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hypothesis-library skill

/plugins/growth-experiments/skills/hypothesis-library

This skill helps teams capture and govern experiment hypotheses with metadata, learnings, and governance, enabling consistent prioritization and cross-pod

npx playbooks add skill gtmagents/gtm-agents --skill hypothesis-library

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: hypothesis-library
description: Curated repository of experiment hypotheses, assumptions, and historical
  learnings.
---

# Hypothesis Library Skill

## When to Use
- Capturing new experiment ideas with consistent metadata.
- Referencing past wins/losses before prioritizing the backlog.
- Sharing reusable learnings across pods and channels.

## Framework
1. **Metadata Schema** – hypothesis ID, theme, persona, funnel stage, metrics.
2. **Assumptions Matrix** – belief statements, supporting evidence, confidence rating.
3. **Status Tracking** – idea → scoped → running → decided → archived.
4. **Learning Tags** – impact summary, guardrail notes, follow-up ideas.
5. **Governance Hooks** – approvals, owners, review cadence.

## Templates
- Intake form for new hypotheses.
- Learning card format (context, result, recommendation).
- Portfolio dashboard summarizing mix by theme/metric.

## Tips
- Require at least one supporting data point before moving to prioritization.
- Use consistent tagging so search/filtering works across teams.
- Link to `synthesize-learnings` outputs to keep narratives fresh.

---

Overview

This skill is a curated hypothesis library for capturing, tracking, and reusing experiment hypotheses, assumptions, and outcomes. It standardizes metadata, assumptions, and status so teams can prioritize experiments with confidence and share learning across sales, marketing, customer success, and revenue ops. The library fits into GTM workflows to reduce duplicate work and accelerate evidence-driven decisions.

How this skill works

The skill enforces a structured metadata schema (ID, theme, persona, funnel stage, target metrics) and an assumptions matrix that records belief statements, supporting evidence, and a confidence rating. It tracks lifecycle status (idea → scoped → running → decided → archived), stores learning cards (context, result, recommendation), and exposes governance hooks for owners and review cadence. Tags and portfolio dashboards enable filtering and executive summaries for prioritization.

When to use it

  • Capturing new experiment ideas with consistent metadata before prioritization
  • Reviewing past wins and losses to inform roadmap decisions
  • Sharing reusable learnings across pods and channels to avoid repeated mistakes
  • Auditing experiment coverage by theme, persona, or funnel stage
  • Preparing stakeholder reviews or governance checkpoints for experiment pipelines

Best practices

  • Require at least one supporting data point before moving a hypothesis to prioritization
  • Use consistent tagging and naming conventions to make search and dashboards reliable
  • Maintain explicit owners and review cadence for governance and accountability
  • Record guardrail notes and follow-up ideas on every learning card
  • Keep portfolio dashboards updated to reflect running vs. decided experiments

Example use cases

  • Intake form to capture a conversion-rate hypothesis tied to a metric and persona
  • Assumptions matrix to document why a campaign should lift engagement and how to validate it
  • Learning card summarizing a failed pricing test with impact, root cause, and next steps
  • Portfolio dashboard showing experiment mix by theme and expected impact for quarterly planning
  • Governance checklist to approve experiments that exceed risk or cost thresholds

FAQ

How much evidence is required to advance a hypothesis?

Require at least one supporting data point or qualitative insight before prioritizing; rigorous validation comes during experiment design and execution.

How do tags and dashboards stay useful across teams?

Enforce a small set of standard tags (theme, persona, funnel stage, metric) and a naming convention so filters and dashboards remain consistent and actionable.