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Find your next superpower If you are an agent, refer to our llms.txt for full access.Creates and updates site infrastructure and metadata for hosting or publishing UI Bundle apps. Configures guest access, default locales, and supported languages through site language settings.
Turn assumptions, funnel opportunities, and product questions into a prioritised, feasibility-checked experiment backlog. Filters by traffic reality, metric latency, and method feasibility — not just ICE/RICE scoring. Maintains a living portfolio with status (idea → designed → running → readout → archived). Load when the user says "what should we test next", "build an experiment backlog", "priorit…
Writes machine learning experiment code for research papers, including training and evaluation pipelines. Debugs failures and uses code reflection to iteratively improve results.
Structures experiment plans into initial implementation, baseline tuning, creative research, and ablation stages. Specifies datasets, comparison baselines, hyperparameter sweeps, and evaluation metrics.
Analyse experiment results, run validity checks (SRM, exposure parity, data integrity, novelty/primacy), interpret causally, make a ship/iterate/kill decision against the pre-declared rule, and append to cumulative learnings. Forces honest readouts — strips significance claims from underpowered or peek-violating tests; never lets directional results masquerade as causal wins. Load when results exi…
Translate an approved experiment spec into a launch runbook — platform binding (PostHog primary), feature flag setup, assignment unit, exposure event definition, instrumentation QA, dashboard wiring, ramp plan, monitoring, and rollback procedure. Platform-agnostic core with one strong PostHog adapter shipped; GrowthBook, Statsig, LaunchDarkly, Optimizely, and Eppo documented as a single mapping ta…
Write a rigorous, decision-grade experiment spec — falsifiable hypothesis, primary metric, guardrails, randomisation unit, exposure definition, method (A/B, holdout, switchback, quasi-experiment, MAB), MDE/duration plan, peek policy, validity threats, and pre-committed decision rule. Platform-agnostic. Load when the user has a candidate experiment and needs to spec it before launch, or says "spec …
Orchestrator for the experimentation skill suite — turn assumptions and product questions into rigorous, well-instrumented experiments and decision-grade readouts. Routes through backlog → spec → runbook → readout based on user need and existing artefacts. Platform-agnostic with PostHog as the primary binding. Load when the user asks to design an experiment, A/B test something, set up an experimen…
Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.
Supports understanding the construction of interfaces and other elements found on the web.
Drives a real browser through a repository, product page, documentation site, blog post, or other URL. Composites the walkthrough and lip-synced avatar in a macOS-style frame; GitHub links add README scanning and live-demo detection.
Honest ingest of https://explainx.ai/trending into the trading lab: parse live page-view scores, map onto existing rails, two-ceiling honesty, planner vs executor split. Never auto-install. Never clone ExplainX.
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npx skillycli add owner/repo --skill name