strategy-memo
Write a strategy memo that commits to a bet and says what you won't do. Use when asked to write a strategy memo, articulate a strategy, make the case for a strategic direction, or align the team on where to focus. Produces a strategy memo — the strategic question, the diagnosis, the bet/approach, why now, explicit non-goals (what we're NOT doing), how we'll know it's working, and the risks.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/strategy-memo && cp -r /tmp/strategy-memo/plugins/pm-business/skills/strategy-memo ~/.claude/skills/strategy-memoSKILL.md
# Strategy Memo Skill Strategy is choosing what *not* to do. A real strategy memo makes a bet and names the sacrifices; a fake one is a list of goals everyone already agreed with. This skill follows the diagnosis → guiding policy → coherent actions structure, forces explicit non-goals, and ties the bet to leading indicators — so the team is aligned on a direction sharp enough to be wrong. ## Required Inputs Ask for these only if they aren't already provided: - **The strategic question** — the choice or challenge this memo resolves. - **The situation** — the honest diagnosis: the market, the competition, your real position and constraints. - **The bet** — the approach you're choosing and what it's betting on being true. - **The trade-offs** — what you'll deliberately not do or de-prioritise to make the bet. ## Output Format ### Strategy Memo: [the strategic question] **1. The question** — the strategic choice being made, in one sentence. **2. Diagnosis** — the honest read of the situation: what's really going on, the few factors that matter most, and your true position (not the aspirational one). A strategy built on a flattering diagnosis fails. **3. The bet (guiding policy)** — the chosen approach and, explicitly, **what it assumes is true**. This is the spine — everything else serves it. **4. Why now** — why this is the right bet at this moment (the window, the catalyst), not last year or next. **5. What we're NOT doing** — the explicit non-goals and de-prioritisations. If this list is empty, it isn't a strategy — it's a wish list. **6. Coherent actions** — the few moves that follow from the bet and reinforce each other (not a laundry list of everything). **7. How we'll know** — the leading indicators that tell you the bet is working (or not) early, and the conditions under which you'd reconsider. **8. Risks** — what could make the bet wrong, and which assumptions to validate first. ## Quality Checks - [ ] The diagnosis is honest about the real position, not aspirational - [ ] The bet states what it assumes to be true — it's falsifiable - [ ] There's an explicit "what we're NOT doing" list with real sacrifices - [ ] The actions are few and coherent (mutually reinforcing), not an everything-list - [ ] Leading indicators are named so you learn early whether it's working - [ ] "Why now" is answered — the timing is justified ## Anti-Patterns - [ ] Do not write goals and call it strategy — "grow revenue, delight customers" is a wish list, not a bet - [ ] Do not skip the non-goals — a strategy that sacrifices nothing commits to nothing - [ ] Do not build on a flattering diagnosis — naming the uncomfortable truth is the hardest and most important part - [ ] Do not list every initiative — coherent actions reinforce one bet; a long list dilutes it - [ ] Do not leave the bet unfalsifiable — if no evidence could prove it wrong, it can't be pressure-tested ## Based On Good Strategy / Bad Strategy (Richard Rumelt) — diagnosis, guiding policy, coherent action; and the discipline of explicit non-goals.
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.