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feat: v2.0 skill renames and CRO consolidation (#291)

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* feat: v2.0 skill renames and CRO consolidation

BREAKING CHANGE: Users must reinstall skills after this update.

## Skill Renames (16)
- ab-test-setup → ab-testing
- analytics-tracking → analytics
- aso-audit → aso
- competitor-alternatives → competitors
- email-sequence → emails
- free-tool-strategy → free-tools
- launch-strategy → launch
- onboarding-cro → onboarding
- paywall-upgrade-cro → paywalls
- popup-cro → popups
- pricing-strategy → pricing
- product-marketing-context → product-marketing
- referral-program → referrals
- schema-markup → schema
- signup-flow-cro → signup
- social-content → social

## Consolidations (1)
- page-cro + form-cro → cro (form content in references/form.md)

## Why 2.0?
- Shorter, cleaner skill names
- Consistent naming (no -strategy, -setup, -cro suffixes)
- All cross-references updated across 100+ files

Total skills: 40

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* fix(v2.0): update evals for renamed skills, fix validate script, clear warnings

- Update 32 evals.json files to reference new skill names (page-cro → cro,
  product-marketing-context → product-marketing, etc.) — these were missed
  in the initial v2.0 rename pass since only SKILL.md and marketplace.json
  were updated.
- Fix validate-skills.sh: replace GNU-only `head -n -1` with portable awk
  so frontmatter extraction works on macOS.
- Move Copy Editing Checklist (56 lines) to references/checklist.md to
  bring copy-editing SKILL.md under the 500-line limit (508 → 457).
- Add "see X" pointers to marketing-psychology description for skill
  discovery (cro, pricing, copywriting).
- Update skill-request.yml issue template placeholder (page-cro → cro).

All 40 skills now pass validation with zero warnings.

* fix(v2.0): add evals for 8 missing skills, strip stale frontmatter from cro/form.md

Adds 48 new eval cases (6 per skill) for skills that previously had no evals:
aso, co-marketing, community-marketing, competitor-profiling, directory-submissions,
image, lead-magnets, video. All 40 skills now have eval coverage (251 total cases).

Strips leftover frontmatter from skills/cro/references/form.md — it was inherited
from the old form-cro SKILL.md before consolidation. Reference files don't need
frontmatter.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* fix(v2.0): rename paid-ads → ads

One more v2.0 simplification — drops the redundant 'paid-' qualifier. Updates
the skill directory, SKILL.md frontmatter, evals.json, README skill table, the
v2.0 rename table in VERSIONS.md (now 17 renames), and all cross-references in
related skills (ad-creative, aso, competitor-profiling, customer-research,
lead-magnets, marketing-ideas) plus the tools/integrations guides.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* fix(v2.0): bump SKILL.md frontmatter version to 2.0.0 for all 40 skills

VERSIONS.md was already updated to 2.0.0 but the metadata.version field inside
each SKILL.md was still on 1.x. That mismatch would have caused the update-check
flow to perpetually report 'update available' since it compares VERSIONS.md
against local SKILL.md metadata versions.

Caught by codex review (P1).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* fix(v2.0): add legacy product-marketing-context.md filename fallback

Before this fix, users upgrading from v1.x who had a `product-marketing-context.md`
file would lose automatic context loading — every skill only checked the new
`product-marketing.md` filename. Now all 40 skills also accept the legacy
filename (in either `.agents/` or `.claude/`), and the README migration command
covers both legacy and current filenames.

Caught by codex review (P1 + P2).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* fix(v2.0): re-sort README skills table alphabetically, fix ads box width

The skills table had a few entries out of alphabetical order from the renames
(co-marketing was after cold-email, ads was at the renamed position).
Re-sorted alphabetically per sync-skills.js. Also padded the 'ads' cell in the
ASCII flow diagram to keep the box width consistent.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* fix(v2.0): document folder cleanup on upgrade, stop sync-skills from re-adding skills array

