experimentation
Designs, runs, and reads A/B tests and growth experiments — hypothesis, sample size, duration, and honest interpretation. Use this to plan a test, judge whether a result is real, build an experimentation program, decide what to test next, or diagnose why tests keep producing inconclusive or non-replicating results.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/experimentation && cp -r /tmp/experimentation/plugins/demand-generation/skills/experimentation ~/.claude/skills/experimentationSKILL.md
# Experimentation Most A/B testing programs produce confident conclusions from insufficient data. The discipline is almost entirely in what you do before launch. ## Before running - **Hypothesis with a mechanism.** "Moving the pricing table above the fold will raise trial starts, because visitors currently leave before seeing pricing." Not "let's try a green button." - **One primary metric**, chosen in advance. Secondary metrics are context, never the verdict. - **Sample size calculated in advance**, from your baseline rate and the smallest lift that would change a decision. If the required sample is unreachable, do not run the test — decide by judgment and say so. - **Duration set in advance**, covering at least one full weekly cycle, and two if the buying cycle is long. - **Guardrail metrics** that would make you reject a win: refunds, support volume, downstream retention. ## While running Do not look at results and act on them mid-flight. Peeking and stopping at significance is the single most common way to generate false positives, and it is very effective at it. Check only that the test is running correctly — even split, no broken variant, tracking firing. ## Reading - **At the pre-set duration**, not before, and not extended because it is nearly significant. Extending until significance manufactures it. - **Significance is not size.** A statistically significant 0.3% lift may not be worth shipping. - **Inconclusive is a real result** and the most common one. It means the change did not matter enough to detect, which is useful. - **Check the guardrails** before declaring a win. - **Segment afterward for hypotheses only**, never for verdicts. Slice enough ways and something is always significant. ## Program level Test where the traffic and the leverage are. Most sites can only run a handful of adequately powered tests a year — spend them on structural questions, not button colors. Keep a log of every test: hypothesis, result, decision. Without it, teams re-run the same tests every eighteen months and re-learn the same things. ## Never - Stop a test because it reached significance early. Peeking until it looks conclusive manufactures the result. - Run a test that cannot reach adequate sample size in a reasonable window. Ship the change on judgment instead and say so. - Change more than one variable and attribute the outcome to the one you liked. - Count a flat result as a failure. A well-run test that rules out a plausible idea has bought information.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.