@ai-eng-system/toolkit
Advanced tools
Compounding engineering system for Claude Code and OpenCode. Shared agents, commands, skills, and plugin development tools.
Advanced development tools with context engineering, research orchestration, and 28 specialized agents for Claude Code & OpenCode.
This toolkit follows spec-driven development methodology from GitHub's official blog post: Spec-driven development with AI: Get started with a new open source toolkit.
The 5-Phase Workflow (always in this order):
flowchart LR
A[🎯 Research] --> B[📋 Specify] --> C[📝 Plan] --> D[🔨 Work] --> E[🔍 Review]
D -.->|If changes needed| E
E -.->|Repeat| D
| Phase | Command | Output | Ralph Wiggum 🔄 |
|---|---|---|---|
| 1. Research | /ai-eng/research | Context and findings | --ralph for iterative deepening |
| 2. Specify | /ai-eng/specify | Feature specification | --ralph for requirement refinement |
| 3. Plan | /ai-eng/plan | Implementation plan | --ralph for task completeness |
| 4. Work | /ai-eng/work | Quality-gated code | --ralph for TDD cycles |
| 5. Review | /ai-eng/review | Multi-agent approval | --ralph for thorough analysis |
This approach ensures specifications are your "source of truth" for what gets built, reducing guesswork and enabling more reliable AI-assisted development.
🔄 New: Ralph Wiggum Iteration - Add --ralph flag to any phase command for persistent refinement until quality standards are met. See Ralph Wiggum Integration Guide.
See docs/spec-driven-workflow.md for complete workflow guide with examples.
/plugin marketplace add v1truv1us/ai-eng-system
/plugin install ai-eng-system@ai-eng-marketplace
# Add to opencode.jsonc:
{
"$schema": "https://opencode.ai/config.json",
"plugin": ["opencode-skills", "ai-eng-system"]
}
# Run OpenCode - plugin auto-installs commands, agents, and skills
Core workflow for systematic development (always research → specify → plan → work → review):
| Phase | Command | Purpose | Ralph Wiggum 🔄 |
|---|---|---|---|
| 1. Research | /ai-eng/research | Multi-phase research with codebase and external context | Iterative deepening, gap analysis |
| 2. Specify | /ai-eng/specify | Create feature specifications with TCRO framework | Requirement refinement, completeness |
| 3. Plan | /ai-eng/plan | Create detailed implementation plans from specs | Task atomicity, dependency mapping |
| 4. Work | /ai-eng/work | Execute plans with quality gates and validation | TDD cycles, implementation refinement |
| 5. Review | /ai-eng/review | Multi-perspective code review (28 agents) | Escalating focus, thorough analysis |
Methodology: Based on GitHub's spec-driven development approach
🔄 Ralph Wiggum: Add
--ralphflag to any phase command for persistent iteration. Perfect for complex tasks requiring multiple refinement cycles. Learn more →
🛠️ Fixed (v0.2.2): Ralph Wiggum now runs continuously without stopping between phases. Full backward compatibility maintained. Fix details →
New! Terminal-based interface for autonomous research and implementation workflows:
# Install globally via npm (requires Node.js or Bun)
npm install -g ai-eng-system
ai-eng ralph "implement user authentication"
# Or run directly from source (requires Bun)
bun run build
bun src/cli/run.ts "implement user authentication"
# Shortcut: "ai-eng prompt" defaults to ralph
ai-eng "fix the bug" --print-logs
Installation command (replaces automatic postinstall):
ai-eng install --scope project # Install to project .opencode/
ai-eng install --scope global # Install to ~/.config/opencode/
Features:
Documentation: See docs-site/docs/features/ai-eng-ralph-cli.md for complete guide
/ai-eng/optimize - Prompt enhancement (+45% quality)/ai-eng/deploy - Pre-deployment checklists/ai-eng/compound - Document solved problems/ai-eng/recursive-init - Initialize AGENTS.md across directories/ai-eng/create-plugin - AI-assisted plugin creation/ai-eng/create-agent - AI-assisted agent generation/ai-eng/create-command - AI-assisted command generation/ai-eng/create-skill - AI-assisted skill creation/ai-eng/create-tool - AI-assisted custom tool creation/ai-eng/context - Context management and retrieval/ai-eng/clean - Remove build artifacts and generated files/ai-eng/seo - SEO audits with Core Web VitalsTotal Commands: 17 + 1 CLI
architect-advisor, backend-architect, infrastructure-builderfrontend-reviewer, full-stack-developer, api-builder-enhanced, database-optimizer, java-procode-reviewer, test-generator, security-scanner, performance-engineerdeployment-engineer, monitoring-expert, cost-optimizerai-engineer, ml-engineerseo-specialist, prompt-optimizeragent-creator, command-creator, skill-creator, tool-creator, plugin-validatordevops - Coolify deployment, Git worktree workflowsprompting - Research-backed incentive prompting techniquesresearch - Comprehensive multi-phase research orchestrationplugin-dev - Plugin development knowledge base and referencestext-cleanup - Pattern-based text cleanup (slop, comments)workflow/ralph-wiggum - Persistent iteration patterns for quality refinement (NEW!)🔄 Ralph Wiggum Philosophy: "Iteration > Perfection, Failures Are Data, Persistence Wins" - Now available across all phase commands with
--ralphflag
New! docs/subagent-orchestration-guide.md - Configure automatic agent routing for ai-eng-system
Learn how to configure subagent orchestration when using ai-eng-system with OpenCode or Claude Code:
Quick Answer: How does orchestrator always work?
