From khanrad
This skill should be used when the user asks to "plan a project", "brainstorm a project", "plan a new app", "decompose a project into stories", "greenfield project planning", "break down an application idea", or wants to go from an application description to a set of Khanrad issues and stories.
How this skill is triggered — by the user, by Claude, or both
Slash command
/khanrad:plan-projectThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill takes a greenfield application description, guides the user through iterative refinement, decomposes the application into domains, generates stories and epics using parallel subagents, and populates a Khanrad board with tagged, prioritized issues.
This skill takes a greenfield application description, guides the user through iterative refinement, decomposes the application into domains, generates stories and epics using parallel subagents, and populates a Khanrad board with tagged, prioritized issues.
auth, payments, notifications). Each domain becomes a tag prefix: domain:<name>.domain:<name> — feature area (e.g., domain:auth, domain:orders)phase:mvp / phase:v2 / phase:future — release phasetype:epic / type:story / type:spike — work typecross-cutting — items spanning multiple domainsA fully populated Khanrad board with issues organized by domain tags, release phase, priority, and board state (Backlog for MVP, Ice Box for future work).
npx claudepluginhub savantly-net/khanrad-mcp-pluginPlans new projects or major epics by exploring domain, defining boundaries and architecture, decomposing into phased features, and producing a project plan artifact. Use for multi-feature work.
Guides software project planning with discovery questions, requirements gathering, user stories, MoSCoW prioritization, T-shirt estimation, scope management, risk assessment, and templates for briefs and epics. Use for new projects or features.
Structured project planning and PRD generation with three modes: new project kickoff, feature PRD, and data-driven retrospective. All modes use a researched Q&A engine with parallel exploration agents.