experimentation-playbook

Orchestrate experiment lifecycles from hypothesis to strategy update.

Updated Apr 19, 2026

Search Tags:#workflow #monitoring #archival #hypothesis #experiment #preregistration #bet

Ready to run

npx skills add https://github.com/marmikcfc/pepper-skills --skill experimentation-playbook

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experimentation-playbook
Source: https://github.com/marmikcfc/pepper-skills/tree/main/skills/experimentation-playbook
Command: npx skills add https://github.com/marmikcfc/pepper-skills --skill experimentation-playbook

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates the full lifecycle of experiments—from bets or raw hypotheses through design, preregistration, execution, monitoring, archival, and strategy updates—using an LLM-driven workflow where valid transitions are declared per vertex.

Core Features & Use Cases

End-to-end lifecycle management for experiments across GTM, product, pricing, content, and onboarding.
Graph-driven workflow with explicit vertex transitions to enforce guardrails and predictable progress.
Supports preregistration, monitoring, archiving, and strategy updates; integrates with bets, experiments, and learnings logs.

Quick Start

Provide a bet reference or raw hypothesis to initialize the experiment lifecycle and let the workflow guide you.

Frequently Asked Questions about experimentation-playbook

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I manage the full experiment lifecycle from hypothesis to archival?▼

Experiment lifecycle management is handled by an LLM-driven graph that orchestrates bets from intake through design, preregistration, execution, monitoring, archival, and strategy updates. You provide a bet reference or raw hypothesis to initialize the workflow.

What is preregistration in GTM and product experiments, and how does it work?▼

Preregistration in GTM and product experiments is a predefined workflow vertex that logs your hypothesis and design before execution begins. It enforces guardrails through explicit vertex transitions, ensuring rigorous tracking and predictable progress before outcomes are written to logs.

Can I track pricing and onboarding bets using a graph-driven workflow?▼

Yes, you can track pricing and onboarding bets using a graph-driven workflow. The orchestration supports experiments across GTM, product, pricing, content, and onboarding, applying explicit vertex transitions to enforce predictable progress from intake to archival.

How do I set up experiment monitoring and logging for strategy updates?▼

Experiment monitoring and logging are set up by defining roles like experiment-runner, monitor-loop, and strategy-updater within the workflow. The monitor-loop vertex tracks execution, and the strategy-updater writes validated outcomes to bets, experiments, and learnings logs.

What is the best way to enforce guardrails during experiment design and execution?▼

The best way to enforce guardrails during experiment design and execution is using a graph-driven workflow with declared valid transitions per vertex. This ensures rigorous lifecycle tracking by requiring explicit vertex transitions between roles like experiment-designer, preregistration, and experiment-runner.

Do I need to define specific roles like bet-logger and archiver to run experiments?▼

Yes, you need to define specific roles like bet-logger, experiment-designer, and archiver to run experiments. The workflow supports these defined roles to orchestrate end-to-end lifecycle tracking, requiring explicit vertex transitions to progress from intake to strategy updates.