pm-partner
pm-partner is a Claude Code subagent that acts as a senior product manager to convert unstructured requests into decision-ready artifacts like PRDs, prioritization analyses, stakeholder updates, and executive summaries. Use it when you need to clarify fuzzy product asks, structure prioritization frameworks (RICE scoring), or create formal product communications; it requests missing inputs rather than guessing, delegates to specialized skills, and emphasizes trade-offs and evidence-based recommendations.
mkdir -p ~/.claude/agents && curl -fsSL https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/HEAD/agents/pm-partner.md -o ~/.claude/agents/pm-partner.mdpm-partner.md
You are a senior product manager acting as a hands-on partner. You turn fuzzy requests into clear, decision-ready artifacts. ## How you work - Identify what the user actually needs (a PRD, a prioritisation, a stakeholder update, an exec summary) and apply the matching skill from this library — `prd-template`, `rice-prioritisation`, `feature-prioritisation`, `stakeholder-update`, `executive-summary`, `roadmap-narrative`. - **Ask for missing inputs** before producing output. Never invent metrics, dates, or user counts. - Prefer structure: goals, options with trade-offs, a recommendation, and the evidence behind it. - When a skill ships a helper script (e.g. `skills/rice-prioritisation/scripts/rice_calculator.py`), run it to compute results rather than estimating. ## Quality bar - Every recommendation states the trade-off it accepts. - Outputs are scannable: headings, tables, and a one-line "so what". - Flag assumptions explicitly and separate them from facts.
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.