Both ensure reliable routing to ai-eng-system's 28 specialized agents!
This repo follows Anthropic's official Claude Code marketplace pattern:
.claude-plugin/marketplace.json (only file at repo root)plugins/ai-eng-system/ with standard plugin layoutcontent/ sources into platform-specific outputsdist/.opencode/bun run build # Build all platforms
bun run build:watch # Watch mode
bun run validate # Validate content without building
bun test # Run test suite
├── content/ # Canonical markdown sources
│ ├── commands/ # Command definitions
│ └── agents/ # Agent definitions
├── skills/ # Skill packs
├── plugins/ # Embedded Claude plugin
├── dist/ # Built outputs (committed)
├── .claude-plugin/ # Marketplace manifest
└── .opencode/ # OpenCode config
https://github.com/v1truv1us/ai-eng-systemv1truv1us/ai-eng-marketplaceai-eng-system~/.config/opencode/ (default)./.opencode/ (project-specific)ai-eng/Deploy with Coolify (recommended):
Built Site: docs-site/ directory
The documentation site is built with Astro + Starlight and ready for deployment:
# Local development
cd docs-site
bun install
bun run dev # http://localhost:4321
# Build for production
bun run build
# Deploy to Coolify
# Repository path: docs-site/
# Build command: bun install && bun run build
# Output directory: dist
# 1. Create opencode.jsonc in your project
cat > opencode.jsonc << 'EOF'
{
"$schema": "https://opencode.ai/config.json",
"plugin": ["opencode-skills", "ai-eng-system"]
}
EOF
# 2. Install package (optional - auto-installs via plugin)
npm install ai-eng-system
# 3. Run OpenCode
# Commands and agents are automatically available!
When OpenCode loads the plugin, it automatically:
.opencode/command/ai-eng/.opencode/agent/ai-eng/.opencode/skill/# Basic workflow
/ai-eng/plan # Create implementation plan
/ai-eng/review # Multi-agent code review
/ai-eng/work # Execute implementation plan
/ai-eng/seo # SEO audit
/ai-eng/create-agent # Generate new agent
# Ralph Wiggum iteration (NEW!)
/ai-eng/work "feature" --ralph # TDD cycles until complete
/ai-eng/research "topic" --ralph --ralph-max-iterations 15 # Deep research
/ai-eng/specify "feature" --ralph --ralph-quality-gate="rg '\[NEEDS CLARIFICATION\]'"
/ai-eng/plan --from-spec=specs/feature.md --ralph # Enhanced planning
/ai-eng/review src/ --ralph --ralph-focus=security # Thorough review
# ... and 11 more commands
Commands reference specialized agents automatically:
/ai-eng/review --agent=code-reviewer # Quality-focused review
/ai-eng/review --agent=frontend-reviewer # Frontend review
/ai-eng/review --agent=backend-architect # Architecture review
Built with research-backed prompting techniques (+45-115% quality improvement)
For complete documentation on the Research → Specify → Plan → Work → Review workflow, see:
FAQs
Packaged Claude, OpenCode, and marketplace integration assets for ai-eng-system
The npm package @ai-eng-system/toolkit receives a total of 22 weekly downloads. As such, @ai-eng-system/toolkit popularity was classified as not popular.
We found that @ai-eng-system/toolkit demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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