By v1truv1us
An engineering toolkit that enforces trunk-based development, code quality discipline, and automated PR workflows. Provides structured research, security audits, performance reviews, testing patterns (TDD, Playwright, smoke tests), and deployment pipelines for Docker, Kubernetes, npm packages, and Coolify. Includes multi-agent coordination for parallel research, CI/CD triage, and refactoring across repositories.
Runs all 9 research query templates from `wiki/research-runner.md` in parallel against a single question, then writes a dated brief to `wiki/briefs/YYYY-MM-DD.md`.
Runs all 9 query templates in parallel against a research question using the
Scheduled research agent that runs on Coolify as a Docker container.
Premier research workflow for systematic investigation, synthesis, and verification. Use for literature reviews, competitive analysis, deep dives, market research, or any task requiring structured research with source attribution.
Daily research agent for
Teach the user a topic through a stateful learning workspace — mission-driven lessons, retrieval practice, cited resources, and durable learning records. Use when the user says teach me, /teach, wants interactive lessons, or is learning something over multiple sessions.
Red-Green-Refactor workflow: write tests before implementation, maintain coverage. Use when implementing logic, fixing bugs, or changing behavior.
Self-continuing loop that fixes a deliberately failing test, reruns it, and exits cleanly when green. Smallest cooking-routines skill — pairs with the cooking Stop hook to keep iterating past natural stops.
Review recently changed files for reuse, quality, and efficiency issues, then fix them. Use after completing a change, before declaring done.
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it. Use for /arena, 'arena this', 'throw it in the arena', or when one attempt at a non-trivial artifact would lock in the wrong shape.
Uses power tools
Uses Bash, Write, or Edit tools
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AI engineering workflow toolkit for Claude Code and OpenCode with namespaced commands, 38 specialized agents, and reusable skills covering the full development lifecycle from idea to production.
This repository ships three npm packages:
@ai-eng-system/core - shared library and content-loading helpers@ai-eng-system/toolkit - generated Claude Code, OpenCode, Cursor, Gemini, Pi, and marketplace assets@ai-eng-system/cli - executable installer and command-line workflowsThe repo root package is private and is never published.
Scheduled Research Runner (Pi cron on your VPS): docs/deploy/coolify.md
Optional docs site: docs-site/DEPLOYMENT.md
npm install -g @ai-eng-system/cli
# Install commands, agents, and skills into the current project
ai-eng install --scope project
# Or install globally for OpenCode
ai-eng install --scope global
/plugin marketplace add v1truv1us/ai-eng-system
/plugin install ai-eng-system@ai-eng-marketplace
{
"$schema": "https://opencode.ai/config.json",
"plugin": ["opencode-skills", "ai-eng-system"]
}
OpenCode learning automation now surfaces toast-based suggestions for /ai-eng/decision-journal and /ai-eng/quality-gate, then waits for explicit /ai-eng/learning-approve, /ai-eng/learning-dismiss, or /ai-eng/learning-snooze consent. Local policy and state live under .ai-context/learning/.
pi install npm:@ai-eng-system/toolkit
Pi loads skills from .pi/skills/ and command prompts from .pi/prompts/ in the toolkit package.
See docs/cursor-setup.md. Install @ai-eng-system/toolkit and use the generated .cursor-plugin bundle (skills, agents, and rules/cursor/).
See docs/gemini-cli-setup.md. Install @ai-eng-system/toolkit and copy the generated .gemini/ bundle (skills and commands).
| Phase | Command | Purpose |
|---|---|---|
| Research | /ai-eng/research | Multi-phase codebase and external research |
| Specify | /ai-eng/specify | Feature/spec generation with TCRO structure |
| Plan | /ai-eng/plan | Implementation planning |
| Work | /ai-eng/work | Guided execution with quality gates |
| Verify | /verify | Lint, typecheck, test, build gate |
| Review | /ai-eng/review | Multi-agent code review |
Shorthand lifecycle entrypoints:
| Shorthand | Canonical Command |
|---|---|
/spec | /ai-eng/specify |
/build | /ai-eng/work |
/ai-eng/plan and /ai-eng/review are direct lifecycle entrypoints with no separate shorthand file.
Related commands:
/ai-eng/ralph-wiggum - iterative full-cycle workflow/ai-eng/simplify - code reuse, quality, and efficiency simplificationai-eng/ namespace plus shorthand lifecycle entrypointsSelected commands beyond the core workflow:
/ai-eng/create-plugin, /ai-eng/create-agent, /ai-eng/create-command, /ai-eng/create-skill, /ai-eng/create-tool/ai-eng/code-review, /ai-eng/agent-analyzer, /ai-eng/fact-check, /ai-eng/deep-research, /ai-eng/content-optimize/ai-eng/deploy, /ai-eng/docker, /ai-eng/cloudflare, /ai-eng/github, /ai-eng/k8s, /ai-eng/monitoring, /ai-eng/security-scan/ai-eng/context, /ai-eng/knowledge-capture, /ai-eng/knowledge-architecture, /ai-eng/decision-journal, /ai-eng/quality-gate, /ai-eng/maintenance-review, /ai-eng/learning-approve, /ai-eng/learning-dismiss, /ai-eng/learning-snooze, /ai-eng/init, /ai-eng/seoClaude marketplace packaging note:
ai-eng-core keeps the core plan/work/review workflowai-eng-learning now packages /ai-eng/knowledge-architecture, /ai-eng/decision-journal, /ai-eng/quality-gate, /ai-eng/maintenance-review, /ai-eng/learning-approve, /ai-eng/learning-dismiss, and /ai-eng/learning-snooze/ai-eng/knowledge-capture remains outside that plugin groupSee docs/reference/commands.md for the full command list.
The generated outputs now preserve namespaced skill paths.
Examples:
skills/ai-eng/simplify/SKILL.md -> /ai-eng/simplifyskills/workflow/ralph-wiggum/SKILL.md -> /ai-eng/ralph-wiggumskills/comprehensive-research/SKILL.md -> /ai-eng/researchskills/knowledge-architecture/SKILL.md -> /ai-eng/knowledge-architectureSee docs/reference/skills.md for the full skill inventory.
npx claudepluginhub v1truv1us/ai-eng-systemContent optimization, SEO, and communication tools
Infrastructure, deployment, and DevOps automation
Meta-tooling for creating plugins, agents, commands, and skills
Core workflow: plan, work, review cycle with research and context engineering
Learning workflows for knowledge mapping, decisions, quality gates, and maintenance reviews
Reliable automation, in-depth debugging, and performance analysis in Chrome using Chrome DevTools and Puppeteer
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
Harness-native ECC operator layer - 64 agents, 261 skills, 84 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
Complete developer toolkit for Claude Code
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex and antigravity CLIs when installed) to get diverse perspectives on coding problems