experimentation-and-ab-testing
About experimentation-and-ab-testing
experimentation-and-ab-testing is a skill for designing controlled social-content tests and drafting variants. It focuses on changing one variable, setting success criteria in advance, and treating organic results as directional evidence.
Key features
- 01Form a one-variable hypothesis
- 02Set metrics and thresholds before publishing
- 03Control test conditions and duration
- 04Draft test variants
- 05Repeat tests and record outcomes
Use cases
- 01Compare alternative hooks for a campaign
- 02Test posting times against a chosen KPI
- 03Evaluate caption or format variations
- 04Use repeated results to update a content playbook
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ social-media-skills/ skills --skill experimentation-and-ab-testingAfter installing, type/experimentation-and-ab-testingin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
evals/evals.jsonreferences/experimentation-2026-reality.mdscope-and-connections.mdthe-test-framework.mdwhat-to-test-and-recipes.mdDetails
From the project READMEexperimentation-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
- brand-profile — voice/format constraints for the variants.
- 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.