Coordinate parallel feature work by assigning each task its own Git worktree lane and orchestrator. Track lane reports and reviews, then integrate candidates one at a time while keeping a single writer on the main branch.
How a root LLM runs several nwdes lanes in parallel with one lane orchestrator per lane, on any host. Load when more than one feature or lane is open or about to be.
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Use a canonical Markdown skeleton to draft an agent specification, including frontmatter, role, goal, principles, workflow, critical rules, examples, and constraints. It serves as a reference for creation and migration procedures, with no required sequence.
Use this reference catalog to spot common design problems in agents, skills, and commands. It explains why patterns such as oversized instructions, redundant skill loading, or inconsistent terminology cause trouble, and gives a focused remedy for each.
Follow a five-phase process to create one new AI agent: analyze its responsibility and overlap, design its role and principles, create it from a template, validate it against a checklist, and refine observed issues. The workflow composes a separate specification-validation skill.
Review agent definitions with a checklist covering format, focus, safety, examples, skill loading, token use, and whether the agent addresses the largest problem. It helps produce a structured verdict with findings and suggested fixes.
Consult worked examples for creating agents, migrating oversized specifications, deciding when to extract reusable skills, and simplifying commands. The examples help guide those procedures with concrete structural choices, without imposing a fixed sequence.
A reference for authoring and modifying agents or skills with concise, structured content. It defines a compact house style, requires a reasoning mandate in changed agents, and sets a measured threshold for compressing prose while preserving machine-readable contracts.