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team-builder

Interactive agent picker for composing and dispatching parallel teams. Use when composing and dispatching a parallel team of agents for a task.

team-builder 是什么?

team-builder is a Claude Code agent skill that interactive agent picker for composing and dispatching parallel teams. Use when composing and dispatching a parallel team of agents for a task.

兼容平台Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/team-builder

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Team Builder

Interactive menu for browsing and composing agent teams on demand. Works with flat or domain-subdirectory agent collections.

When to Use

  • You have multiple agent personas (markdown files) and want to pick which ones to use for a task
  • You want to compose an ad-hoc team from different domains (e.g., Security + SEO + Architecture)
  • You want to browse what agents are available before deciding

Prerequisites

Agent files must be markdown files containing a persona prompt (identity, rules, workflow, deliverables). The first # Heading is used as the agent name and the first paragraph as the description.

Both flat and subdirectory layouts are supported:

Subdirectory layout — domain is inferred from the folder name:

agents/
├── engineering/
│   ├── security-engineer.md
│   └── software-architect.md
├── marketing/
│   └── seo-specialist.md
└── sales/
    └── discovery-coach.md

Flat layout — domain inferred from shared filename prefixes. A prefix counts as a domain when 2+ files share it. Files with unique prefixes go to "General". Note: the algorithm splits at the first -, so multi-word domains (e.g., product-management) should use the subdirectory layout instead:

agents/
├── engineering-security-engineer.md
├── engineering-software-architect.md
├── marketing-seo-specialist.md
├── marketing-content-strategist.md
├── sales-discovery-coach.md
└── sales-outbound-strategist.md

Configuration

Agents are discovered via two methods, merged and deduplicated by agent name:

  1. claude agents command (primary) — run claude agents to get all agents known to the CLI, including user agents, plugin agents (e.g. ecc:architect), and built-in agents. This automatically covers ECC marketplace installs without any path configuration.
  2. File glob (fallback, for reading agent content) — agent markdown files are read from:
    • ./agents/**/*.md + ./agents/*.md — project-local agents
    • ~/.claude/agents/**/*.md + ~/.claude/agents/*.md — global user agents

Earlier sources take precedence when names collide: user agents > plugin agents > built-in agents. A custom path can be used instead if the user specifies one.

How It Works

Step 1: Discover Available Agents

Run claude agents to get the full agent list. Parse each line:

  • Plugin agents are prefixed with plugin-name: (e.g., ecc:security-reviewer). Use the part after : as the agent name and the plugin name as the domain.
  • User agents have no prefix. Read the corresponding markdown file from ~/.claude/agents/ or ./agents/ to extract the name and description.
  • Built-in agents (e.g., Explore, Plan) are skipped unless the user explicitly asks to include them.

For user agents loaded from markdown files:

  • Subdirectory layout: extract the domain from the parent folder name
  • Flat layout: collect all filename prefixes (text before the first -). A prefix qualifies as a domain only if it appears in 2 or more filenames (e.g., engineering-security-engineer.md and engineering-software-architect.md both start with engineering → Engineering domain). Files with unique prefixes (e.g., code-reviewer.md, tdd-guide.md) are grouped under "General"
  • Extract the agent name from the first # Heading. If no heading is found, derive the name from the filename (strip .md, replace hyphens with spaces, title-case)
  • Extract a one-line summary from the first paragraph after the heading

If no agents are found after running claude agents and probing file locations, inform the user: "No agents found. Run claude agents to verify your setup." Then stop.

Step 2: Present Domain Menu

Available agent domains:
1. Engineering — Software Architect, Security Engineer
2. Marketing — SEO Specialist
3. Sales — Discovery Coach, Outbound Strategist

Pick domains or name specific agents (e.g., "1,3" or "security + seo"):
  • Skip domains with zero agents (empty directories)
  • Show agent count per domain

Step 3: Handle Selection

Accept flexible input:

  • Numbers: "1,3" selects all agents from Engineering and Sales
  • Names: "security + seo" fuzzy-matches against discovered agents
  • "all from engineering" selects every agent in that domain

If more than 5 agents are selected, list them alphabetically and ask the user to narrow down: "You selected N agents (max 5). Pick which to keep, or say 'first 5' to use the first five alphabetically."

Confirm selection:

Selected: Security Engineer + SEO Specialist
What should they work on? (describe the task):

Step 4: Spawn Agents in Parallel

  1. Read each selected agent's markdown file
  2. Prompt for the task description if not already provided
  3. Spawn all agents in parallel using the Agent tool:
    • subagent_type: "general-purpose"
    • prompt: "{agent file content}\n\nTask: {task description}"
    • Each agent runs independently — no inter-agent communication needed
  4. If an agent fails (error, timeout, or empty output), note the failure inline (e.g., "Security Engineer: failed — [reason]") and continue with results from agents that succeeded

Step 5: Synthesize Results

Collect all outputs and present a unified report:

  • Results grouped by agent
  • Synthesis section highlighting:
    • Agreements across agents
    • Conflicts or tensions between recommendations
    • Recommended next steps

If only 1 agent was selected, skip synthesis and present the output directly.

Rules

  • Dynamic discovery only. Never hardcode agent lists. New files in the directory auto-appear in the menu.
  • Max 5 agents per team. More than 5 produces diminishing returns and excessive token usage. Enforce at selection time.
  • Parallel dispatch. All agents run simultaneously — use the Agent tool's parallel invocation pattern.
  • Parallel Agent calls, not TeamCreate. This skill uses parallel Agent tool calls for independent work. TeamCreate (a Claude Code tool for multi-agent dialogue) is only needed when agents must debate or respond to each other.

Examples

User: team builder

Claude:
Available agent domains:
1. Engineering (2) — Software Architect, Security Engineer
2. Marketing (1) — SEO Specialist
3. Sales (4) — Discovery Coach, Outbound Strategist, Proposal Strategist, Sales Engineer
4. Support (1) — Executive Summary

Pick domains or name specific agents:

User: security + seo

Claude:
Selected: Security Engineer + SEO Specialist
What should they work on?

User: Review my Next.js e-commerce site before launch

[Both agents spawn in parallel, each applying their specialty to the codebase]

Claude:
## Security Engineer Findings
- [findings...]

## SEO Specialist Findings
- [findings...]

## Synthesis
Both agents agree on: [...]
Tension: Security recommends CSP that blocks inline styles, SEO needs inline schema markup. Resolution: [...]
Next steps: [...]

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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