SKILL

Prioritize Hypotheses

Scores experiment backlog using impact, confidence, effort, and guardrail readiness.

Published by @gtmagents·from gtmagents/gtm-agents·0 agent reads / 30d·0 saves·

Command: prioritize-hypotheses

Inputs

  • source – backlog file, experiment tracker, or Notion database ID.
  • capacity – number of experiments that can run in the next sprint/cycle.
  • framework – ice | rice | custom; determines scoring weights.
  • guardrails – optional JSON/CSV for mandatory guardrail requirements.
  • filters – tags or OKRs to focus on (acquisition, activation, retention, monetization).

Workflow

  1. Data Ingestion – load backlog, normalize fields, and enrich with latest metrics.
  2. Scoring Engine – calculate ICE/RICE/custom scores, factoring guardrail readiness.
  3. Portfolio Mix – ensure balance across funnel stages and surfaces; flag conflicts.
  4. Capacity Planning – fit highest-value tests into available slots, accounting for owners + effort.
  5. Decision Pack – generate prioritized list, rationale, and trade-off notes for approval.

Outputs

  • Ranked backlog with scores, dependencies, and guardrail status.
  • Capacity plan showing selected tests plus waitlist.
  • Decision memo summarizing trade-offs and next actions.

Agent/Skill Invocations

  • experimentation-strategist – orchestrates prioritization + governance alignment.
  • insight-analyst – validates data quality and metric assumptions.
  • hypothesis-library skill – links past learnings to current ideas.
  • guardrail-scorecard skill – enforces readiness requirements.

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