setup-pm-skills
Onboard a new user — find out what they do, recommend the right bundles & top skills, and set up a project CONTEXT.md so every skill is tailored to them.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/HEAD/commands/setup-pm-skills.md -o ~/.claude/commands/setup-pm-skills.mdsetup-pm-skills.md
You are onboarding someone to the **PM Skills** library (198 professional Agent Skills across 27 bundles). Goal: get them from "installed" to "got real value" in under two minutes. Context they gave: $ARGUMENTS Do this in order, conversationally — don't dump everything at once: 1. **Learn the user (one question).** If `$ARGUMENTS` already says their role/task, skip the question. Otherwise ask the single question: *"What do you do, and what's one thing you're working on right now?"* Wait for the answer. 2. **Recommend a starting set.** From their answer, name: - The **1–2 bundles** that fit (e.g. `pm-essentials`, `pm-founders`, `pm-engineering`, `pm-education`, `pm-gtm`…). - The **3 highest-value skills** to try first, each with a one-line "use it when…". Prefer 🟢 production-tier skills. - The **one workflow recipe** (slash command) most relevant to them, if any (e.g. `/launch-a-product`, `/close-the-quarter`, `/prd`). 3. **Set up their CONTEXT.md.** Offer to create a `CONTEXT.md` in the project root capturing their company, product, audience, voice/tone, key metrics, and constraints — explain that skills read it so outputs come back tailored without re-typing. If they say yes, ask the 4–5 essentials, then write a clean `CONTEXT.md` (see `CONTEXT.example.md` for the shape). 4. **Show, don't tell.** Offer to run their most relevant skill *right now* on a real task of theirs, so they see the output quality immediately. 5. **Point onward (one line each).** The browser [Playground](https://mohitagw15856.github.io/pm-claude-skills/) to run any skill free · `npx skills add mohitagw15856/pm-claude-skills` for other agents · the [browser extension](https://github.com/mohitagw15856/pm-claude-skills/tree/main/extension) for ChatGPT/Claude.ai/Gemini · `writing-great-skills` if they want to contribute one. Keep it warm and brief. The win condition is they run one skill on something real before the conversation ends.
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.