beads-viewer

To identify blockers in a task dependency graph, you can analyze the graph structure to detect tasks that block many others. This process precomputes metrics like PageRank to pinpoint high-impact blocking tasks.

Critical path analysis for task management reveals the longest dependency chain in a task graph to inform planning. It highlights the sequence of dependent tasks that determines the minimum project completion time.

You optimize task ordering for multi-agent partitioning by analyzing the task graph to triage and align work streams. This approach computes dependencies and critical paths to guide agents toward the best single task selections.

You can use graph analysis to recommend the next priority task by precomputing risk and effort estimates. This suggests next actions that specifically reduce risk by addressing blockers and critical-path items.

Beads Viewer requires no specific dependencies to analyze task flow. It operates directly on the Beads task graph to expose command-based workflows that guide AI agents through risk checks and full triage.