README upgrade guide now includes:
- A clear cleanup step for stale v1.x skill folders (renamed + consolidated) so
  users don't end up with both old and new folders side-by-side after upgrading
- The full v1 to v2 rename map for reference
- Existing product-marketing-context.md migration steps (preserved)

sync-skills.js no longer (re-)introduces a `skills` array on marketplace.json --
Claude Code's plugin schema discovers skills via the `skills/` directory, and the
explicit array was failing validation. The script now refreshes the description
count and strips the stale array if present.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Corey Haines
2026-05-13 22:05:15 -07:00
committed by GitHub
parent f3990f7f99
commit 30f9b9a729
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skills/ab-testing/references/sample-size-guide.md
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# Sample Size Guide
Reference for calculating sample sizes and test duration.
## Contents
- Sample Size Fundamentals (required inputs, what these mean)
- Sample Size Quick Reference Tables
- Duration Calculator (formula, examples, minimum duration rules, maximum duration guidelines)
- Online Calculators
- Adjusting for Multiple Variants
- Common Sample Size Mistakes
- When Sample Size Requirements Are Too High
- Sequential Testing
- Quick Decision Framework
## Sample Size Fundamentals
### Required Inputs
1. **Baseline conversion rate**: Your current rate
2. **Minimum detectable effect (MDE)**: Smallest change worth detecting
3. **Statistical significance level**: Usually 95% (α = 0.05)
4. **Statistical power**: Usually 80% (β = 0.20)
### What These Mean
**Baseline conversion rate**: If your page converts at 5%, that's your baseline.
**MDE (Minimum Detectable Effect)**: The smallest improvement you care about detecting. Set this based on:
- Business impact (is a 5% lift meaningful?)
- Implementation cost (worth the effort?)
- Realistic expectations (what have past tests shown?)
**Statistical significance (95%)**: Means there's less than 5% chance the observed difference is due to random chance.
**Statistical power (80%)**: Means if there's a real effect of size MDE, you have 80% chance of detecting it.
---
## Sample Size Quick Reference Tables
### Conversion Rate: 1%
| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (1% → 1.05%) | 1,500,000 | 3,000,000 |
| 10% (1% → 1.1%) | 380,000 | 760,000 |
| 20% (1% → 1.2%) | 97,000 | 194,000 |
| 50% (1% → 1.5%) | 16,000 | 32,000 |
| 100% (1% → 2%) | 4,200 | 8,400 |
### Conversion Rate: 3%
| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (3% → 3.15%) | 480,000 | 960,000 |
| 10% (3% → 3.3%) | 120,000 | 240,000 |
| 20% (3% → 3.6%) | 31,000 | 62,000 |
| 50% (3% → 4.5%) | 5,200 | 10,400 |
| 100% (3% → 6%) | 1,400 | 2,800 |
### Conversion Rate: 5%
| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (5% → 5.25%) | 280,000 | 560,000 |
| 10% (5% → 5.5%) | 72,000 | 144,000 |
| 20% (5% → 6%) | 18,000 | 36,000 |
| 50% (5% → 7.5%) | 3,100 | 6,200 |
| 100% (5% → 10%) | 810 | 1,620 |
### Conversion Rate: 10%
| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (10% → 10.5%) | 130,000 | 260,000 |
| 10% (10% → 11%) | 34,000 | 68,000 |
| 20% (10% → 12%) | 8,700 | 17,400 |
| 50% (10% → 15%) | 1,500 | 3,000 |
| 100% (10% → 20%) | 400 | 800 |
### Conversion Rate: 20%
| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (20% → 21%) | 60,000 | 120,000 |
| 10% (20% → 22%) | 16,000 | 32,000 |
| 20% (20% → 24%) | 4,000 | 8,000 |
| 50% (20% → 30%) | 700 | 1,400 |
| 100% (20% → 40%) | 200 | 400 |
---
## Duration Calculator
### Formula
```
Duration (days) = (Sample per variant × Number of variants) / (Daily traffic × % exposed)
```
### Examples
**Scenario 1: High-traffic page**
- Need: 10,000 per variant (2 variants = 20,000 total)
- Daily traffic: 5,000 visitors
- 100% exposed to test
- Duration: 20,000 / 5,000 = **4 days**
**Scenario 2: Medium-traffic page**
- Need: 30,000 per variant (60,000 total)
