workflow-sprint-planning
The workflow-sprint-planning slash command executes a three-step sprint preparation process that transforms backlog items into actionable sprint work. It sequentially generates refinement notes documenting discussions and decisions, creates user stories with acceptance criteria, and identifies edge cases and error states. Teams use this command to move from raw backlog or product requirements documents into fully specified, sprint-ready work items.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/product-on-purpose/pm-skills/HEAD/commands/workflow-sprint-planning.md -o ~/.claude/commands/workflow-sprint-planning.mdworkflow-sprint-planning.md
Run the Sprint Planning workflow to prepare sprint-ready stories from a backlog or PRD. This workflow uses multiple skills in sequence. For each step, read the skill instructions and follow them to create the artifact. ## Workflow Steps ### Step 1: Refinement Notes Use the `iterate-refinement-notes` skill from `skills/iterate-refinement-notes/SKILL.md`. Document the backlog refinement session: stories discussed, estimates, questions raised, and decisions made. ### Step 2: User Stories Use the `deliver-user-stories` skill from `skills/deliver-user-stories/SKILL.md`. Generate user stories with clear acceptance criteria from the refined backlog items. ### Step 3: Edge Cases Use the `deliver-edge-cases` skill from `skills/deliver-edge-cases/SKILL.md`. Document edge cases, error states, and boundary conditions for the stories from Step 2. ## Output Create all three artifacts in sequence, ensuring each builds on the previous. Reference the Sprint Planning workflow at `_workflows/sprint-planning.md` for additional guidance. Context from user: $ARGUMENTS
Run an ad-hoc ordered chain of pm-skills with shared context (ephemeral; routes to the pm-workflow-orchestrator)
Defines a testable hypothesis with clear success metrics and a validation approach. Use when forming assumptions to test or aligning a team on what success looks like, before any experiment is designed. To design the A/B test or experiment that will validate the hypothesis, use measure-experiment-design.
Creates a Jobs to be Done canvas capturing the functional, emotional, and social dimensions of a customer job. Use when deeply understanding customer motivations, designing for jobs, or reframing product positioning.
Creates an opportunity solution tree connecting a desired outcome to customer opportunities and candidate solutions, preventing solution-first jumps in continuous discovery. Use when structuring scattered feature ideas or communicating how the roadmap traces to outcomes. For ranking an existing flat list of candidates, use define-prioritization-framework instead; this skill discovers the list, it does not score one.
Run applicable prioritization frameworks (RICE, ICE, MoSCoW, Weighted Scoring, Kano) against a list of features or initiatives. Produces a comparison table showing where rankings agree and diverge across frameworks, and an executive summary with recommendation. Framework applicability is filtered by data availability; Kano requires customer research. Refuses to fabricate scores; produces an estimation scaffold when input data is missing.
Creates a clear problem framing document with user impact, business context, and success criteria. Use when starting a new initiative, realigning a drifted project, or communicating up to leadership.
Generates structured Given/When/Then acceptance criteria for a user story or feature slice, covering the happy path, key failure scenarios, and non-functional expectations in testable form. Use when turning requirements into verifiable scenarios for engineering handoff and QA sign-off. For a dedicated catalog of boundary conditions, error states, and recovery paths across a feature, use deliver-edge-cases; to write the stories themselves, use deliver-user-stories.
Documents edge cases, error states, boundary conditions, race conditions, and recovery paths for a feature - the systematic catalog of what can go wrong and the failure modes to design for. Use during specification to map the failure surface and ensure comprehensive coverage, or during QA planning to identify boundary and limit scenarios to test. Distinct from deliver-acceptance-criteria, which writes story-level Given/When/Then checks; this skill produces the whole-feature edge-case catalog.