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experimentation-and-ab-testing — Claude Code skill: honest content

Designs disciplined organic A/B tests for social content: one variable, a decision rule set before publishing, repetitions. Settles content debates with evidence.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add social-media-skills/skills --skill experimentation-and-ab-testing -a claude-code
Open on GitHub

What experimentation-and-ab-testing does

The skill designs the test and drafts the variants. It reads brand-profile and goals-and-kpis first, so the hypothesis is tied to a real KPI and the variants match your voice. Then it works through the TEST framework: target one variable, establish the decision rule first, set controls and sample, tally and repeat. You change one thing — hook, first frame, caption, CTA, posting time or format — and keep everything else identical.

WoopSocial publishes the variants as controlled sequential posts. There is no audience-split surface, with one exception: YouTube's native Test & Compare in YouTube Studio for long-form video, up to 3 titles, thumbnails or title+thumbnail combos. Results come from native analytics via analytics-and-reporting. Organic reach can't reach clean statistical significance, so the skill leans on tight controls, a roughly 20% effect threshold and 3–5 paired repetitions instead.

The decision rule is set before publishing, and that part matters: set it afterwards and any outcome can be rationalised into a win. Nothing gets fabricated, and a tie is a valid result. This is not analytics-and-reporting, which measures what happened, nor goals-and-kpis, which sets the target.

What it helps with

  • settle which of two hooks actually drives reach
  • lock a win condition before publishing so the readout stays honest
  • pick a posting time without guessing
  • test a format or CTA inside a short sprint
  • tell a lucky viral post apart from a repeatable pattern

Example prompts for the agent

  • Design an A/B test for this Reels hook: one variable, metric and win threshold set before publishing
  • What should I test next? Read brand-profile and goals-and-kpis first
  • Read the title test results — is this signal or noise?

How to install experimentation-and-ab-testing

1Skills CLI (Vercel)

npx skills add social-media-skills/skills --skill experimentation-and-ab-testing -a claude-code

2Manually: put the files in place

~/.claude/skills/experimentation-and-ab-testing/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/experimentation-and-ab-testing/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about experimentation-and-ab-testing

How do I install experimentation-and-ab-testing in Claude?

Run npx skills add social-media-skills/skills --skill experimentation-and-ab-testing -a claude-code. The skill lands in ~/.claude/skills/experimentation-and-ab-testing/SKILL.md.

What does experimentation-and-ab-testing do?

It designs organic A/B tests for content: one variable, controlled context, a decision rule set before publishing, 3–5 paired repetitions. It also drafts the variants; WoopSocial schedules them.

How is it different from analytics-and-reporting?

This skill manipulates one variable to establish causation. analytics-and-reporting observes and measures what already happened, while goals-and-kpis sets the target metric.

Source text

SKILL.md · MIT
---
name: experimentation-and-ab-testing
description: >-
  A/B testing and experimentation for social media content — evidence over opinion, at honest
  organic-scale rigor. Use when someone wants to "A/B test" or "split test" content, "test which
  version/hook/time/caption/thumbnail works," "set up an experiment," "what should I test," or to
  settle a content debate with evidence instead of opinion. Designs disciplined organic tests: one
  variable, controlled context, a decision rule set BEFORE publishing, enough duration and
  repetitions to separate signal from noise. Uses the TEST framework. Reads brand-profile +
  goals-and-kpis first. It DESIGNS the test and drafts variants; WoopSocial schedules them as
  controlled sequential posts (exception: YouTube's native Test & Compare); the result is read from
  native analytics via analytics-and-reporting. Organic can't reach true statistical significance;
  nothing is fabricated or p-hacked. Distinct from analytics-and-reporting (measures) and
  goals-and-kpis (sets targets).
version: 1.0.0
---

# experimentation-and-ab-testing

The **causation engine** — manipulate one variable under controlled conditions to learn what actually
moves a KPI. This skill **designs** the test and drafts variants; **scheduling-and-queue → WoopSocial**
publishes them; **analytics-and-reporting** reads the result.

