sprint-plan
The sprint-plan skill guides teams through capacity estimation, story selection, dependency mapping, and risk identification to create a balanced sprint plan. Use it when preparing for sprint planning meetings, estimating how much work a team can realistically complete, selecting stories from a prioritized backlog, sequencing dependent work, or identifying potential blockers and risks that could impact sprint success.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/sprint-plan && cp -r /tmp/sprint-plan/pm-execution/skills/sprint-plan ~/.claude/skills/sprint-planSKILL.md
## Sprint Planning Plan a sprint by estimating team capacity, selecting and sequencing stories, and identifying risks. ### Context You are helping plan a sprint for **$ARGUMENTS**. If the user provides files (backlogs, velocity data, team rosters, or previous sprint reports), read them first. ### Instructions 1. **Estimate team capacity**: - Number of team members and their availability (PTO, meetings, on-call) - Historical velocity (average story points per sprint from last 3 sprints) - Capacity buffer: reserve 15-20% for unexpected work, bugs, and tech debt - Calculate available capacity in story points or ideal hours 2. **Review and select stories**: - Pull from the prioritized backlog (highest priority first) - Verify each story meets the Definition of Ready (clear AC, estimated, no blockers) - Flag stories that need refinement before committing - Stop adding stories when capacity is reached 3. **Map dependencies**: - Identify stories that depend on other stories or external teams - Sequence dependent stories appropriately - Flag external dependencies and owners - Identify the critical path 4. **Identify risks and mitigations**: - Stories with high uncertainty or complexity - External dependencies that could slip - Knowledge concentration (only one person can do it) - Suggest mitigations for each risk 5. **Create the sprint plan summary**: ``` Sprint Goal: [One sentence describing what success looks like] Duration: [2 weeks / 1 week / etc.] Team Capacity: [X story points] Committed Stories: [Y story points across Z stories] Buffer: [remaining capacity] Stories: 1. [Story title] — [points] — [owner] — [dependencies] ... Risks: - [Risk] → [Mitigation] ``` 6. **Define the sprint goal**: A single, clear sentence that captures the sprint's primary value delivery. Think step by step. Save as markdown. --- ### Further Reading - [Product Owner vs Product Manager: What's the difference?](https://www.productcompass.pm/p/product-manager-vs-product-owner)
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.