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By SteveGJones
Delegate SDLC workflows to specialist AI agents that architect cloud-native systems, design databases, conduct deep web research, optimize performance and observability, distill repo knowledge, and build production agents via orchestrated pipelines.
npx claudepluginhub stevegjones/ai-first-sdlc-practices --plugin sdlc-team-commonUnified entry point for agent creation pipeline. Routes web research or repo analysis, then delegates to agent-builder for construction.
Analyzes repositories and knowledge bases to produce synthesis documents for agent creation via RELIC evaluation and artifact discovery.
Builds production agents from research via 6-phase pipeline, archetype selection, and knowledge distillation. Use when creating or rebuilding agents.
Expert in database design, schema modeling, query optimization, HA/DR architecture, and data security. Use for database technology selection, performance tuning, migration planning, and compliance implementation.
Executes systematic web research campaigns from structured prompts, evaluates sources via CRAAP, and produces synthesis documents.
Uses power tools
Uses Bash, Write, or Edit tools
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AI/ML specialist agents — architects, prompt engineers, RAG designers
Complete SDLC framework with 58 specialized agents for software development lifecycle management. Phase-based workflows (Inception→Elaboration→Construction→Transition), security reviews, testing orchestration, and deployment automation.
Plugin de ingeniería de software completa: 10 agentes de núcleo y 9 opcionales con personalidad propia, memoria persistente por proyecto, quality gates y flujos automatizados desde la idea hasta producción.
An engineering team in a box for Claude Code. 12 specialized subagents (planner, fullstack-engineer, refactor-specialist, migration-engineer, frontend-designer, critic, vuln-verifier, debugger, db-expert, onboarder, tool-expert, web-researcher) plus 15 automation hooks (pre-commit secret scan, MCP health tracking, cost tracking, test runner, branch protection, large file warner, session summary, batch format, design quality, config protection, and more) wired by the P7/P9/P10 methodology with three red lines: closure discipline, fact-driven, exhaustiveness.
Self-orchestrating multi-agent development system — 8 specialized AI agents, parallel quality gates, and automated workflows. You say WHAT, the AI decides HOW.
Persona-driven AI development team: orchestrator, team agents, review agents, skills, slash commands, and advisory hooks for Claude Code
AI/ML specialist agents — architects, prompt engineers, RAG designers
Full-stack agents — frontend, backend, API, DevOps architects
AI-First SDLC — zero-debt development with validators, enforcement, and workflows
Python-specific validation, patterns, and expert agents
Cloud infrastructure agents — cloud, container, SRE specialists
Table of Contents
A framework for integrating AI agents as primary developers while maintaining quality and process compliance. Provides specialist agents, validation tools, enforcement rules, and workflow skills for zero-technical-debt development.
Install the plugin family from the Claude Code marketplace. This is the standard approach for using the framework in your projects.
Step 1: Add the marketplace and install the core plugin
/plugin marketplace add SteveGJones/ai-first-sdlc-practices
/plugin install sdlc-core@ai-first-sdlc
Step 2: Configure your team
/sdlc-core:setup-team
This presents project types (Full-stack, AI/ML, Cloud, API, Security, Custom) and installs the matching team plugins. For example, a full-stack web app installs:
| Plugin | Description |
|---|---|
sdlc-core | Rules, validators, enforcement, workflows (always installed) |
sdlc-team-common | Solution architect, research agent, performance engineer, database architect |
sdlc-team-fullstack | Frontend, backend, API, DevOps architects |
Step 3: Start working
/sdlc-core:new-feature 1 my-feature "Description of the feature"
| Plugin | Description |
|---|---|
sdlc-core | Rules, validators, enforcement, workflows (always install) |
sdlc-team-common | Cross-cutting architects, researchers, performance engineers |
sdlc-team-ai | AI/ML specialists — architects, prompt engineers, RAG designers |
sdlc-team-fullstack | Frontend, backend, API, DevOps architects |
sdlc-team-cloud | Cloud, container, SRE specialists |
sdlc-team-security | Security, compliance, privacy specialists |
sdlc-team-pm | Agile coach, delivery manager, progress tracking |
sdlc-team-docs | Technical writer, documentation architect |
sdlc-lang-python | Python-specific validation, patterns, expert agent |
sdlc-lang-javascript | JavaScript/TypeScript validation and patterns |
| Skill | Description |
|---|---|
/sdlc-core:validate | Run validation pipeline (--syntax, --quick, --pre-push) |
/sdlc-core:new-feature | Create feature proposal, retrospective, and branch |
/sdlc-core:commit | Validated commit with test execution |
/sdlc-core:pr | Full validation + PR creation |
/sdlc-core:setup-team | Configure team formation for your project |
/sdlc-core:setup-ci | Generate GitHub Actions workflow |
/sdlc-core:release-plugin | Package source into plugins |
For testing unreleased agents or contributing to the framework, install agents directly from the repository. This approach gives you access to agents before they're published as plugins.
# Download the setup orchestrator
curl -s https://raw.githubusercontent.com/SteveGJones/ai-first-sdlc-practices/main/agents/v3-setup-orchestrator.md > v3-setup-orchestrator.md
mkdir -p .claude/agents && mv v3-setup-orchestrator.md .claude/agents/
# Restart Claude Code, then:
# "Use the v3-setup-orchestrator agent to set up AI-First SDLC for my project"
The orchestrator interviews you about your project and downloads only the agents you need. Use this approach when:
The original Python script approach. Still functional but superseded by the plugin system.