retro-analysis
Retro-analysis generates a data-grounded retrospective brief from sprint metrics including completion rates, carry-over analysis, unplanned work, and velocity trends. Use this skill when a team needs to prepare for a retrospective meeting, transform raw sprint data into discussion prompts, or identify concrete process experiments. The output separates factual patterns from subjective interpretation, providing Start/Stop/Continue prompts tied to specific sprint behaviors and one measurable experiment for the next sprint.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/retro-analysis && cp -r /tmp/retro-analysis/plugins/pm-delivery/skills/retro-analysis ~/.claude/skills/retro-analysisSKILL.md
# Retrospective Analysis Skill Generate a data-grounded retrospective brief that separates facts from feelings, so the team spends retro time on solutions rather than debating what happened. ## Required Inputs Ask the user for these if not provided: - **Sprint tickets: planned vs. completed** - **Carry-over tickets and reasons** (if known) - **Tickets reopened after closing** (quality signal) - **Any incidents or unplanned work** (scope creep signal) - **Sprint velocity vs. historical average** (trend context) ## Process 1. Calculate: completion rate, carry-over rate, unplanned work percentage 2. Identify patterns: which ticket types were most likely to carry over? Which caused blockers? 3. Note any process or communication breakdowns visible in the data 4. Prepare 3 "Start / Stop / Continue" prompts based on the data — not generic, specific to this sprint 5. Suggest 1 concrete experiment for the next sprint based on the biggest friction point 6. **Validate** — Confirm each prompt is specific to this sprint (not a recycled generic prompt), and that the recommended experiment is concrete and measurable ## Output Structure ### Sprint [Number] Retrospective Brief **By the Numbers:** - Planned: [n] tickets | Completed: [n] | Carry-over: [n] | Completion rate: [%] - Unplanned work: [n] tickets ([%] of capacity) - Velocity: [points] vs. [average] average **What the Data Suggests:** [2-3 observations grounded in the numbers above] **Discussion Prompts:** - Start: [specific prompt based on this sprint's data] - Stop: [specific prompt based on this sprint's data] - Continue: [specific prompt based on this sprint's data] **Suggested Experiment for Next Sprint:** [One concrete, testable process change — with a specific success metric] ## Quality Checks - [ ] Each Start/Stop/Continue prompt names a specific behaviour, not a vague category - [ ] The recommended experiment is testable in one sprint - [ ] Carry-over analysis identifies the ticket type or cause, not just the count - [ ] Data observations don't assign blame — they describe patterns - [ ] Velocity trend is mentioned in context (is this a one-off or a pattern?) ## Anti-Patterns - [ ] Do not assign blame to individuals in the retrospective brief — observations must describe patterns, not people - [ ] Do not produce Start/Stop/Continue prompts that are vague categories — each must name a specific behaviour - [ ] Do not recommend an experiment that cannot be completed within one sprint — small, testable experiments only - [ ] Do not treat carry-over tickets as a velocity problem without first identifying the root cause category - [ ] Do not run the same retrospective format every sprint — vary the format to prevent engagement fatigue
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.