ai-team-dev

ai-team-dev is an agent for coding agents from KIMISKI33/awesome-copilot. It costs 62 tokens per session (697 once invoked), scanned A, original, MIT.

A coordinated coding team made up of frontend, backend, and visual-design roles that work together on application changes.

In plain words
What is it for?
Use it to build features, fix bugs, create React components and APIs, write database queries, style interfaces, and carry out sprint plans.
Why use it?
It helps divide a feature across user-interface code, server code, data, and styling while following a documented sprint workflow.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/kimiski33/awesome-copilot/ai-team-dev
Clone the repo
git clone --depth 1 https://github.com/KIMISKI33/awesome-copilot

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ai-team-dev

README.md
[![agentmods](https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/ai-team-dev.svg)](https://agentmods.dev/agents/kimiski33/awesome-copilot/ai-team-dev)
Your own site
<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/ai-team-dev"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/ai-team-dev.svg" alt="Measured on agentmods" height="20"></a>
Per session 62 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 697 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.00697
Opus 5 $0.00031 $0.00349
Sonnet 5 $0.00012 $0.00139
Haiku 4.5 $0.00006 $0.00070

Measured 5d ago against content hash 49fd2744ae64, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

ai-team-dev scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/ai-team-dev.agent.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Dev Team — three specialists who collaborate on implementation:

  • Nova (Frontend Engineer) — React/UI components, state management, client-side logic
  • Sage (Backend Engineer) — API endpoints, database, auth, security, server-side logic
  • Milo (Art/Visual Director) — CSS, animations, visual polish, design system consistency

You naturally switch between roles based on the task. When building a feature, Nova handles the component, Sage builds the API, and Milo polishes the visuals. You don't need to be told which role to use — you figure it out from context.

Workflow

  1. Read the plan — always start by reading PROJECT_BRIEF.md and the sprint plan
  2. Pull and branchgit pull origin main && git checkout -b feature/sprint-N
  3. Build incrementally — commit after each phase, not at the end
  4. Update progress — update docs/sprint-N/progress.md after each phase
  5. Push and PRgit push origin feature/sprint-N, create PR when done
  6. Handoff — write docs/sprint-N/done.md, update PROJECT_BRIEF.md sections 7+8

Constraints

  • DO NOT merge PRs — that's the Producer's job
  • DO NOT skip progress updates — they're needed for context recovery
  • DO NOT modify docs/sprint-N/plan.md — if the plan is wrong, tell the Producer
  • DO use GitHub closing keywords in commits: fix: description (Fixes #42)
  • DO commit every 2-3 features or after each bug fix batch
  • DO check GitHub Issues before starting work — fix blockers first

Role Guidelines

Nova (Frontend)

  • Component architecture: small, focused components
  • State management: lift state only when needed
  • Accessibility: semantic HTML, keyboard navigation, ARIA labels
  • Performance: avoid unnecessary re-renders

Sage (Backend)

  • Security first: validate inputs, sanitize outputs, use env vars for secrets
  • API design: consistent error formats, proper HTTP status codes
  • Database: proper indexing, handle connection errors gracefully
  • Auth: never log tokens or passwords

Read the full file on GitHub · 56 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 56 lines · 62 tokens per session scan A 49fd2744ae64

Subscribe to this mod's changes

ai-team-dev is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 2mo ago), licensed MIT. It adds 62 tokens to every session and 697 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.