Ai Toolkit
Plugin ActiveEngineering discipline layer for Claude Code — 5 workflows, 69 commands, 21 rules, 29 skills, 9 agents organized in 12 packs
To provide a workflow-first engineering discipline layer for AI coding tools, enabling end-to-end problem-solving across the software development lifecycle.
Features
- Workflow-based command structure
- Organized skills and agents in themed packs
- Automated project setup and scaffolding
- Integrated debugging and testing workflows
- Guidance on infrastructure and design patterns
Use Cases
- Developing new features from concept to deployment
- Debugging production issues with structured investigation
- Researching market opportunities and validating ideas
- Setting up new projects with best practices
- Auditing code quality and security configurations
Non-Goals
- Replacing core IDE functionality
- Providing low-level code editing
- Acting as a standalone AI assistant without context
Scope
- warning:Tool surface sizeThe plugin exposes a very large number of commands (69) and packs (12), which could potentially lead to scope ambiguity or trigger precision issues.
Invocation
- info:Overlapping near-synonym toolsWhile commands are generally descriptive, the sheer number of commands (69) may lead to some overlap in function, requiring careful selection by the user or the routing agent.
- info:Name collisionsThe plugin uses a consistent '/spartan:' prefix for all commands, mitigating naming collisions. However, the sheer volume of commands might lead to user confusion about the best command to choose.
Execution
- info:Pinned dependenciesWhile dependencies are not explicitly pinned with lockfiles in the provided snippets, the installation via npx typically handles dependency resolution. The focus is on external MCPs rather than internal JS package management.
Installation
First, add the marketplace
/plugin marketplace add spartan-stratos/spartan-ai-toolkit/plugin install toolkit@spartan-marketplaceContains 9 extensions
Skill (9)
CI/CD pipeline patterns for GitHub Actions, PR automation, and deployment workflows. Use when setting up CI, fixing broken pipelines, automating PR checks, or configuring deployment.
Audit JS/TS projects against NPM Security Guidelines covering project setup, dependency hygiene, CI/CD pipeline, Dependabot, and incident response. Use when reviewing package.json or lockfiles, adding or upgrading npm dependencies, setting up CI security gates, hardening a new repo, or responding to a compromised package.
Investigate a monitoring alert end-to-end. Pulls metrics, logs, traces, and recent code changes to identify root cause. Works with any monitoring MCP.
Create a structured on-call log by pulling alerts from monitoring and writing a summary to your team's wiki. Requires a monitoring MCP (recommended: Datadog) and a wiki MCP (Confluence or Notion).
Creates FastAPI endpoints with layered architecture (Router → Service → Repository). Use when creating new API endpoints, CRUD operations, or scaffolding a new domain module in a FastAPI project.
Python/FastAPI coding standards including async patterns, Pydantic v2, SQLAlchemy 2.0, and project structure. Use when writing Python code, reviewing FastAPI projects, or learning FastAPI conventions.
Testing patterns for FastAPI with pytest-asyncio, httpx AsyncClient, fixtures, and test data factories. Use when writing tests, setting up test infrastructure, or improving coverage in a FastAPI project.
Structured debugging runbook for backend services. Use when investigating production issues, API errors, performance problems, or when something broke and you need to find why.
UI/UX design intelligence with searchable style, palette, typography, and chart databases. Use when designing UI components, choosing colors/fonts, reviewing code for UX issues, building landing pages, or implementing responsive layouts.
Quality Score
Trust Signals
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