feat: 100 skills milestone — 7 new skills + quality improvements across all 93

New skills added:
- teaching-lesson-plan: structured lesson plans for any subject/audience/setting
- seo-content-brief: complete SEO briefs with intent, competitor gaps, and outline
- media-pitch: story-first journalist pitches with angle development framework
- change-management-plan: stakeholder analysis, comms strategy, adoption metrics
- workshop-facilitation-guide: activity instructions, decision protocols, facilitator moves
- sales-forecasting-model: pipeline model, scenario analysis, assumption log
- tax-planning-checklist: year-end tax planning across income, pension, CGT, reliefs

Quality improvements across all 93 existing skills:
- Standardised description format: "Verb the thing. Use when X. Produces Y."
- Added Required Inputs section to all skills missing it (prompts for missing info)
- Added Quality Checks section to all skills missing it (specific, not generic)
- Fixed broken multiline YAML descriptions
- Removed non-standard frontmatter keys (tool_integration, metadata blocks)

README updated to v6.0.0 with 100-skill count, new skill tables, and article series

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

skills/product-health-analysis/SKILL.md

@@ -1,13 +1,20 @@
 ---
 name: product-health-analysis
-description: Interpret product metrics against goals and surface actionable signals
-tool_integration: Google Analytics, Mixpanel
+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."
 ---
-# Product Health Dashboard Skill
 
-## Purpose
+# Product Health Analysis Skill
+
 Transform raw metrics data into a clear health narrative — what's working, what's not, and what needs immediate attention.
 
+## Required Inputs
+
+Ask the user for these if not provided:
+- **Metrics data** (current values for key metrics — even rough numbers work)
+- **Targets or benchmarks** (OKR targets, historical baselines, or industry benchmarks)
+- **Period** (week / month / quarter being analysed)
+- **Product area or segment** (are we looking at the whole product or a specific feature?)
+
 ## Metrics Framework
 Analyse across four layers:
 1. **Acquisition** — new users, source quality, CAC trends
@@ -21,8 +28,9 @@ Analyse across four layers:
 3. Look for correlations — does a drop in activation explain a retention dip 2 weeks later?
 4. Write a plain-English health summary (no jargon) suitable for sharing with non-data stakeholders
 5. Recommend top 3 areas for immediate investigation with suggested diagnostic steps
+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
 
-## Output Format
+## Output Structure
 
 ### Product Health Report — [Period]
 **Overall Health:** 🟢 On Track / 🟡 Watch / 🔴 Action Required
@@ -41,3 +49,11 @@ Analyse across four layers:
 
 **Recommended Actions:**
 [Specific next steps with owners and timelines]
+
+## Quality Checks
+
+- [ ] Every metric includes both a target and a trend (not just a snapshot)
+- [ ] At least one correlation is drawn between metrics (e.g., activation → retention)
+- [ ] Every flagged metric has a root cause hypothesis, not just "it dropped"
+- [ ] Observations are written for a non-technical stakeholder (no raw query language or data jargon)
+- [ ] Overall health rating is justified with specific evidence