- Daily traffic: 2,000 visitors
- 100% exposed
- Duration: 60,000 / 2,000 = **30 days**
**Scenario 3: Low-traffic with partial exposure**
- Need: 15,000 per variant (30,000 total)
- Daily traffic: 500 visitors
- 50% exposed to test
- Effective daily: 250
- Duration: 30,000 / 250 = **120 days** (too long!)
### Minimum Duration Rules
Even with sufficient sample size, run tests for at least:
- **1 full week**: To capture day-of-week variation
- **2 business cycles**: If B2B (weekday vs. weekend patterns)
- **Through paydays**: If e-commerce (beginning/end of month)
### Maximum Duration Guidelines
Avoid running tests longer than 4-8 weeks:
- Novelty effects wear off
- External factors intervene
- Opportunity cost of other tests
---
## Online Calculators
### Recommended Tools
**Evan Miller's Calculator**
https://www.evanmiller.org/ab-testing/sample-size.html
- Simple interface
- Bookmark-worthy
**Optimizely's Calculator**
https://www.optimizely.com/sample-size-calculator/
- Business-friendly language
- Duration estimates
**AB Test Guide Calculator**
https://www.abtestguide.com/calc/
- Includes Bayesian option
- Multiple test types
**VWO Duration Calculator**
https://vwo.com/tools/ab-test-duration-calculator/
- Duration-focused
- Good for planning
---
## Adjusting for Multiple Variants
With more than 2 variants (A/B/n tests), you need more sample:
| Variants | Multiplier |
|----------|------------|
| 2 (A/B) | 1x |
| 3 (A/B/C) | ~1.5x |
| 4 (A/B/C/D) | ~2x |
| 5+ | Consider reducing variants |
**Why?** More comparisons increase chance of false positives. You're comparing:
- A vs B
- A vs C
- B vs C (sometimes)
Apply Bonferroni correction or use tools that handle this automatically.
---
## Common Sample Size Mistakes
### 1. Underpowered tests
**Problem**: Not enough sample to detect realistic effects
**Fix**: Be realistic about MDE, get more traffic, or don't test
### 2. Overpowered tests
**Problem**: Waiting for sample size when you already have significance
**Fix**: This is actually fine—you committed to sample size, honor it
### 3. Wrong baseline rate
**Problem**: Using wrong conversion rate for calculation
**Fix**: Use the specific metric and page, not site-wide averages
### 4. Ignoring segments
**Problem**: Calculating for full traffic, then analyzing segments
**Fix**: If you plan segment analysis, calculate sample for smallest segment
### 5. Testing too many things
**Problem**: Dividing traffic too many ways
**Fix**: Prioritize ruthlessly, run fewer concurrent tests
---
## When Sample Size Requirements Are Too High
Options when you can't get enough traffic:
1. **Increase MDE**: Accept only detecting larger effects (20%+ lift)
2. **Lower confidence**: Use 90% instead of 95% (risky, document it)
3. **Reduce variants**: Test only the most promising variant
4. **Combine traffic**: Test across multiple similar pages
5. **Test upstream**: Test earlier in funnel where traffic is higher
6. **Don't test**: Make decision based on qualitative data instead
7. **Longer test**: Accept longer duration (weeks/months)
---
## Sequential Testing
If you must check results before reaching sample size:
### What is it?
Statistical method that adjusts for multiple looks at data.
### When to use
- High-risk changes
- Need to stop bad variants early
- Time-sensitive decisions
### Tools that support it
- Optimizely (Stats Accelerator)
- VWO (SmartStats)
- PostHog (Bayesian approach)
### Tradeoff
- More flexibility to stop early
- Slightly larger sample size requirement
- More complex analysis
---
## Quick Decision Framework
### Can I run this test?
```
Daily traffic to page: _____
Baseline conversion rate: _____
MDE I care about: _____
Sample needed per variant: _____ (from tables above)
Days to run: Sample / Daily traffic = _____
If days > 60: Consider alternatives
If days > 30: Acceptable for high-impact tests
If days < 14: Likely feasible
If days < 7: Easy to run, consider running longer anyway
```