workflow-technical-discovery
The workflow-technical-discovery command systematically evaluates technical feasibility and documents architecture decisions through three sequential steps: creating a spike summary that captures time-boxed exploration results, developing an architecture decision record that details context and options considered, and producing design rationale that explains reasoning and trade-offs. Use this workflow when investigating complex technical challenges that require documented decision-making for future reference and team alignment.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/product-on-purpose/pm-skills/HEAD/commands/workflow-technical-discovery.md -o ~/.claude/commands/workflow-technical-discovery.mdworkflow-technical-discovery.md
Run the Technical Discovery workflow to evaluate technical feasibility and document architecture decisions. 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: Spike Summary Use the `develop-spike-summary` skill from `skills/develop-spike-summary/SKILL.md`. Document the results of a time-boxed technical exploration. ### Step 2: Architecture Decision Record Use the `develop-adr` skill from `skills/develop-adr/SKILL.md`. Record the architecture decision with context, options considered, and consequences. ### Step 3: Design Rationale Use the `develop-design-rationale` skill from `skills/develop-design-rationale/SKILL.md`. Document the reasoning behind design decisions, alternatives considered, and trade-offs. ## Output Create all three artifacts in sequence, ensuring each builds on the previous. Reference the Technical Discovery workflow at `_workflows/technical-discovery.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.