sprint-brief
The sprint-brief skill generates a structured, team-readable sprint overview document from sprint data, goals, and ticket lists. Use this skill when a product manager or team lead needs to create a sprint brief, document sprint scope and goals, or produce a clear summary that engineers, designers, and stakeholders can scan in under three minutes. The output includes sprint goal, rationale, work grouped by theme, critical path items, flagged risks with mitigations, carry-over items, and definition of done criteria.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/sprint-brief && cp -r /tmp/sprint-brief/plugins/pm-delivery/skills/sprint-brief ~/.claude/skills/sprint-briefSKILL.md
# Sprint Brief Skill Produce a clear, scannable sprint brief that every team member — engineer, designer, PM — can read in under three minutes and understand exactly what we're doing and why. ## Required Inputs Ask the user for these if not provided: - **Sprint name and number** - **Sprint goal** (1-2 sentences — flag if too vague) - **Ticket list with owners** (or a description of the work) - **Known dependencies or blockers** - **Carry-over items from previous sprint** (if any) ## Process 1. Read sprint goal and check it's specific and measurable — flag if it's too vague 2. Group tickets by theme or feature area 3. Identify the critical path — which tickets must complete for the sprint goal to be met? 4. Flag risks: tickets with unclear acceptance criteria, missing designs, unresolved dependencies 5. Note carry-over items and whether they affect this sprint's goal 6. **Validate** — Confirm the sprint goal is achievable given the ticket scope and capacity. If the critical path items alone would fill the sprint, flag it as overloaded. ## Output Structure ### Sprint [Number] Brief — [Dates] **Sprint Goal:** [1-2 sentences — specific and measurable] **Why This Sprint Matters:** [Connect to quarterly OKR in 2-3 sentences] **What We're Building:** - [Theme 1]: [tickets and owners] - [Theme 2]: [tickets and owners] **Critical Path:** [The 2-3 tickets everything else depends on] **Risks to Flag:** - [Risk 1 + mitigation] - [Risk 2 + mitigation] **Carry-over from Last Sprint:** [List + impact on current goal] **Definition of Done:** [Specific, agreed criteria for sprint success] ## Quality Checks - [ ] Sprint goal is specific enough to score pass/fail at the end of the sprint - [ ] Critical path items are named — not just "the important ones" - [ ] Every risk has a mitigation or owner (not just "this is a risk") - [ ] Carry-over items are connected to their impact on this sprint's goal - [ ] Definition of Done is agreed criteria, not a task list ## Anti-Patterns - [ ] Do not write a sprint goal as a task list — the goal must be a single outcome-focused statement that can be scored pass/fail - [ ] Do not leave the critical path unnamed — "the important tickets" is not a critical path - [ ] Do not list risks without a mitigation or owner — a risk without a response is just a worry list - [ ] Do not ignore carry-over items' impact on this sprint's capacity and goal - [ ] Do not write a Definition of Done that mixes task completion with outcome criteria — they must be observable and agreed before the sprint starts
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.