What problem does it solve?
Manually deciding which approved stories fit into a TAPD iteration is error-prone: dependencies get missed, iteration capacity overflows, and re-planning an existing iteration risks duplicating work. This Skill automates iteration planning by evaluating the approved story pool, resolving dependencies, and filling the iteration within a configurable size budget.
Core Features & Use Cases
- Dependency-aware scheduling: Parses dependency sections in story descriptions, builds a DAG, and orders stories by topological order, size, and priority so prerequisites enter the iteration first.
- Capacity-controlled planning: Enforces a total size limit (default 1000), computes remaining capacity when re-entering an existing iteration, and skips stories that no longer fit.
- Dual modes: Creates a new iteration (with naming convention
iteration-vMAJOR.MINOR.x) or re-enters an existing one, supporting both automatic pool filtering and user-specified story IDs.
- Use Case: A product manager types "plan iteration iteration-v0.2.x" and the Skill creates the iteration, pulls approved leaf stories, expands their dependencies, topologically sorts them, assigns them via TAPD MCP until capacity is reached, and outputs a planning summary.
Quick Start
Ask the assistant to plan iteration iteration-v0.2.x using the approved stories in your TAPD workspace.
