171건 · 관련도순
Run a structured sprint retrospective, a review of a short work cycle, using team feedback and delivery results. Identify shared themes and produce two or three prioritized improvements with owners, deadlines, and measures of success.
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Use when a user requests an engineering retrospective for a period. Not for agent-environment retrospectives: use agent-environment-retrospective. For learning milestones: use learning-retrospective.
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Create engineering retrospectives from Git history, covering delivered changes, work patterns, test code ratios, and frequently changed files. Review individual contributions and improvement opportunities, save metrics, and compare results with earlier periods.
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Review a completed epic, a group of related development tasks, against its plans, code changes, and commits. Produce a retrospective with evidence-backed findings, follow-up actions, and a decision on whether to accept the work.
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Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.
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Review a sprint or milestone by comparing planned work with actual results. Summarize completion, estimation gaps, and blockers, then produce a retrospective with specific improvement actions, owners, and deadlines for the next iteration.
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Generates complete NHANES-style cross-sectional epidemiology + retrospective clinical validation research designs from a user-provided disease and biomarker direction. Always use this skill whenever a user wants to design, plan, or build a population-level biomarker association study using NHANES or similar survey datasets, especially when the article logic includes disease definition, biomarker formula derivation, multivariable logistic regression, restricted cubic spline analysis, subgroup stability testing, and a secondary hospital-based retrospective validation cohort. Covers five study patterns (cross-sectional association, dose-response / RCS, subgroup-stability, NHANES + retrospective validation, preliminary screening-performance) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
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Data-driven sprint retrospectives from git history — velocity, cycle/lead time, contributor insights, and churn hotspots. Use to run a retrospective, analyze team velocity or throughput, or generate a retro report from commit history.
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Use when asked to review learning progress after a milestone. Not for engineering retrospectives, use engineering-retrospective. For agent-environment ones, use agent-environment-retrospective.
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Use when a completed session needs an agent-environment retrospective. Not for an engineering retrospective from telemetry: use engineering-retrospective.
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Create a technical journey retrospective document from the current session's work. Use when the user asks for a retrospective, technical journey, dev blog post, session writeup, or wants to document what was built and why. Triggers on phrases like "write a retrospective", "create a journey doc", "document this session", "write a blog post about what we did".
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Review a week of engineering work using commit history, work patterns, and code quality measures. It tracks trends across retrospectives and breaks down each team member’s contributions, highlighting strengths and areas for growth.
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Run a structured after-action review (postmortem, retrospective) on a launch, incident, or completed project to capture timeline, root cause analysis, contributing factors, and actionable lessons. Use this skill whenever the user wants to run a postmortem, retrospective, AAR, or after-action review on any past event. Triggers on after-action report, AAR, postmortem, retrospective, retro, post-incident review, what went well what didn't, lessons learned, blameless postmortem, root cause analysis, RCA, five whys. Also triggers when the user has just shipped something or just resolved an incident and wants to capture learnings.
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Data-driven Scrum Master for sprint health scoring, Monte Carlo velocity forecasting, retrospective analysis, capacity planning, and Tuckman team coaching. Use when facilitating sprint planning, diagnosing velocity, or running retrospectives.
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Use when a campaign has ENDED and the team needs a structured retrospective — a scorecard against target, what worked, what did not, and why with evidence, surprises, process review, lessons written back into the playbook and Brand Hub, and action items with owners. Trigger on 'campaign retrospective', 'campaign retro', 'post-mortem', 'campaign debrief', 'what worked and what did not', 'the campaign is over, what did we learn'. Also use when a campaign underdelivered and the team is arguing about why. Not for — a periodic performance report, see `07-marketing-report-global`; diagnosing live numbers mid-flight, see `03-performance-eval-global`; the next period ads plan, see `57-next-ads-plan-global`.
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Review a finished epic folder in the ticket tree against the evidence it left behind — the epic file, each ticket plan, diffs, commits — and produce a retrospective with sourced findings, action items, and an acceptance decision. Use when the user says "run a retrospective" or "lets retro the epic [epic]". Supports -H/--headless
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Conduct a project or sprint retrospective by gathering data from status reports and velocity metrics, structuring what went well and what needs improvement, and generating actionable improvement items with owners and due dates. Use at the end of a sprint, after a project phase or milestone, following a significant incident or success, at a quarterly review of ongoing processes, or before starting a similar project to capture lessons learned.
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Weekly-cadence engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective". Proactively suggest at the end of a work week or sprint. NOT for per-session or per-task reflection (see mk:memory / Phase 6 Reflect).
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Cycle-end retrospective converting recurring judgment failures into durable enforcement. Reviews the cycle's friction — repeated user corrections, guard trips, RCA themes, integrate failures — and turns each recurring one into a landmines.md entry, then proposes graduation candidates up the enforcement funnel (landmine → advisory hook → hard gate). Pairs with standup: standup says what shipped and what's next; retrospective says what kept going wrong and how to stop it structurally. Writes only memory entries and a report — graduation proposals are advisory and require the normal seed.md amendment path to become enforcement. Invoke at the end of a release cycle, an epic, or any stretch of work with repeated friction.
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作業をふりかえって改善案を引き出し、記録する。セッション内のすべてのストーリーが完了したとき、依頼者の終わり発話 (作業を区切る発話) を観測したとき、または依頼者が「ふりかえろう」「retrospective」と明示したときに発火。同セッション内で 1 度 skip された場合は再発火しない。
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Use when working with Blameless — blameless incident management, SLO tracking, retrospectives, and reliability insights. Covers incident lifecycle, blameless retrospective facilitation, follow-up tracking, reliability scorecards, and incident type categorization. Use when managing active incidents, conducting retrospectives, tracking SLO compliance, or analyzing reliability trends in Blameless.
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Turn product requirements, roadmaps, architecture reviews, and retrospectives into HTML slides for decision-making. Choose one of eight visual themes to create a consistent, horizontally navigable deck, using source evidence and real numbers to support review.
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Rank problems and proposed fixes using four scored criteria: expected benefit, implementation cost, deployment ease, and risk. The skill produces a sorted priority table with scoring rationales and explains why the top three items should come first.
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Review an executed business decision against its original success and stopping criteria. Compare actual results, revisit assumptions and dissenting views, and create a retrospective record with lessons and follow-up actions.
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