larch

Larch

Larch is a Claude Code workflow automation framework that orchestrates multi-agent design, code review, and implementation through collaborative AI-driven processes.

Table of Contents

• Setup
  • Installation and Setup — plugin install, local development, agent setup recipes (Claude / Codex / Cursor), clean-main entry contract for /implement and /design, what the plugin provides, the python/cli.py checks run-relevant consumer contract, prerequisites
• Reference
  • Features
  • Skills
  • Aliases
  • Review Agents — the unified code-reviewer archetype
  • Run Logs — committed larch-logs/ batch files, manifest, and tracking-issue comments
  • Topology Projection — stable anchors for cross-doc topology counts
  • Linting — linters, Makefile targets, halt-rate regression harness
  • Issue-Anchored Plan — live wire format for the /design ↔ /implement plan handoff and clarification round-trip
• Architecture and workflow
  • Workflow Lifecycle — how skills compose end-to-end
  • Agent System — parallel subagent orchestration
  • Design Flow — direct plan drafting and plan review
  • Voting Process — the voting panel protocol
  • Point Competition — reviewer scoring system

Features

• Direct design planning and plan reviews — Step 2b drafts the plan directly, then the validation panel reviews it.
• Voting-based review resolution — The YES/NO panel protocol adjudicates plan and code review findings.
• Reviewer competition scoring — Reviewers earn points based on finding quality; a scoreboard tracks accepted, neutral, and rejected findings.
• Tracked runs — /implement writes full run artifacts to committed larch-logs/ files and keeps the tracking issue slim with marker-keyed summary comments.

Skills

larch ships public skills with the plugin (skills/); private skills live under .claude/skills/ and are dev-only (not exported). Both are listed below; shortcut aliases are in the Aliases section. See docs/skills.md for full per-skill detail.

Public skills