launch-a-product
The launch-a-product workflow chains five product management skills in sequence to move from market analysis through to launch announcement. Use this slash command when preparing to bring a new product to market and needing a structured end-to-end process that maps competitors, defines positioning, plans go-to-market strategy, builds a launch checklist, and produces a press release, with each stage informing the next.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/HEAD/commands/launch-a-product.md -o ~/.claude/commands/launch-a-product.mdlaunch-a-product.md
Run the **Launch 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. Open with a one-line plan of the 5 stages, then ask once for any essential missing inputs (the product, target customer, key competitors, launch date). Don't re-ask between stages. Run each stage under a clear `## Stage N — <name>` heading: 1. **Map the field** — apply the `competitor-teardown` skill to map competitors and find the positioning gap to exploit. 2. **Position it** — apply the `product-positioning-doc` skill, using that gap, to define positioning, value props, and messaging pillars. 3. **Plan the GTM** — apply the `go-to-market` skill to turn the messaging into an audience-and-channel launch plan. 4. **Make it launch-ready** — apply the `product-launch-checklist` skill to produce an owner-by-owner readiness checklist. 5. **Announce it** — apply the `press-release` skill to draft the announcement, consistent with the positioning above. Do not invent facts, dates, or competitor claims — flag assumptions. End with a 5-bullet **"What you now have"** recap.
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
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.
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
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.