By v1truv1us
Conduct deep research across codebases, tickets, and documents using graph-based retrieval and Jira investigation; capture knowledge as structured artifacts and knowledge graphs; and generate comprehensive documentation for AI engineering projects.
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
Write concise documentation pages in required format.
Analyze codebases and generate comprehensive technical documentation.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
Enhance any content type using research-backed techniques. Optimize AI prompts with step-by-step approval, improve code quality, refine database queries, enhance documentation, optimize commit messages, and improve communication. Wraps incentive-prompting skill with content-type detection.
Relationship-aware retrieval using graph traversal, entity anchors, community expansion, and hybrid vector plus graph search. Use when chunk similarity alone misses paths, entities, or subsystem context.
Knowledge graph development for LLM applications. Graph storage selection, graph algorithms, extraction from documents/code/websites, entity extraction, Graph RAG, and visualization. Use when building knowledge graphs, graph databases, or LLM graph applications.
Long-running Jira investigation that self-continues past natural stops until a written hypothesis with evidence citations is produced. Pairs with the cooking Stop hook and hard-stops at 10 iterations.
Document solved problems with context, examples, and gotchas so learnings compound for the team. Use after completing a workflow worth remembering.
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.
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
Core workflow: plan, work, review cycle with research and context engineering
npx claudepluginhub p/v1truv1us-ai-eng-research-plugins-ai-eng-researchv9.52.0 - 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.
Comprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
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.
AI-powered development tools for code review, research, design, and workflow automation.
Build and maintain an LLM-curated personal knowledge base in your project — Andrej Karpathy's LLM Wiki pattern, designed to scale to thousands of pages without becoming a context bottleneck. Now with an optional compiled graph layer for typed, provenance-backed relationships.