CCteam-creator
Multi-agent team orchestration skill for Claude Code.
One skill, a full engineering team. CCteam-creator turns a single Claude Code session into a coordinated team of 2-6 AI agents — with built-in CI enforcement, code review, doc-code sync, and a taste feedback loop that encodes your preferences into automated checks. Human steers, agents execute.
English | 中文
Standing on the Shoulders of Giants
CCteam-creator is built upon outstanding open-source projects and engineering practices:
| Source | What We Learned |
|---|
| planning-with-files | Manus-style persistent markdown planning — the 3-file pattern (task_plan.md / findings.md / progress.md) that survives context compression. The "context window = RAM, file system = disk" philosophy. |
| everything-claude-code | Agent harness optimization by Anthropic hackathon winner. 13 expert agents, 40+ skills. Inspired our role-based agent design and skill structure. |
| mattpocock/skills | TDD vertical-slice philosophy, "design it twice" parallel sub-agent pattern, interface durability principles, and plan stress-testing methodology. |
| OpenAI Harness Engineering | The discipline of designing constraints, feedback loops, and documentation systems that make AI agents reliable at scale. Inspired our docs/ knowledge base, invariant-driven review, Doc-Code Sync, failure-to-guardrail loop, and anti-bloat principles. |
| Anthropic Harness Design | Anthropic Labs' research on multi-agent architectures for long-running autonomous coding. Three key lessons absorbed into CCteam-creator: (1) Evaluator calibration — out-of-the-box LLMs are poor QA agents that rationalize away issues; the fix is few-shot calibration anchors with concrete STRONG/WEAK examples, which shaped our Review Dimensions system. (2) Every harness component is an assumption — each mechanism encodes a belief about what the model can't do alone, and these assumptions go stale as models improve; this became our Assumption Audit checklist. (3) Generator-evaluator separation — separating the agent doing the work from the agent judging it is more tractable than making a generator self-critical, validating our existing dev/reviewer split and motivating the anti-leniency rule. |
What It Does
CCteam-creator sets up parallel AI agent teams in Claude Code. Instead of a single AI assistant, you orchestrate multiple specialized agents — developers, researchers, testers, reviewers — working together on your project.
When invoked, CCteam-creator:
- Consults with you — explains how agent teams work, understands your project, recommends a team
- Sets up everything — planning files, docs/ knowledge base, CLAUDE.md operations guide, agent onboarding
- Manages collaboration — agents communicate directly, persist state to files, follow built-in protocols
How It Works — Full Lifecycle
Here's a complete walkthrough of how CCteam-creator operates, from first invocation to project completion and session resume.
Phase 1: Setup (First Session)
You: "Set up a team for my e-commerce project"
┌─ Step 1: Consultation ──────────────────────────────────┐
│ team-lead (Claude) asks about: │
│ - Project goals and deliverables │
│ - Task type (software, research, etc.) │
│ - Current state (greenfield or existing code) │
│ - Quality priorities → become Review Dimensions │
│ team-lead recommends: backend-dev + frontend-dev + │
│ researcher + reviewer (4 agents) │
└─────────────────────────────────────────────────────────┘
↓ user confirms
┌─ Step 2-3: File Creation ───────────────────────────────┐
│ Creates .plans/ecommerce/ with: │
│ task_plan.md, decisions.md, docs/, per-agent dirs │
│ Generates CLAUDE.md (always in context, survives │
│ compressions — the team's persistent memory) │
└─────────────────────────────────────────────────────────┘
↓
┌─ Step 4: Spawn & Snapshot ──────────────────────────────┐
│ Spawns all agents in parallel with onboarding prompts │
│ Saves team-snapshot.md (complete onboarding prompts │
│ + skill file timestamps → enables fast resume) │
└─────────────────────────────────────────────────────────┘
Phase 2: Collaboration (Working Session)