By v1truv1us
Orchestrates the full AI-assisted development lifecycle: planning, research, architecture design, implementation, code review, debugging, and documentation. Provides structured workflows, specialized agents, and context management to improve code quality and developer productivity.
Manage session state, memories, and context engineering
List active cooking-routines loops by reading marker files. Prunes stale markers (dead PIDs or mismatched start_epoch).
Route a task to the right Anthropic-powered subagent. The conductor assesses complexity and intent, then dispatches to LookupAgent (Haiku), WorkAgent (Sonnet), PlannerAgent (Opus), DebuggerAgent (Sonnet), or RefactorAgent (Opus).
Initialize ai-eng-system configuration and project setup
Orchestrate multiple agents for complex development tasks
Architectural guidance and technical decisions
Design RESTful APIs, microservice boundaries, and database schemas. Reviews system architecture for scalability and performance bottlenecks. Use PROACTIVELY when creating new backend services or APIs.
Conductor agent that receives tasks, assesses complexity and intent, and routes to the appropriate Anthropic-powered subagent. Supports single-step dispatch and multi-step chaining.
Generalist implementation developer focused on end-to-end feature delivery (UI → API → data) within established architectural, security, performance, and infrastructure guidelines. Provides cohesive, maintainable full-stack solutions while deferring deep specialization decisions to appropriate expert agents.
Expert Java development with modern Java 21+ features
Contract-first design, Hyrum's Law, One-Version Rule, error semantics, boundary validation. Use when designing APIs, module boundaries, or public interfaces.
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in. Use for /architect, 'architect this', 'design this', or non-trivial work where jumping to code would lock in the wrong shape.
Simplifies code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be.
Use only when the user explicitly types `/orchestrate <goal>` to decompose a large task, spawn a tree of parallel cloud-agent workers/subplanners/verifiers via the Cursor SDK, and collect structured handoffs; do not invoke autonomously.
Decompose specs into small, verifiable tasks with acceptance criteria and dependency ordering. Use when you have a spec and need implementable units.
Uses power tools
Uses Bash, Write, or Edit tools
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Sign in to claimnpx claudepluginhub p/v1truv1us-ai-eng-core-plugins-ai-eng-coreBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
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.
Content optimization, SEO, and communication tools
Infrastructure, deployment, and DevOps automation
Curated collection of engineering tools, agents, and workflows. Comprehensive system for AI-assisted software engineering and DevOps.
Meta-tooling for creating plugins, agents, commands, and skills
Learning workflows for knowledge mapping, decisions, quality gates, and maintenance reviews
Persistent file-based planning for AI coding agents. Crash-proof markdown plans (task_plan.md, findings.md, progress.md) that survive context loss and /clear, with an opt-in completion gate and multi-agent shared state. Manus-style. Works with Claude Code, Codex CLI, Cursor, Kiro, OpenCode and 60+ agents via the SKILL.md standard. Includes Arabic, German, Spanish, and Chinese (Simplified and Traditional).
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
v9.52.0 - Reliability wave: tangle contextual review correction loop with hard round ceiling, progress-supervised review rounds (per-agent stall watch, descendant-tree kills), council diversity and agy pin fixes, marketplace generator source-of-truth fix, provider troubleshooting runbook and cost-expectations docs. Run /octo:setup.
Supergraph enforces a complete, evidence-based coding pipeline — scan → plan → TDD → fix → verify → review — grounded in real codebase analysis at every step. It combines AST dependency graphs, LSP-level code intelligence, and a structured skill chain so Claude never guesses about impact before making a change.
Claude harness - A harness for solo developers (Vibecoders) to handle full-cycle contract development.
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.