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By SteveGJones
Delegate SDLC security workflows to AI agents that generate compliance reports with metrics visualizations and GitHub/Jira integrations, perform multi-jurisdiction privacy assessments like GDPR/CCPA, design behavioral enforcement strategies for team adoption, and architect zero-trust systems with threat modeling.
npx claudepluginhub stevegjones/ai-first-sdlc-practices --plugin sdlc-team-securityExpert in SDLC compliance reporting, metrics visualization, remediation tracking, and audit-ready documentation. Use for creating actionable reports tailored to different audiences (team, executive, auditor) and tracking compliance trends.
Expert in GDPR, CCPA/CPRA, LGPD, PIPL, and privacy-by-design. Use for privacy impact assessments, data subject rights implementation, consent management, data minimization strategies, and multi-jurisdiction privacy compliance.
Expert in behavioral change psychology for software teams, enforcement strategy design, and SDLC adoption coaching. Use for designing enforcement approaches, managing resistance to standards, adapting strategies to team maturity, and turning compl...
Expert in security architecture design, threat modeling, zero-trust principles, and secure SDLC integration. Use for architectural security reviews, compliance framework guidance, threat modeling sessions, and security strategy development.
Uses power tools
Uses Bash, Write, or Edit tools
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Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
AI-First SDLC — zero-debt development with validators, enforcement, and workflows
Engineering process for solo founders and teams up to 50 engineers. Agents do architecture, code review, QA, and security. You make two decisions per feature.
Universal quality control orchestrator and final authority for any software development project. Dynamically discovers and coordinates with available sub-agents, performs comprehensive multi-dimensional quality assessment, security validation, and deployment readiness verification. Adapts to any project type, programming language, or development framework while maintaining enterprise-grade quality standards. Examples: <example>Context: Code changes ready for review across any project. user: 'Please review this code before commit' assistant: 'I'll use the 1-ceo-quality-control-agent to orchestrate comprehensive quality validation, discover available specialists, and perform final security scanning before approval.' <commentary>Universal quality control requires comprehensive validation across all dimensions regardless of project type.</commentary></example> <example>Context: Multi-agent work completion needing validation. user: 'Several agents completed their tasks, need quality review' assistant: 'Let me engage the 1-ceo-quality-control-agent to coordinate comprehensive validation across all completed work and ensure quality standards.' <commentary>Multi-agent coordination and quality validation applies to any development project.</commentary></example>
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.
AI-powered secure development workflow for healthcare software. Implements RePPITS (Research, Propose, Plan, Implement, Test, Secure) with HIPAA, SOC2, and HITRUST compliance gates that run against your diff before commit. Slash commands: /reppit, /research-codebase, /make-proposals, /make-plan, /implement, /review-code, /secure.
Regulatory compliance verification for GDPR, SOC2, and HIPAA
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
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