# Growth Experimentation > >- - **Type:** Skill - **Install:** `agentstack add skill-leadmagic-gtm-skills-growth-experimentation` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [LeadMagic](https://agentstack.voostack.com/s/leadmagic) - **Installs:** 0 - **Category:** [Communication](https://agentstack.voostack.com/c/communication) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [LeadMagic](https://github.com/LeadMagic) - **Source:** https://github.com/LeadMagic/gtm-skills/tree/main/skills/analytics/growth-experimentation - **Website:** https://github.com/LeadMagic/gtm-skills ## Install ```sh agentstack add skill-leadmagic-gtm-skills-growth-experimentation ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Growth Experimentation ## Overview The companies with the highest growth rates don't have better ideas — they have better systems for testing ideas. A high-velocity experimentation system runs 15-30 experiments per month across acquisition, activation, retention, and monetization. Most experiments fail. That's by design. The team that learns fastest from each failure wins. ## When to Use - "Build an experimentation program" - "Set up growth sprints" - "Prioritize experiments with ICE" - "Increase our test velocity" - "Create a learning repository" ## Authoritative Foundations - **Sean Ellis & Morgan Brown (Hacking Growth)** — coined "growth hacking." North Star Metric. Growth experimentation loop: analyze → ideate → prioritize → test → learn. - **Brian Balfour (Reforge, ex-HubSpot VP Growth)** — increasing HubSpot's experiment velocity from 5 to 20/week produced 3x growth rate improvement. Four Fits Framework: Market-Product, Product-Channel, Channel-Model, Model-Market. - **Andrew Chen (a16z, ex-Uber Growth)** — The Cold Start Problem. Growth teams at scale. - **Fareed Mosavat (Reforge, ex-Slack Growth)** — experimentation systems. ## Step-by-Step Process ### Phase 1: Set the North Star Metric One metric that captures core value delivery. If this moves up, the business is healthier. All experiments ladder to this metric. ### Phase 2: ICE Scoring Score every experiment idea 1-10 on Impact, Confidence, Ease. Average the three. Prioritize by ICE score. Re-score weekly as new data arrives. ### Phase 3: Growth Sprint Cadence Weekly cycle: idea generation (Monday), prioritization (Tuesday), build (Wed-Thu), launch (Fri), analyze (Mon). 2-week sprints for complex tests. AI compresses cycle: a single growth marketer with AI can test 10 variants in time it used to take to build one. ### Phase 4: Experiment Design Every experiment: hypothesis, success metric, minimum detectable effect, required sample size, maximum duration. Document everything — winners and losers. Build a searchable learning repository. ### Phase 5: 4 Layers of Experiments 1. Channel/tactic assessment — test how channels impact conversions 2. Offer optimization — pricing, packaging, trial length 3. Message personalization — copy and creative by segment 4. AI-powered — autonomous experiment generation, prediction, optimization ## Output Format Experimentation system with North Star Metric definition, ICE backlog, sprint calendar, experiment design template, and learning repository structure. ## Quality Check Before delivering, verify: - [ ] All required sections are complete - [ ] Output matches the user's stated need - [ ] Named frameworks are cited for key recommendations - [ ] No vague claims — every recommendation has a specific action - [ ] Deliverable is ready for operational use, not just conceptual ## Common Pitfalls 1. **Tests too large** — redesigning entire onboarding (4 weeks to build) loses to testing a single screen change (2 days). Small tests = fast learning. 2. **No learning repository** — running 50 experiments without documenting learnings is running the same test twice. Document everything. 3. **Statistical ignorance** — calling a test at 70% confidence produces false positives. Wait for 95%+ confidence. 4. **Winner's bias** — only shipping winners without understanding losers means you don't know why things work. ## Execution Artifacts - `references/framework-notes.md` — named frameworks, citation anchors, and operating assumptions - `templates/output-template.md` — copy-paste deliverable structure for the user - `scripts/check-output.py` — local checklist validator for required sections This skill includes lightweight artifacts the agent can load on demand: Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice. ## Implementation Depth Use this section when the user asks for a finished asset, not a high-level explanation. ### Diagnostic Questions 1. What is the primary motion: founder-led, sales-led, product-led, partner-led, or lifecycle-led? 2. Which ICP tier is the output for: small business, mid-market, enterprise, or mixed? 3. What proof is available today: customer stories, usage data, third-party validation, screenshots, or none? 4. What system will execute the work: CRM, sequencer, warehouse, support desk, product analytics, or manual workflow? 5. What decision will the user make from this output: launch, prioritize, route, rewrite, score, coach, or measure? ### Framework Application Map the recommendation explicitly to the named frameworks in this skill: - Sean Ellis Hacking Growth: apply only the part that directly improves the requested deliverable. - Brian Balfour Reforge: apply only the part that directly improves the requested deliverable. - Andrew Chen Growth: apply only the part that directly improves the requested deliverable. - ICE Scoring: apply only the part that directly improves the requested deliverable. ### Deliverable Standard A strong output from this skill includes: - A crisp diagnosis of the current situation - A recommended path with tradeoffs, not a generic list - A concrete artifact the user can use immediately: table, script, checklist, scorecard, sequence, dashboard spec, or implementation plan - A measurement plan with leading and lagging indicators - Risks and edge cases called out before execution ### Adaptation Rules - For small business: reduce complexity, shorten time-to-value, and prioritize owner/operator clarity. - For mid-market: include workflow ownership, handoffs, integrations, and enablement assets. - For enterprise: include governance, risk, procurement, stakeholder mapping, and proof requirements. ## Related Skills - **a-b-testing**: Statistical framework for individual tests - **gtm-metrics**: Growth metrics and dashboard design ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [LeadMagic](https://github.com/LeadMagic) - **Source:** [LeadMagic/gtm-skills](https://github.com/LeadMagic/gtm-skills) - **License:** MIT - **Homepage:** https://github.com/LeadMagic/gtm-skills Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-leadmagic-gtm-skills-growth-experimentation - Seller: https://agentstack.voostack.com/s/leadmagic - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.