Multi-Agent Serial Pipeline with Cursor + Git Worktree

Practice Log: Multi-Agent Serial Pipeline with Cursor + Git Worktree

Core stack: Cursor | Git Worktree | Opus 4.6 & Codex-5.3

Core goal: Use a single prompt to trigger multiple sub-agents into an automated serial workflow, enabling parallel development across multiple tasks.

Current proposal: openspec/changes/redesign-skill-card-vertical

Automated Serial Workflow (Agent Pipeline)

• Note: Do not use a SubAgent for any step that does not explicitly ask for one.

0 TASK 0: Proposal review

• Model: Gemini-3.1-pro
• Task: Use the openspec-review-specs skill.
• Focus: Review proposal completeness, key data flows, and property/interface alignment.

1 TASK 1: Architect review

• Model: Opus 4.6
• Task: Review and improve the proposal from an architect's perspective. Use the neversight-skills_feed-system-architect skill.
• Focus: Examine the system design, look for reuse opportunities, and make sure the solution is robust.

2 TASK 2: Implementation

• Model: Opus 4.6
• Task: Run /opsx/apply directly to turn the refined proposal into code.

3 TASK 3: Code review

• Model: Codex-5.3 (model switch)
• Task:
  • Run git commit to save the initial implementation.
  • Review the current commit strictly against the proposal.
  • This phase is read-only: do not modify code, so the review remains objective.

4 SubAgent 4: Auto-refinement

• Model: Opus 4.6 (switch back)
• Task: Ingest the review report from Agent 3, then update and fix the implementation based on the feedback.

5 SubAgent 5: Archiving

• Model: Opus 4.6
• Task:
  • Run /opsx/archive to archive the proposal status.
  • Run the final git commit to close the loop for this feature.

Bottleneck Review and Optimization

The current pipeline is highly automated, but the total runtime is still a bit long.

• Optimization idea: The current sub-agent responsibilities are too fine-grained. A next step is to reduce granularity by merging highly cohesive tasks, such as combining architect review and implementation into one larger agent step, or combining auto-refinement and archiving. This reduces communication overhead from model switching and context passing, which should improve total execution speed.