Verified:GitHub

sdlc-practices

Coordinates AI agent teams through SDLC planning, review, and gated merges with dedicated worktrees and peer-review cycles.

Updated Apr 12, 2026

Search Tags:#branching #sdlc #code-review #ai-agents #design-process #team-workflow

Ready to run

npx skills add https://github.com/lygav/vladi-plugins-marketplace --skill sdlc-practices

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sdlc-practices
Source: https://github.com/lygav/vladi-plugins-marketplace/tree/main/plugins/sdlc-practices/skills/sdlc-practices
Command: npx skills add https://github.com/lygav/vladi-plugins-marketplace --skill sdlc-practices

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides procedural knowledge for coordinating AI agent teams following strict software development lifecycle practices. It captures battle-tested rules to prevent common failure modes such as branch contamination, missing review gates, scope creep, and disjoint coordination across layers.

Core Features & Use Cases

SDLC discipline for AI agent orchestration, including dedicated feature branches, task decomposition, and gated reviews.
Guidance for planning work packages, performing implementation scouting, and dispatching targeted agents to maintain scope and quality.
Design-to-implementation bridge with checkpoint checks and repeatable reviewer cycles to ensure alignment with design docs.

Quick Start

Follow the SDLC checklist to plan implementation and run a scoped scouting session.

Frequently Asked Questions about sdlc-practices

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I coordinate AI agent teams for software development without branch contamination?▼

Coordinate AI agent teams by enforcing SDLC discipline with dedicated feature branches and isolated worktrees. This prevents branch contamination and ensures scoped, gated development across project layers.

What is the best way to enforce code review gates in a multi-agent development workflow?▼

Enforce code review gates in a multi-agent workflow by mandating explicit prompt scopes and establishing repeatable peer-review cycles. This ensures alignment with design documents before gated merges occur.

How do I plan and dispatch work packages to AI agents for implementation scouting?▼

Plan and dispatch work packages to AI agents by following a structured SDLC checklist. This involves decomposing tasks, defining explicit scope boundaries, and dispatching targeted agents for implementation scouting.

Does my team workflow need design-to-implementation checkpoints for AI-generated code?▼

Yes, your team workflow needs design-to-implementation checkpoints for AI-generated code to prevent scope creep and disjoint coordination. These checkpoints bridge design docs with implementation through repeatable reviewer cycles.

Can I use an SDLC process to manage scope creep when orchestrating multiple AI agents?▼

Yes, you can use an SDLC process to manage scope creep when orchestrating multiple AI agents. Mandating explicit scope in prompts and enforcing task decomposition keeps agents aligned with assigned work packages.

What are the limitations of using ad-hoc team workflows instead of a disciplined SDLC process for AI agents?▼

Ad-hoc team workflows for AI agents lack branch isolation, explicit scoping, and review gates, leading to disjoint coordination and missing review cycles. Without a disciplined SDLC process, multi-agent development risks branch contamination and scope creep.