I’ve been working on a lightweight workflow that sits on top of Claude Code and Vibe Kanban to make AI-assisted development more structured and less fragile over time.
It’s not a product or UI. It’s a set of slash commands that run inside Claude Code and use Vibe Kanban (via MCP) as the persistent coordination layer.
The core flow is:
- PRD review with clarifying questions (optional PRD generation)
- Development plan with epics, task dependencies, complexity, and acceptance criteria
- Bidirectional sync with Vibe Kanban (drift detection, dependency violations)
- Task execution with full context (PRD + plan + AC + codebase)
Most of this has been exercised heavily in a single-task, human-in-the-loop model.
Recently I started experimenting with parallel execution, using full agent sessions in isolated git worktrees (and optional delegation to VK workspace sessions). Early results are promising: small batches of independent tasks complete much faster, while still stopping on conflicts and keeping humans in the loop for merges and judgment calls.
The main idea is treating task systems as memory and governance for agents, not just tracking, and making parallelism dependency-aware rather than optimistic.
Docs include architecture notes, comparisons with other agent frameworks, and a cookbook with concrete workflows.
I’ve been working on a lightweight workflow that sits on top of Claude Code and Vibe Kanban to make AI-assisted development more structured and less fragile over time.
It’s not a product or UI. It’s a set of slash commands that run inside Claude Code and use Vibe Kanban (via MCP) as the persistent coordination layer.
The core flow is:
- PRD review with clarifying questions (optional PRD generation)
- Development plan with epics, task dependencies, complexity, and acceptance criteria
- Bidirectional sync with Vibe Kanban (drift detection, dependency violations)
- Task execution with full context (PRD + plan + AC + codebase)
Most of this has been exercised heavily in a single-task, human-in-the-loop model.
Recently I started experimenting with parallel execution, using full agent sessions in isolated git worktrees (and optional delegation to VK workspace sessions). Early results are promising: small batches of independent tasks complete much faster, while still stopping on conflicts and keeping humans in the loop for merges and judgment calls.
The main idea is treating task systems as memory and governance for agents, not just tracking, and making parallelism dependency-aware rather than optimistic.
Docs include architecture notes, comparisons with other agent frameworks, and a cookbook with concrete workflows.
Repo: https://github.com/ericblue/claude-vibekanban
Would appreciate feedback, especially from folks who’ve tried running multiple agents on non-trivial codebases and hit coordination or drift issues.