Managing Experiment Lifecycle
技能 活跃Guides experiment state transitions: launching, pausing, resuming, ending, shipping variants, archiving, resetting, and duplicating. Covers preconditions, implications for variant assignment and analysis, and the decision framework for when to use each action. TRIGGER when: user asks to launch, pause, resume, end, ship, archive, reset, or duplicate an experiment. DO NOT TRIGGER when: user is creating an experiment (use creating-experiments), configuring rollout (use configuring-experiment-rollout), or setting up metrics (use configuring-experiment-analytics).
To provide clear guidance and execute actions for managing the entire lifecycle of product experiments, ensuring correct state transitions and understanding their impact on users and analysis.
功能
- Guides experiment state transitions (launch, pause, resume, end, ship, archive, reset, duplicate)
- Covers preconditions for each action
- Explains implications for variant assignment
- Details impact on statistical analysis
- Provides a decision framework for selecting actions
使用场景
- When a user asks to launch, pause, resume, end, ship, archive, reset, or duplicate an experiment.
- When needing to understand the consequences of an experiment state change on user assignment or data collection.
- When a user needs to clean up or restart an experiment's configuration.
非目标
- Creating new experiments (use `creating-experiments`)
- Configuring experiment rollout (use `configuring-experiment-rollout`)
- Setting up experiment metrics (use `configuring-experiment-analytics`)
工作流
- Identify the desired experiment action (launch, pause, end, etc.).
- Determine the current state and necessary preconditions.
- Execute the corresponding tool with any required parameters (e.g., `variant_key`).
- Review the outcome or error message provided by the tool.
实践
- Experiment lifecycle management
- Product analytics
- A/B testing
- Feature flagging
先决条件
- An experiment ID to operate on
Trust
- warning:Issues AttentionWith 544 open issues and 163 closed issues in the last 90 days, the closure rate is low (approx. 23%), indicating slow maintainer response to open issues.
安装
npx skills add PostHog/posthog通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
类似扩展
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100Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.
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99A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, interpretation, decision-making, and the common failure modes that produce confidently wrong shipping decisions.
OraClaw Bandit
99A/B 测试和功能优化，适用于 AI 代理。使用多臂老虎机和上下文老虎机（LinUCB）自动选择最佳选项。无需数据仓库——直接从请求运行
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98A platform decision framework for experimentation. When to use Statsig vs PostHog vs GrowthBook vs Optimizely vs Amplitude vs Eppo vs Kameleoon. How to migrate between them. How to coordinate when multi-platform is genuinely warranted. The decisions that compound for years and the ones you can defer. Triggers on which experimentation platform, choose Statsig vs PostHog, evaluate experimentation tools, switch experimentation platform, migrate from Optimizely, consolidate experimentation tools, multi-platform experimentation, experimentation platform decision, ab test platform selection, feature flag platform vs experiment platform, warehouse-native experiments, vendor lock-in experimentation. Also triggers when a team is asking about cost, governance, or migration cost across experimentation tools, or when an evaluation is starting.
Ab Test Setup
98When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.