strategic-planning

To prioritize your backlog, analyze task dependencies, bottlenecks, and risks within the project graph to rank work by impact. This surfaces high-leverage tasks and flags blockers, enabling faster, data-driven decisions during backlog triage or sprint planning.

Finding bottlenecks in a project graph involves querying for top blockers that slow progress. By analyzing dependencies and risks, you can surface these critical constraints, estimate parallelism, and generate recommendations to unblock tasks and accelerate delivery.

Yes, you can assess parallel tasks during sprint planning by estimating how many tasks can run concurrently within the project graph. This parallelism assessment helps distribute work effectively across available agents and identifies dependencies that might limit concurrent execution.

Yes, HtmlGraph provides a Python SDK to generate data-driven work recommendations. By initializing the SDK and accessing the project task graph, you can use functions like recommend_next_work and find_bottlenecks to prioritize tasks based on dependencies and impact.

Data-driven prioritization should be used during new-session starts, backlog triage, sprint planning, and risk assessments. It is most effective when you need to identify high-impact work, detect blockers slowing progress, and estimate parallelism to make faster, more informed decisions.