dev-team

Plans, builds, tests, secures, and operates full-stack software and trained ML models.

Updated Sep 13, 2026
One-click install
npx skills add https://github.com/abdulazeezoj/monovella-poc --skill dev-team-abdulazeezoj
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dev-team
Source: https://github.com/abdulazeezoj/monovella-poc/tree/main/.agents/skills/dev-team
Command: npx skills add https://github.com/abdulazeezoj/monovella-poc --skill dev-team-abdulazeezoj

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? It closes the gap between code that runs locally and software that survives production by enforcing verification, security, testing, and operational discipline across every engineering task. ## Core Features & Use Cases - Full-lifecycle engineering commands: /research, /plan, /build, /fix, /debug, /review, /deploy, and /operate cover the entire software lifecycle from investigation to production runbooks. - AI/ML rigor: Covers LLM/agentic systems (prompt injection defense, eval gates, cost-effective model selection) and trained models (data leakage checks, subgroup evaluation, drift monitoring, rollback). - Verified frontend delivery: Builds UI in slices with screenshots at phone, tablet, and desktop widths, accessibility checks, and end-to-end tests before handoff. - Use Case: Ask it to build a new checkout screen: it inspects the repo, plans the slice, implements frontend and backend together, screenshots the result at real sizes, runs tests, and reports exactly what was verified. ## Quick Start Ask the agent to build, fix, debug, review, or deploy a specific feature or issue in your repository, for example by saying "build the settings screen with tests and screenshots".

Frequently Asked Questions about dev-team

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

FAQPage Schema
How do I build a full-stack feature with an AI coding agent?

Use the /build command, which plans, implements, tests, and documents a complete vertical slice covering both frontend and backend. For screens it includes screenshots at real device sizes and passing end-to-end tests before handoff.

How do I debug a production issue systematically?

Use the /debug command, which gathers evidence first (logs, repro steps, stack traces, recent diffs) and confirms the root cause before patching. This avoids hiding bugs behind plausible-looking but unconfirmed fixes.

What stack does this skill recommend for new projects?

Defaults include Next.js for web, Expo for mobile, FastAPI or Next.js for backends, Postgres for data, and self-hosted Docker infrastructure. These are defaults for new decisions only, never a mandate to migrate existing working stacks.

Does it handle LLM prompt injection and AI security?

Yes, the ai-engineering reference covers defense-in-depth against prompt injection, least-privilege tool scoping, output validation, and access control before retrieval in RAG systems. Model output is always treated as untrusted input.

When should I fine-tune a model instead of prompting?

Fine-tune when the task needs behavior a prompt cannot reliably produce, such as narrow high-volume classification or domain-specific style. Prompt or use RAG when the task benefits from frontier model reasoning and volume does not justify training infrastructure.

What are the limitations of this engineering workflow?

It requires an environment where commands, tests, and browsers can actually run, since it never claims unverified work as done. When credentials or test environments are missing, it states what was not verified rather than working around the gap.