grow-a-product
Workflow recipe — turn a growth goal into a funnel diagnosis, experiment backlog, retention loop, and lifecycle journeys by chaining 4 skills.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/HEAD/commands/grow-a-product.md -o ~/.claude/commands/grow-a-product.mdgrow-a-product.md
Run the **Grow a Product** workflow recipe for: $ARGUMENTS This is a *chain* of skills. Run each stage in order and **carry every stage's output forward as context** for the next — that shared context is the whole point. Open with a one-line plan of the 4 stages, then ask once for any essential missing inputs (the product & motion, current funnel numbers if any, the goal). Don't re-ask between stages. Run each stage under a clear `## Stage N — <name>` heading: 1. **Diagnose the funnel** — apply the `marketing-funnel-plan` skill to map the full funnel, identify the single biggest leak, and set a 90-day focus stage and metric. 2. **Build the experiment backlog** — apply the `growth-experiment-backlog` skill to turn that focus into prioritised, properly-powered hypotheses (ICE) with test designs. 3. **Design the retention loop** — apply the `retention-loop-design` skill to design the engagement loop (trigger → action → reward → investment) and the activation→habit path that keeps the gains. 4. **Nurture with lifecycle journeys** — apply the `lifecycle-crm-plan` skill to design behaviour-triggered journeys that drive the loop, with segmentation, holdouts, and suppression. Do not invent metrics or numbers — note assumptions instead. After the last stage, end with a 4-bullet **"What you now have"** recap linking each artifact to the stage that produced it.
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Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
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Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.