retro
The retro skill facilitates structured sprint retrospectives using formats like Start/Stop/Continue, 4Ls, or Sailboat to surface team insights. Use this skill when running a retrospective meeting, analyzing sprint performance against goals, processing raw team feedback from surveys or sticky notes, or generating prioritized action items with assigned owners and deadlines to drive continuous improvement between sprints.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/retro && cp -r /tmp/retro/pm-execution/skills/retro ~/.claude/skills/retroSKILL.md
## Sprint Retrospective Facilitator Run a structured retrospective that surfaces insights and produces actionable improvements. ### Context You are facilitating a retrospective for **$ARGUMENTS**. If the user provides files (sprint data, velocity charts, team feedback, or previous retro notes), read them first. ### Instructions 1. **Choose a retro format** based on context (or let the user pick): **Format A — Start / Stop / Continue**: - **Start**: What should we begin doing? - **Stop**: What should we stop doing? - **Continue**: What's working well that we should keep? **Format B — 4Ls (Liked / Learned / Lacked / Longed For)**: - **Liked**: What did the team enjoy? - **Learned**: What new knowledge was gained? - **Lacked**: What was missing? - **Longed For**: What do we wish we had? **Format C — Sailboat**: - **Wind (propels us)**: What's driving us forward? - **Anchor (holds us back)**: What's slowing us down? - **Rocks (risks)**: What dangers lie ahead? - **Island (goal)**: Where are we trying to get to? 2. **If the user provides raw feedback** (e.g., sticky notes, survey responses, Slack messages): - Group similar items into themes - Identify the most frequently mentioned topics - Note sentiment patterns (frustration, energy, confusion) 3. **Analyze the sprint performance**: - Sprint goal: achieved or not? - Velocity vs. commitment (over-committed? under-committed?) - Blockers encountered and how they were resolved - Collaboration patterns (what worked, what didn't) 4. **Generate prioritized action items**: | Priority | Action Item | Owner | Deadline | Success Metric | |---|---|---|---|---| | 1 | [Specific, actionable improvement] | [Name/Role] | [Date] | [How we'll know it worked] | - Limit to 2-3 action items (more won't get done) - Each must be specific, assignable, and measurable - Reference previous retro actions if available — were they completed? 5. **Create the retro summary**: ``` ## Sprint [X] Retrospective — [Date] ### Sprint Performance - Goal: [Achieved / Partially / Missed] - Committed: [X pts] | Completed: [Y pts] ### Key Themes 1. [Theme] — [summary] ### Action Items 1. [Action] — [Owner] — [By date] ### Carry-over from Last Retro - [Previous action] — [Status: Done / In Progress / Not Started] ``` Save as markdown. Keep the tone constructive — the goal is improvement, not blame.
The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.
Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.