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ai-coding-setup

Set of prompts, skills, and scripts to aid in utilizing AI coding agents in development workflows.

Prerequisites

Quick Start

git clone https://github.com/rlorenzo/ai-coding-setup.git
cd ai-coding-setup
./setup

The script detects which AI tools you have installed and walks you through installing commands for each one interactively.

Windows: Run the setup script from Git Bash.

Supported Tools

Tool Command format Source directory Installs to
Claude Code Markdown (.md) .claude/commands/ ~/.claude/commands/
Gemini CLI TOML (.toml) .gemini/commands/ ~/.gemini/commands/
Codex CLI Agent Skills (SKILL.md) .codex/skills/ ~/.codex/skills/
Copilot CLI Agent Skills (SKILL.md) .copilot/skills/ ~/.copilot/skills/
Antigravity CLI Unified Plugin (plugin.json) .antigravity/ ~/.gemini/antigravity-cli/plugins/ai-coding-setup/
Shared prompts Markdown (.md) prompts/ ~/.local/share/ai-coding-setup/prompts/

Available Commands

/commitmsg

Propose a conventional commit message for the currently staged changes. Detects ticket IDs from branch names and follows the project's recent commit style.

Usage:

  • Claude Code: /commitmsg
  • Gemini CLI: /commitmsg
  • Codex CLI: $commitmsg
  • Copilot CLI: /commitmsg
  • Antigravity CLI: /commitmsg

/review-pr

Process unresolved review comments on a GitHub PR, fix valid issues, ensure CI passes, and re-request review.

Usage:

  • Claude Code: /review-pr [PR_NUMBER]
  • Gemini CLI: /review-pr [PR_NUMBER]
  • Codex CLI: $review-pr [PR_NUMBER]
  • Copilot CLI: /review-pr [PR_NUMBER]
  • Antigravity CLI: /review-pr [PR_NUMBER]

/code-refinement

Review staged files for code quality (KISS, DRY, YAGNI, Clean Code), fix linting issues, and check test coverage.

Usage:

  • Claude Code: /code-refinement
  • Gemini CLI: /code-refinement
  • Codex CLI: $code-refinement
  • Copilot CLI: /code-refinement
  • Antigravity CLI: /code-refinement

/code-review

Run a standalone code review on staged changes. Writes findings to agent-code-review.md.

Usage:

  • Claude Code: /code-review
  • Gemini CLI: /code-review
  • Codex CLI: $code-review
  • Copilot CLI: /code-review
  • Antigravity CLI: /code-review

Review Loops

Two multi-agent feedback loops live in bin/: code-review-loop (for staged code) and plan-review-loop (for plan documents). Each loop pairs an editor agent with a different reviewer agent and iterates until the reviewer is satisfied or --max-iterations is hit. Using two different models for editing and reviewing surfaces issues a single agent tends to miss in its own output.

Both scripts are installed onto your PATH by ./setup and rely on the prompts in prompts/ (installed to ~/.local/share/ai-coding-setup/prompts/).

code-review-loop

Runs a full review cycle over your staged changes:

  1. Refinement: editor agent runs the code-refinement prompt (lint, KISS/DRY/YAGNI, test coverage). Skip with -s.
  2. Stage: any fixes from refinement are staged.
  3. Initial review: reviewer agent writes findings to agent-code-review.md.
  4. Fix → re-review loop: editor responds to findings, reviewer re-reviews, repeat until clean or max iterations.
  5. Summary: editor writes a narrative summary to agent-review-summary.md.

A pre-review snapshot of your staged work is saved to the git stash so you can restore the original if the loop mangles something. Partially staged files are rejected up front, so fully stage or unstage before running.

Usage:

code-review-loop                                # default agents, 5 iterations
code-review-loop -m 3                           # cap at 3 review cycles
code-review-loop -s                             # skip the refinement step
code-review-loop --editor claude --reviewer codex

Outputs (project root): agent-code-review.md (latest findings), agent-review-summary.md (narrative).

plan-review-loop

Iteratively improves a plan document through review feedback:

  1. Initial review: reviewer agent reads the plan, writes structured feedback to feedback-plan.md.
  2. Improve → re-review loop: editor revises the plan in place, reviewer re-reviews, repeat until the reviewer emits NO_FURTHER_FEEDBACK or max iterations.
  3. Summary: editor writes a narrative summary to plan-review-summary.md.

