parallel
Multi-Agent Pipeline Orchestrator
You are the Multi-Agent Pipeline Orchestrator Agent, running in the main repository, responsible for collaborating with users to manage parallel development tasks.
Role Definition
- You are in the main repository, not in a worktree
- You don't write code directly - code work is done by agents in worktrees
- You are responsible for planning and dispatching: discuss requirements, create plans, configure context, start worktree agents
- Delegate complex analysis to research: find specs, inspect code structure, and reduce ambiguity before dispatch
Operation Types
Operations in this document are categorized as:
| Marker | Meaning | Executor |
|---|
More from mindfold-ai/trellis
trellis-meta
Understand and customize the local Trellis architecture inside a user project. Use when modifying .trellis plus platform hooks, settings, agents, skills, commands, prompts, or workflows generated by trellis init.
175cc-codex-spec-bootstrap
Claude Code + Codex parallel pipeline for bootstrapping Trellis coding specs. CC analyzes the repo with GitNexus (knowledge graph) + ABCoder (AST), creates Trellis task PRDs with full architectural context and MCP tool instructions, then Codex agents run those tasks in parallel to fill spec files. Use when: bootstrapping coding guidelines, setting up Trellis specs, 'bootstrap specs for codex', 'create spec tasks', 'CC + Codex spec pipeline', 'initialize coding guidelines with code intelligence'. Also triggers when user wants to set up GitNexus or ABCoder MCP for multi-agent spec generation.
89brainstorm
Collaborative requirements discovery session optimized for AI coding workflows. Creates task directories, seeds PRDs, runs codebase research, proposes concrete implementation approaches with trade-offs, and converges on MVP scope through structured Q&A. Use when requirements are unclear, multiple implementation paths exist, trade-offs need evaluation, or a complex feature needs scoping before development.
38break-loop
Deep post-fix bug analysis across five dimensions: root cause categorization, fix failure analysis, prevention mechanisms, systematic expansion, and knowledge capture. Updates .trellis/spec/ guides with lessons learned to prevent recurring bugs. Use when a debugging session completes, after fixing a tricky bug, when the same class of bug keeps recurring, or when you want to capture debugging insights into project documentation.
34record-session
Records completed work progress to .trellis/workspace/ journal files after human testing and commit. Captures session summaries, commit hashes, and updates developer index files for future session context. Use when a coding session is complete, after the human has committed code, or to persist session knowledge for future AI sessions.
32start
Initializes an AI development session by reading workflow guides, developer identity, git status, active tasks, and project guidelines from .trellis/. Classifies incoming tasks and routes to brainstorm, direct edit, or task workflow. Use when beginning a new coding session, resuming work, starting a new task, or re-establishing project context.
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