workflow-post-launch-learning
This workflow orchestrates post-launch measurement and learning by sequencing five interconnected tasks: defining event tracking specifications, designing analytics dashboards, documenting feature results with analysis, conducting a team retrospective, and documenting lessons for organizational retention. Use it after shipping a feature to establish measurement infrastructure, evaluate performance against goals, and systematize insights from the launch experience for future product decisions.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/product-on-purpose/pm-skills/HEAD/commands/workflow-post-launch-learning.md -o ~/.claude/commands/workflow-post-launch-learning.mdworkflow-post-launch-learning.md
Run the Post-Launch Learning workflow to set up measurement, evaluate results, and capture learnings after a feature ships. 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: Instrumentation Spec Use the `measure-instrumentation-spec` skill from `skills/measure-instrumentation-spec/SKILL.md`. Define event tracking and analytics instrumentation requirements for the shipped feature. ### Step 2: Dashboard Requirements Use the `measure-dashboard-requirements` skill from `skills/measure-dashboard-requirements/SKILL.md`. Specify the analytics dashboard including metrics, visualizations, and data sources. ### Step 3: Experiment Results Use the `measure-experiment-results` skill from `skills/measure-experiment-results/SKILL.md`. Document the results of the feature launch with analysis and recommendations. ### Step 4: Retrospective Use the `iterate-retrospective` skill from `skills/iterate-retrospective/SKILL.md`. Facilitate a team retrospective covering the full feature lifecycle. ### Step 5: Lessons Log Use the `iterate-lessons-log` skill from `skills/iterate-lessons-log/SKILL.md`. Distill retrospective findings into durable lessons for organizational memory. ## Output Create all five artifacts in sequence. Steps 1-2 should happen at or before launch; Steps 3-5 after data accumulates. Reference the Post-Launch Learning workflow at `_workflows/post-launch-learning.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.