experimentation-platform-orchestrator

Solid

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 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.

AI & Automation 280 stars 37 forks Updated 2 days ago MIT

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Skill Content

# 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 (use `experimentation-analytics`), or feature flag operations (use `feature-flagging`). Pair this skill with the relevant integrations microsite when you need platform-specific MCP details. The audience is a PM, engineering leader, or data lead who is making the decision or recovering from a pr...

Details

Author
rampstackco
Repository
rampstackco/claude-skills
Created
1 months ago
Last Updated
2 days ago
Language
Python
License
MIT

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