Browse Skills
$ skillbazaar install --author deanpeters --agent claudeSign in (free) to install skills with the CLI.
$ skillbazaar install --author deanpeters --agent claudeSign in (free) to install skills with the CLI.
Run a user story mapping workshop with adaptive questions and a structured map output. Use when you need backbone activities, tasks, and release slices for a workflow.
Run a full discovery cycle from problem hypothesis to validated solution. Use when a team needs a structured path through framing, interviews, synthesis, and experiments.
Turn raw PM content into a compliant, publish-ready skill. Use when creating or updating a repo skill without breaking standards.
Guide teams through MITRE's Problem Framing Canvas. Use when you need a clearer problem statement before jumping to solutions.
Create user stories with Mike Cohn format and Gherkin acceptance criteria. Use when turning user needs into development-ready work with clear outcomes and testable conditions.
Write a user-centered problem statement with who is blocked, what they are trying to do, why it matters, and how it feels. Use when framing discovery, prioritization, or a PRD.
Guide the PM-to-Director transition across preparing, interviewing, landing, and recalibrating. Use when leadership scope is changing and you need practical coaching.
Calculate TAM, SAM, and SOM with explicit assumptions, methods, and caveats. Use when sizing a market for a product idea, business case, or executive review.
Analyze political, economic, social, technological, environmental, and legal forces. Use when external market shifts could materially affect a product, roadmap, or strategy.
Create a customer journey map across stages, touchpoints, actions, emotions, and metrics. Use when diagnosing a broken experience or aligning a team on the full customer flow.
Structure a spoken PM product-sense answer with assumptions, segmentation, pain-point prioritization, and MVP tradeoffs. Use when practicing design, improve, or build-next interview questions.
Diagnose context stuffing vs. context engineering. Use when an AI workflow feels bloated, brittle, or hard to steer reliably.