Experimentation And Ab Testing logo

Experimentation And Ab Testing

Organization
social-media-skills
experimentation-and-ab-testing

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).

Overview

Publishersocial-media-skills
Repositoryskills
Skill nameexperimentation-and-ab-testing
Stars
215
Forks
44
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by social-media-skills on GitHub. Read the source before you install it.

Installation

Install the Experimentation And Ab Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/social-media-skills/skills/tree/main/skills/experimentation-and-ab-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/social-media-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/experimentation-and-ab-testing .claude/skills/experimentation-and-ab-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Experimentation And Ab Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Experimentation And Ab Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Experimentation And Ab Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Experimentation And Ab Testing AI skill do?

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 t...

Why use Experimentation And Ab Testing on TypingMind?

Because you install it once and use it with any model. Experimentation And Ab Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Experimentation And Ab Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/social-media-skills/skills/tree/main/skills/experimentation-and-ab-testing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Experimentation And Ab Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Experimentation And Ab Testing?

As many as you like. As long as a model supports skills, you can use Experimentation And Ab Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Experimentation And Ab Testing AI skill free?

Yes. It is published on GitHub by social-media-skills under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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