beads-issue-tracking

Issue tracking across multiple AI sessions requires a persistent dependency graph that survives context compaction. This Skill stores issues and relationships locally, using notes fields designed for post-compaction recovery so you can resume work seamlessly after context windows are cleared.

Dependency management tracks blockers, related tasks, and parent-child hierarchies to visualize project structure. By mapping these relationships, the system automatically identifies ready work, showing which tasks are unblocked and available to start during your current session.

Git syncing for issue tracking is fully supported. Issues and their dependency relationships are stored locally and synced via Git, ensuring project memory and context are never lost across different development environments or when collaborating with team members.

Managing multi-week feature development benefits from a persistent issue graph that tracks subtasks, dependencies, and design decisions. This approach ensures you can pick up exactly where you left off, even after significant time away or AI context resets, by checking for ready work at session start.

Proactive issue creation during discovery is supported. As you explore the project, you can capture new issues on the fly, recording their discovery origins and relationships to ensure no newly identified tasks or dependencies are lost between sessions.

Context persistence for issue tracking is needed when managing complex, multi-session projects in software engineering. It becomes essential when AI context windows are frequently cleared, requiring notes fields designed for post-compaction recovery to maintain project continuity and detailed session handoffs.