tell you — never oversell confidence
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---
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name: product-health-analysis
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description: Interpret product metrics against goals and surface actionable signals
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tool_integration: Google Analytics, Mixpanel
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description: "Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions."
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---
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# Product Health Dashboard Skill
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## Purpose
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# Product Health Analysis Skill
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Transform raw metrics data into a clear health narrative — what's working, what's not, and what needs immediate attention.
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## Required Inputs
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Ask the user for these if not provided:
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- **Metrics data** (current values for key metrics — even rough numbers work)
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- **Targets or benchmarks** (OKR targets, historical baselines, or industry benchmarks)
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- **Period** (week / month / quarter being analysed)
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- **Product area or segment** (are we looking at the whole product or a specific feature?)
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## Metrics Framework
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Analyse across four layers:
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1. **Acquisition** — new users, source quality, CAC trends
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3. Look for correlations — does a drop in activation explain a retention dip 2 weeks later?
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4. Write a plain-English health summary (no jargon) suitable for sharing with non-data stakeholders
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5. Recommend top 3 areas for immediate investigation with suggested diagnostic steps
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6. **Validate** — Confirm every flagged metric has a plausible root cause hypothesis, not just a raw number, and every recommended action has a specific owner or team
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## Output Format
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## Output Structure
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### Product Health Report — [Period]
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**Overall Health:** 🟢 On Track / 🟡 Watch / 🔴 Action Required
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**Recommended Actions:**
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[Specific next steps with owners and timelines]
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## Quality Checks
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- [ ] Every metric includes both a target and a trend (not just a snapshot)
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- [ ] At least one correlation is drawn between metrics (e.g., activation → retention)
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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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---
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name: retention-analysis
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description: Structures retention analysis, churn investigations, and engagement deep-dives for product teams. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Triggers on "retention analysis", "churn", "DAU/MAU", "user retention", "why are users leaving".
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description: "Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions."
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---
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# Retention Analysis Skill
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@@ -108,6 +108,24 @@ Users who [specific action] in first [N] days retain at [X%] vs [Y%] for those w
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---
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## Required Inputs
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Ask the user for these if not provided:
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- **Product and business model** (SaaS / consumer app / marketplace / other)
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- **Current retention metrics** (D1, D7, D30 if available)
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- **Segment to analyse** (all users / paid / free / a specific cohort)
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- **Key question to answer** (why is retention dropping? what drives retention?)
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- **Available data** (analytics events, churn surveys, interview notes)
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## Quality Checks
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- [ ] Retention curve shape is diagnosed (flattening vs trending to zero = PMF vs onboarding)
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- [ ] Cohorts are segmented before analysis (not all users lumped together)
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- [ ] "Aha moment" correlation is identified or flagged as unknown
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- [ ] Interventions are specific (not "improve onboarding")
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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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## Guidelines
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- Never recommend "improve onboarding" without specifying *what* to change and *why*
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