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Small Feature Ship Workflow A reusable workflow for shipping small product or engineering features with minimal human coordination and clear agent ownership. This workflow is optimized for small, bounded changes where the goal is to avoid process overhead while still protecting product judgment, implementation quality, and release safety. How to Use When a user makes a small feature/change request in chat, treat this workflow as the operating procedure and execute s001 → s004 in the same chat session. Intermediate artifacts (decision brief, adversarial review prompt, Codex verdict) live in the chat context — no external persistence needed. The final deliverable returned to the user in chat is the PR URL . Role Map | Role | Assignee | Responsibility | | | | | | Feature Owner | human | Owns intent, final judgment, and shipping decision. | | Decision Challenger | Claude Code | Challenges whether the feature should be built, whether the proposed solution is the right one, and what the real problem is. | | Builder | Claude Code | Implements the agreed solution, opens the PR. | | Reviewer | Codex (preferred) / Claude Code (fallback) | Adversarially reviews the PR. Codex owns the verdict when available; otherwise Claude Code performs the same adversarial review against its own work. | | Shipper | human | Reviews the PR, merges/releases, and observes early results. | Core Loop Decide → Build → Check → Ship Four steps. Each step has a clear owner, with explicit handoff artifacts so the next owner can proceed without ambiguity. Operating Principle Do not move forward simply because a feature request exists. The first step must pressure test the request — but right size the scrutiny to the change . A variable rename does not deserve a 9 point brief; a behavior change with user impact does. The Decide step defines what "enough scrutiny" looks like for each tier.
This workflow works with any coding agent that supports Skills, file access, shell commands, and GitHub operations. This workflow turns raw bug reports from a Discord channel into a continuous improvement loop for your product. A lightweight bot converts reports into structured GitHub Issues, a triage skill reads each need triage Issue and labels it as auto fix or manual fix, and an auto fix skill takes auto fix Issues, inspects the codebase, applies small focused changes, runs tests, and opens review ready pull requests. Engineers stay in control through labels and normal code review, while the system quietly handles the repetitive work of sorting bugs and drafting fixes, so the product keeps getting better from real user feedback with less manual overhead.
A battle tested 10 step setup for running multi agent Claude Code workflows: from initial CLAUDE.md config through skills, hooks, MCP connections, PM2 orchestration, parallelization, handoff contracts, done criteria, and verification loops. Synthesized from two sources battle tested over 10+ months of daily use: Shorthand Guide: https://x.com/affaanmustafa/status/2012378465664745795 Longform Guide: https://x.com/affaanmustafa/status/2014040193557471352 Preconditions: Claude Code installed and authenticated Node.js + PM2 installed (npm install g pm2) Git initialized in your project Themes covered: CLAUDE.md context design, skill architecture, hook patterns, session memory, MCP strategy, PM2 orchestration, git worktree parallelization, inter agent handoff contracts, done criteria, and verification loops.
This workflow is a practical, reproducible way to ship faster by combining: Git worktrees to maintain multiple isolated working directories from a single repository, so multiple tasks can proceed without branch switching or file contention. Coding agents (for example, terminal based agents) running one agent per worktree to enable true parallel execution with minimal interference. Stacked pull requests (small PRs that depend on each other) to keep review size small while still delivering larger features incrementally, supported by a reviewer playbook that prevents the stack from stalling.
This workflow enables developers to move beyond simple AI chatting by establishing a Local Second Brain (Project Knowledge Base) and Automated Skills . By maintaining an updated context of your project, you can delegate complex engineering tasks to AI with high accuracy. Source: Claude Code Workflows That Will 10x Your Productivity