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Agent Team Dev

Community
YuxiaoWang-520
agent-team-dev

Coordinate coding tasks with a lean sub-agent team (max 4 sub-agents) using explicit role ownership and strict quality gates. Use when tasks need parallel design/implementation/testing/review handoffs with correctness as the top priority, efficiency second, and token optimization third.

Overview

PublisherYuxiaoWang-520
Repositoryharness-craft
Skill nameagent-team-dev
Stars
73
Forks
5
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by YuxiaoWang-520 on GitHub. Read the source before you install it.

Installation

Install the Agent Team Dev AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/YuxiaoWang-520/harness-craft/tree/main/skills/agent-team-dev
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/YuxiaoWang-520/harness-craft.git /tmp/harness-craft
mkdir -p .claude/skills
cp -r /tmp/harness-craft/skills/agent-team-dev .claude/skills/agent-team-dev
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Team Dev in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Team Dev on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Team Dev is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent Team Dev

Run coding as a compact accountable team: one Team Lead in main thread, plus up to 4 sub-agents.

Priority order for all decisions:

  1. Correctness and requirement fulfillment.
  2. Execution efficiency and cycle time.
  3. Token/cost optimization (only when it does not reduce 1 or 2).

Team Topology

Keep the main thread as Team Lead. Add up to 4 sub-agents from the roles below.

RoleAgent TypeOwnershipOutput
Team Lead (main thread)n/aTask contract, delegation plan, integration, final verificationIntegrated solution and final report
Solution ArchitectexplorerRead-only analysisDesign brief, file impact map, execution plan
Feature EngineerworkerProduction code files onlyMinimal patch implementing behavior
Test EngineerworkerTests, fixtures, test config onlyFailing-then-passing tests and test notes
Reviewer/VerifierexplorerRead-only reviewRisk-ranked findings and release recommendation

Real-World Problems This Skill Solves

  • Scope drift across agents: fix with task contract + explicit non-goals.
  • Merge conflicts from overlapping edits: fix with disjoint file ownership.
  • Quality regressions from rushed parallel work: fix with staged verification and independent review.
  • Slow progress from over-delegation: fix with mode-based staffing and round limits.
  • Unnecessary token waste from repeated analysis: fix with one canonical architecture brief and short delta updates.

Activation Checklist

  1. Write a task contract before delegation.
  2. Choose the collaboration mode that best protects correctness for current risk.
  3. Assign disjoint file ownership per sub-agent.
  4. Define stop conditions (round cap, no-op cutoff, escalation triggers).
  5. Spawn only sidecar work in parallel; keep immediate blockers in main thread.
  6. Enforce quality gates before final delivery.

Task Contract Format

Create this in the main thread first:

markdown
Objective:
Non-goals:
Constraints:
Definition of done:
Target files/modules:
Verification commands:
Risk level: low | medium | high
Collaboration mode:
Round cap:
Output budget:

Collaboration Modes (Token-Aware)

  • Mode A: Fast Patch (0-1 sub-agents): small low-risk change, clear implementation path.
  • Mode B: Build + Test (2 sub-agents): behavior + tests can run in parallel.
  • Mode C: Full Safety (3-4 sub-agents): high-risk change needs architecture and independent review.
  • Prefer the smallest mode that still protects correctness.
  • Escalate mode immediately when correctness risk is non-trivial.
  • Never exceed 4 active sub-agents.

Delegation Protocol

Send every sub-agent a scoped work packet:

markdown
Role:
Objective:
Owned files/directories:
Do not edit:
Inputs:
Deliverables:
Validation to run:
Output budget:

For all worker roles, include this sentence:

text
You are not alone in the codebase. Do not revert edits you did not make; adapt to existing changes.

Output Contract (Keep Responses Compact)

  • Solution Architect: <=12 bullets, include file impact map and top 3 risks only.
  • Feature Engineer: changed file list + <=8 bullets + commands run.
  • Test Engineer: changed test list + covered scenarios + pass/fail.
  • Reviewer/Verifier: findings by severity, max 10 items total, include file references.
  • Keep reports concise, but include all details required to validate correctness.

Execution Sequence

  1. Run architecture pass (if needed) with Solution Architect.
  2. Run Feature Engineer and Test Engineer in parallel with non-overlapping write scopes.
  3. Integrate patches in main thread and resolve conflicts centrally.
  4. Run Reviewer/Verifier after integration for an independent pass.
  5. Execute verification commands in main thread and close unresolved findings.
  6. Stop when quality gates pass or escalate if blocked.

Round policy:

  • Default round cap: 2 delegation rounds.
  • Allow a 3rd round only for unresolved high-severity issues.
  • If two consecutive sub-agent turns produce no actionable delta, stop delegating and continue in main thread.
  • Override round caps whenever additional iteration is required to satisfy quality gates.

Boundary Enforcement

  • Enforce file-level ownership before spawn and before merge.
  • Reject and re-scope any patch that edits files outside ownership.
  • Keep integration centralized in Team Lead; sub-agents do not self-merge each other.
  • Use Do not edit paths for shared infrastructure files unless explicitly assigned.

Quality Gates

Do not mark complete until all gates pass:

  1. Behavior gate: implementation satisfies task contract.
  2. Test gate: relevant tests pass; new behavior has coverage.
  3. Regression gate: no unrelated breakage in touched flows.
  4. Review gate: Reviewer/Verifier has no unaddressed high-severity findings.
  5. Evidence gate: final report includes changed files, commands run, and residual risks.

Token and Throughput Guardrails

  • These guardrails are secondary to correctness and required validation.
  • Reuse prior artifacts; do not ask multiple agents to rediscover the same context.
  • Use delta-first handoffs: only what changed since last checkpoint.
  • Prefer one strong scoped prompt over multiple iterative clarifications.
  • Use wait_agent only when the main thread is blocked on that output.
  • Reuse existing sub-agent threads for follow-ups; avoid unnecessary respawns.
  • Close idle agents once their output is integrated.
  • Skip architecture/review roles on trivial low-risk edits where they add no value.

Escalation Rules

  • Escalate to user only for missing credentials, destructive irreversible operations, legal/policy risk, or contradictory requirements.
  • If assumptions are needed, choose the safest reversible option and document it.
  • If sub-agent outputs conflict, Team Lead reconciles and keeps a single source of truth in main thread.

Anti-Patterns

  • Do not let multiple sub-agents edit the same files unless explicitly planned.
  • Do not parallelize tasks when the second task depends on the first task's result.
  • Do not skip independent review on high-risk changes.
  • Do not keep agents running without clear ownership or pending work.
  • Do not request full restatements of repo context in every handoff.

Reference

Read references/operating-contract.md for role-specific prompt templates, severity rubric, and a full runbook.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Team Dev AI skill do?

Coordinate coding tasks with a lean sub-agent team (max 4 sub-agents) using explicit role ownership and strict quality gates. Use when tasks need parallel design/implementation/testing/review handoffs with correctness as the top priority, efficiency second, and token optimization third.

Why use Agent Team Dev on TypingMind?

Because you install it once and use it with any model. Agent Team Dev is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Team Dev in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/YuxiaoWang-520/harness-craft/tree/main/skills/agent-team-dev. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Team Dev?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Team Dev?

As many as you like. As long as a model supports skills, you can use Agent Team Dev with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Team Dev AI skill free?

It is published on GitHub by YuxiaoWang-520. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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