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

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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>
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skills/data-analysis-standard/SKILL.md
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---
name: data-analysis-standard
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".
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."
---
# Data Analysis Standard Skill
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---
## Required Inputs
Ask the user for these if not provided:
- **Metric or question** being investigated
- **Time period** (what changed, from when to when)
- **Data available** (which segments, sources, or queries you have access to)
- **Business context** (what decision this analysis informs)
- **Audience** (who will read this — exec / team / data team)
## Quality Checks
- [ ] Analysis answers all 4 questions: what changed, why, so what, now what
- [ ] Root cause has evidence (not just hypothesis)
- [ ] Confidence level is stated and justified
- [ ] What the data cannot tell us is explicitly named
- [ ] Recommended action includes an owner and timeline
## Guidelines
- Always state what the data *cannot* tell you — never oversell confidence