Usage:

plan-review-loop PLAN-feature.md
plan-review-loop -m 3 PLAN-feature.md
plan-review-loop --reviewer claude --editor codex PLAN-feature.md

Outputs (project root): the plan file is edited in place; feedback-plan.md (latest feedback, removed when reviewer is satisfied); plan-review-summary.md (narrative).

Configuration

Defaults are --editor claude --reviewer codex. Override per-run with -e / -r, or persist defaults in ~/.ai-coding-setup.conf:

EDITOR_AGENT=claude
REVIEWER_AGENT=codex

Supported agents: claude, codex, gemini, copilot, antigravity. Only the agents you actually have installed need to be referenced.

Shared prompts

Both loops are driven by agent-agnostic prompts in prompts/, not interactive commands. They're listed here so you can audit or tweak the behavior:

Prompt Used by Role
code-refinement.md code-review-loop Editor's lint/refine pre-review step
code-review.md code-review-loop Reviewer's initial pass
code-review-followup.md code-review-loop Reviewer's follow-up passes
code-review-response.md code-review-loop Editor's response to findings
plan-review.md plan-review-loop Reviewer's initial pass
plan-review-followup.md plan-review-loop Reviewer's follow-up passes

How It Works

  • Each AI tool has its own command format, so commands are maintained as separate source files per tool.
  • The setup script copies selected commands to the appropriate user-level directory for each tool.
  • Shared prompts are installed to ~/.local/share/ai-coding-setup/prompts/ and referenced by the review loop scripts.
  • Installed commands are tagged with a source marker so the script can safely update them later without overwriting your custom commands that happen to share the same name.

MCP Server Configuration

The setup script can configure Model Context Protocol (MCP) servers for your AI tools. Currently supported:

Server Package Description
Playwright @playwright/mcp@latest Browser automation and web testing

MCP servers are added via each tool's mcp add CLI command at user scope.

Adding New Commands

To add a command, create the appropriate file(s) for each tool you want to support:

  1. Claude Code: create .claude/commands/command-name.md (markdown with $ARGUMENTS placeholder)
  2. Gemini CLI: create .gemini/commands/command-name.toml (TOML with description and prompt fields, {{args}} placeholder)
  3. Codex CLI: create .codex/skills/command-name/SKILL.md (markdown with YAML front matter containing name and description)
  4. Copilot CLI: create .copilot/skills/command-name/SKILL.md (same format as Codex skills)
  5. Antigravity CLI: create .antigravity/skills/command-name/SKILL.md (same format as Codex/Copilot skills)

Run ./setup again to install.

Uninstalling

Delete the command/skill from the corresponding directory (or uninstall the plugin for Antigravity):

  • Claude: ~/.claude/commands/
  • Gemini: ~/.gemini/commands/
  • Codex: ~/.codex/skills/
  • Copilot: ~/.copilot/skills/
  • Antigravity: Run agy plugin uninstall ai-coding-setup

The setup script only manages commands it originally installed.

Contributing

Running Tests

The test suite uses BATS (Bash Automated Testing System). After cloning with submodules:

git clone --recurse-submodules https://github.com/rlorenzo/ai-coding-setup.git
cd ai-coding-setup
test/run

If you already cloned without submodules:

git submodule update --init --recursive
test/run

Unit tests (test/run) cover config parsing, prompt loading, validation, and review status checks. They run in seconds and need no API keys.

Smoke Tests

Smoke tests run real AI agents against a temporary git repo to verify that CLI flags are accepted and agents can perform basic read/write tasks:

test/smoke                   # test all installed agents
test/smoke claude codex      # test specific agents
test/smoke --timeout 180     # override per-test timeout (default: 120s)

Each installed agent is tested as both editor (can it modify a file?) and reviewer (does it produce a review file?). Requires at least one AI tool installed and authenticated.

Pre-commit hooks (optional)

This repo uses pre-commit to run linters locally before each commit. Install it once and you'll get automatic checks for shell scripts (shellcheck), markdown (markdownlint), and TOML syntax.

pip install pre-commit   # or: brew install pre-commit (macOS)
pre-commit install

After that, hooks run automatically on git commit. You can also run them manually:

pre-commit run --all-files

If you skip the local setup, the same checks run in CI on your pull request.

License

MIT

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Set of prompts/skills/scripts to aid in utilizing AI coding agents in dev workflows

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