## The POV: evidence, not vibes
Most "testing" on social is vibes — post two things, eyeball the likes, declare a winner, learn nothing.
Real experimentation turns guesses into evidence: change **one variable**, control everything else, set
the **decision rule before you publish**, and run it **long and often enough** to separate signal from
noise. Organic can't give clean statistical significance (small samples, an algorithm in the middle), so
you compensate with tighter controls, a **~20%+ effect threshold**, **guardrail metrics**, and **3–5
repetitions** — and treat a single viral post as **noise, not a strategy.**

## Read these first
1. **brand-profile** — voice/format constraints for the variants.
2. **goals-and-kpis** — the **KPI/primary metric** the test must move.

## The framework: TEST
(Depth: `references/the-test-framework.md`.)
- **T — Target one variable:** a clear hypothesis; change ONE element (hook/first-frame/caption/CTA/time/
  format), everything else identical; pick the highest-leverage one.
- **E — Establish the decision rule first:** set the **primary metric + win threshold + guardrail** before
  publishing ("B wins if reach +15% and saves/reach not worse"); no post-hoc rationalizing.
- **S — Set controls + sample:** same platform/format/topic/length/window; run ≥7 days (small accounts
  2–4 weeks); judge on a **~20%+ consistent effect** (a tie = "test elsewhere").
- **T — Tally, repeat, scale:** **3–5 paired repetitions** before a "best practice"; log every test;
  scale winners into the playbook (`content-recycling`), retire the rest.

## What to test (highest leverage, in your control)
Hook/first-frame (short video) → posting time (easy) → format → caption/CTA → thumbnail → hashtags —
always tied to the KPI; test what's **in your control**, not algorithm-dependent factors. Run a **30-day
sprint** with one test always running. Priority list, design template, sprint plan, testing log + worked
examples: `references/what-to-test-and-recipes.md`. Full method + rules:
`references/experimentation-2026-reality.md`.

## Honest scope (never violate)
- **Organic isn't lab-grade** — results are **directional**; compensate with controls + effect-size +
  repetition, not p-value theater.
- **WoopSocial has no A/B/audience-split surface** → organic testing = **controlled sequential posts**;
  the agent designs + drafts variants + schedules; the **primary metric is read from native analytics**
  (`analytics-and-reporting`). **One true native split exists: YouTube's Test & Compare** (YouTube
  Studio, long-form, not Shorts) — up to **3 titles, thumbnails, or title+thumbnail combos**; use it
  for YouTube title/thumbnail tests instead of sequential posts. (verify-quarterly)
- **No p-hacking / HARKing / cherry-picking** — decision rule pre-set; a multi-variable change can't be
  pinned on one element; one post/one day is noise. **Never fabricate a result; a tie is valid.**
  (Scope, the loop role + connections: `references/scope-and-connections.md`.)

## Distinct from its siblings (route correctly)
**experimentation (this)** = manipulate one variable to establish **causation** · **analytics-and-reporting**
= observe/measure what happened · **goals-and-kpis** = set the target/primary metric · **content-recycling**
= scale proven winners · **viral-reverse-engineering** = explain a *past* post (hindsight) vs testing forward.

## Where this connects
Reads first: **brand-profile**, **goals-and-kpis**. Variants drafted via: **hook-writer**, **caption-writer**,
**reels-script**/**tiktok-script**, **carousel-writer**, **image-prompt**/**ideogram**/**nano-banana**,
**thumbnail-design**. Readout: **analytics-and-reporting** (native analytics). Scale/plan:
**content-recycling**, **social-strategy**, **content-calendar**/**batch-content-plan**, every **\*-growth**
skill. Publish variants: **scheduling-and-queue → WoopSocial** (controlled sequential posts).

## Definition of done
A clear hypothesis testing ONE variable tied to a KPI; identical controlled context; a primary metric +
win threshold + guardrail set **before** publishing; duration ≥7 days (2–4 weeks small accounts) and 3–5
paired repetitions; results read from native analytics and judged on a ~20%+ consistent effect (ties
acknowledged); winners logged and scaled to content-recycling/strategy; organic limits stated, nothing
fabricated or p-hacked, correctly distinguished from analytics-and-reporting and goals-and-kpis.

© social-media-skills · MIT · social-media-skills/skills

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