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creating-experiments

Guides agents through the 3-step experiment creation flow: defining the hypothesis, configuring rollout, and setting up analytics. Delegates rollout decisions to configuring-experiment-rollout and metric setup to configuring-experiment-analytics.\nTRIGGER when: user asks to create a new experiment or A/B test, OR when you are about to call experiment-create.\nDO NOT TRIGGER when: user is updating an existing experiment, managing lifecycle, or only browsing experiments.

Stars
34,779
Source
PostHog/posthog
Updated
2026-05-31
Slug
PostHog--posthog--creating-experiments
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/PostHog/posthog/HEAD/products/experiments/skills/creating-experiments/SKILL.md -o .claude/skills/creating-experiments.md

Drops the SKILL.md into .claude/skills/creating-experiments.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

Creating experiments

This skill walks through the 3-step flow for creating a new A/B test experiment.

Core principle: draft first, iterate on details

Create the experiment as a draft quickly, then iterate on metrics and configuration. The user gets a tangible draft immediately and can refine it.

The 3-step creation flow

Step 1: What are we testing?

Gather these before calling experiment-create:

  • Experiment name — descriptive, inferred from context when possible
  • Hypothesis — what you expect to happen (goes in description)
  • Feature flag key — kebab-case. Ask if they want a new flag or to reuse an existing one. The flag is auto-created — do NOT create one separately.
  • Type — leave empty (will internally default to "product". The "web" value is reserved for no-code experiments configured visually with the PostHog toolbar in a browser; it cannot be meaningfully driven via MCP. If a user asks for a no-code/toolbar experiment, point them to the PostHog UI instead of creating one here.)

If the user gives enough context to infer these, don't ask — just proceed.

Step 2: Who sees what variant?

This is about rollout configuration.

Before asking any rollout question, load configuring-experiment-rollout. The disambiguation wording, recommendations, and post-answer branches live there — do not formulate rollout questions yourself, and do not assume an example you remember covers the user's path.

Key decision points (covered in detail by configuring-experiment-rollout):

  • Variant split (how many variants, what percentage each)
  • Overall rollout percentage (what % of all users enter the experiment)
  • Whether to persist the flag across authentication steps

If the user doesn't mention rollout specifics, use defaults: 50/50 control/test, 100% rollout.

Step 3: How to measure impact?

This is about analytics and metrics. Load the configuring-experiment-analytics skill for guidance. That skill's first step checks for an existing shared metric to reuse before building a new one — don't duplicate a metric the project already has set up.

Do NOT configure metrics on creation. Metrics are not passed to experiment-create — they are added afterwards via experiment-update. This keeps the creation call lightweight.

When the user specifies metrics upfront, acknowledge them and add them immediately after creation. When they don't, create the draft and then guide them through metric setup as a follow-up.

How to create

Call experiment-create with:

{
  "name": "Descriptive experiment name",
  "feature_flag_key": "kebab-case-key",
  "description": "Hypothesis: [what you expect to happen]",
  "feature_flag": {
    "filters": {
      "multivariate": {
        "variants": [
          { "key": "control", "name": "Control", "rollout_percentage": 50 },
          { "key": "test", "name": "Test", "rollout_percentage": 50 }
        ]
      },
      "groups": [{ "properties": [], "rollout_percentage": 100 }]
    },
    "ensure_experience_continuity": false
  }
}

Flag config goes in the feature_flag object, in the flag's own filters shape (not the deprecated parameters keys). Two different percentages live in there, do NOT mix them up:

  • filters.multivariate.variants[].rollout_percentage is how users inside the experiment are split across variants (must sum to 100, recommended to have an even split).
  • filters.groups[0].rollout_percentage is the overall gate: what fraction of all users enter the experiment at all (0-100, defaults to 100).

Key details:

  • Exactly one variant key must be the literal string "control" (the baseline). Minimum 2, maximum 20 variants.
  • filters.groups[0].rollout_percentage defaults to 100 if omitted.
  • ensure_experience_continuity persists a user's variant across authentication steps; leave it false unless the flag is shown to both logged-out and logged-in users (see configuring-experiment-rollout).
  • Stats default to Bayesian. Only set stats_config if the user requests Frequentist.

After creation

  1. Always show the experiment URL. The experiment-create response includes _posthogUrl — always display this link so the user can view and configure the experiment in the UI.

  2. Remind the user to implement the feature flag in code. Link to the experiment page and say "implement the flag as shown here" — the experiment detail page shows implementation snippets for the user's SDK.

  3. Guide through metrics if not yet configured — load the configuring-experiment-analytics skill.

  4. Launch when ready — use the experiment-launch tool.