experimentation-platform-orchestrator
A 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
Profile →npx skills add rampstackco/claude-skills --skill experimentation-platform-orchestrator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Instructs a senior leader to apply a decision framework for choosing among experimentation platforms, planning migrations, and coordinating multi-platform use. It emphasizes seven considerations (data architecture, statistical rigor, MCP availability, feature-flag integration, analytics depth, governance and audit, cost shape) and provides per-platform profiles (Statsig, PostHog, GrowthBook, Optimizely, Amplitude, Eppo, Kameleoon) with strengths, gotchas, and ideal customers. It also includes a decision matrix and guidance on when multi-platform is warranted, plus migration patterns and example paths between platforms.
How it works
It guides the agent to assess platforms against seven questions in order, read platform profiles for strengths and drawbacks, reference the decision matrix for context, and plan migrations or consolidations. It describes multi-platform coordination requirements (ownership matrix, shared metric definitions, reconcilable dashboards) and outlines concrete migration paths with estimated effort (e.g., "Statsig to PostHog" involves parallel-run and cutover after trust). It also notes when to consider each platform based on organizational context (startup, enterprise, data-team-led, AI-forward). The content is organized into sections: platform profiles, decision matrix, multi-platform guidance, and migration patterns.
When to use it
The skill is intended for choosing a platform from scratch, evaluating whether to switch, deciding whether to consolidate from multi-platform to single, or planning an approved migration.
What it can touch
The guidance references multiple platforms and patterns but does not enumerate executable tooling beyond naming the platforms and the MCP concept. It instructs considering MCP availability, governance, data architecture, and cost shapes when evaluating platforms.
Caveats
No explicit licensing or risk warnings are stated beyond general governance and cost considerations; the text warns about vendor-native vs warehouse-native patterns and the potential for migration effort. It notes that MCP availability varies by platform and that Eppo lacks a first-party MCP as of May 2026. It emphasizes that migration patterns involve non-trivial engineering effort and parallel-run periods.
# Experimentation Platform Orchestrator A senior product and engineering leader's playbook for making the experimentation platform decision and recovering from making it wrong. Picking an experimentation platform is one of those decisions that looks easy at the start and compounds for years afterward. The wrong choice costs you in lost experiments (because the team avoids the painful workflow), in cost (because the wrong pricing model penalizes your usage shape), in vendor lock-in (because migration is real engineering work, not a config change), and in cultural drift (because the platform's defaults shape what your team thinks experimentation is). This skill is the discipline that makes the decision well the first time and the migration plan when you didn't. When to use this skill: choosing a platform from scratch, evaluating whether to switch, deciding whether to consolidate from multi-platform to single, or planning a migration that has already been approved. --- ## What this skill is for This skill spans platform selection, multi-platform decisions, migration planning, and governance setup. It does not cover experiment design (use `experiment-design`), result interpretation (us
- What this skill is for
- The 7 considerations for the platform decision
- Statsig
- PostHog
- GrowthBook
- Optimizely
- Amplitude
- Eppo
- Kameleoon
- Decision matrix: which platform for which context
- Multi-platform: when warranted, when a mess
- Migration patterns
- The cost reality
- Governance and team fit
What does the experimentation-platform-orchestrator skill do?
A 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
How do I install it?
Run `npx skills add rampstackco/claude-skills --skill experimentation-platform-orchestrator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From rampstackco/claude-skills, a repository with 515 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.