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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/ab-test-planner/SKILL.md
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---
name: ab-test-planner
description: Designs statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, run an A/B test, calculate sample size, or interpret test results. Triggers on "A/B test", "experiment", "split test", "statistical significance", "sample size".
description: "Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide."
---
# A/B Test Planner Skill
Design experiments that produce trustworthy results — not just directional signals. Every test output includes hypothesis, success metrics, sample size, duration, and a results interpretation guide.
## Required Inputs
Ask the user for these if not provided:
- **What is being tested** (feature, UI change, copy, pricing, onboarding step)
- **Hypothesis** (or ask to help formulate one)
- **Primary metric** (conversion rate, click-through, completion rate, etc.)
- **Baseline rate** and **minimum detectable effect** (MDE)
- **Daily eligible users** (to calculate duration)
## Experiment Design Checklist
Before running any test, confirm:
@@ -93,3 +102,12 @@ Flag if traffic is too low to reach significance in under 8 weeks — recommend
- If user wants to test multiple variants, explain the multiple comparisons problem and recommend a Bonferroni correction or a Bayesian approach
- If traffic is very low (<1,000 users/day), recommend qualitative alternatives: moderated testing, 5-second tests, or user interviews
- Never approve a test with no guardrail metrics — always protect revenue, retention, or core engagement
## Quality Checks
- [ ] Hypothesis is directional (predicts a specific direction and magnitude, not "let's see")
- [ ] Primary metric is singular (guardrail metrics are secondary)
- [ ] Sample size is calculated from actual MDE and baseline (not guessed)
- [ ] Test duration accounts for weekly seasonality (minimum 2 weeks)
- [ ] Guardrail metrics are defined (at least one to protect revenue or core engagement)
- [ ] Rollback trigger is specified with a concrete threshold