Team Builder

在 OpenClaw 上一键部署多 Agent SaaS 团队工作区。内置双开发轨（devops 交付 + fullstack-dev 实现）、实时 spawn 调度、cron 巡检、Deep Dive 产品知识目录、onboarding 引导。支持自定义角色、模型、时区，可选 Telegram 接入。

一条命令部署完整多 Agent 团队骨架，包含角色、信箱、看板、产品知识目录、cron 巡检、双开发轨模板。

详细安装步骤和首次配置引导见 README.md（中文完整版）。

前置条件

• OpenClaw 已安装并运行
• Node.js 16+
• 已配置至少一个模型 provider

此技能会修改系统配置：apply-config.js 会写入 openclaw.json，create-crons.* 会创建 cron 任务。均需手动执行，不自动运行。

Internal Dispatch Protocol (MANDATORY)

• Standard agents (product-lead, growth-lead, intel-analyst, devops, etc.): sessions_spawn(runtime="subagent", mode="run") — 禁止带 streamTo
• ACP agents (fullstack-dev): sessions_spawn(runtime="acp") — 可带 streamTo="parent"
• 结果通过 auto-announce 推送；不要轮询 sessions_list 或 subagents list
• chief-of-staff 是群内唯一入口；其他 agent 内部 spawn，不直接监听群聊
• 详见 shared/knowledge/team-workflow.md 零章

参谋长执行边界（MANDATORY）

• 参谋长不下地干活：任何多步骤任务（编码、调研、分析、内容、部署）→ 全部派给对应 agent
• 参谋长不亲自开子代理干活：spawn 子代理执行具体业务 = 等价于自己干活
• 参谋长做且只做：任务拆解（L1-L4 复杂度判断）、编排、派活、监控、结果汇总
• 先执行后汇报；默认输出只保留：已做什么 / 拿到什么结果 / 卡在哪里

子代理使用规则（MANDATORY，全团队适用）

• 优先主 agent 自己干：干得过来时不开子代理
• 分析透再派：开子代理前必须先分析清楚边界/依赖/输入输出
• 子代理只做原子任务：一句话说清、执行完就结束，不做判断或策略决策
• 主 agent 全权负责：判断、策略、经验积累——子代理不做这三件
• 子代理结果由主 agent 汇总后再上报参谋长

Credentials involved

• Telegram bot tokens (optional) -- stored in openclaw.json, used for agent-to-Telegram binding
• Model API keys -- must already be configured in your OpenClaw model providers (not handled by this skill)

Recommended

• Review generated apply-config.js before running
• Check the backup of openclaw.json after running
• Test with 2-3 agents before enabling all cron jobs

Team Architecture

Default reference architecture for a SaaS/growth multi-agent team (customizable to 2-10 agents):

CEO
 |-- Chief of Staff (dispatch + strategy + efficiency)
 |-- Data Analyst (data + user research)
 |-- Growth Lead (GEO + SEO + community + social media)
 |-- Content Chief (strategy + writing + copywriting + i18n)
 |-- Intel Analyst (competitor monitoring + market trends)
 |-- Product Lead (product management + tech architecture)
 |-- DevOps (delivery / deploy / environment / acceptance)
 |-- Fullstack Dev (implementation / module deep dive / ACP coding session)

Multi-Team Support

One OpenClaw instance can run multiple teams:

node <skill-dir>/scripts/deploy.js                  # default team
node <skill-dir>/scripts/deploy.js --team alpha      # named team "alpha"
node <skill-dir>/scripts/deploy.js --team beta       # named team "beta"

Named teams use prefixed agent IDs (alpha-chief-of-staff, beta-growth-lead) to avoid conflicts. Each team gets its own workspace subdirectory.

Flexible Team Size

The wizard lets you select 2-10 agents from the available roles. Skip roles you don't need. The 8-agent default covers most SaaS scenarios with dual-dev routing, but you can run leaner (3-4 agents) or expand with custom roles.

Model Auto-Detection

The wizard scans your openclaw.json for registered model providers and auto-suggests models by role type:

Role Type	Best For	Auto-detect Pattern
Thinking	Strategic roles (chief, growth, content, product)	/glm-5|opus|o1|deepthink/i
Execution	Operational roles (data, intel, fullstack)	/glm-4|sonnet|gpt-4/i
Fast	Lightweight tasks	/flash|haiku|mini/i

You can always override with manual model IDs.

Setup / Config / Scripts

Required Inputs

• Team name
• Workspace dir
• Timezone
• Morning brief hour
• Evening brief hour
• Thinking model
• Execution model
• CEO title

Optional Inputs

• Telegram user ID
• Telegram bot tokens
• Proxy
• ACP coding agent(给 fullstack-dev 使用)

Core Scripts

node <skill-dir>/scripts/deploy.js
node <workspace-dir>/apply-config.js
powershell <workspace-dir>/create-crons.ps1
bash <workspace-dir>/create-crons.sh
openclaw gateway restart

Execution Priority

• First: matched execution skill (for coding work, coding-lead if loaded)
• Second: agent-role fallback when no matching skill is loaded
• Third: templates/README explain boundaries and ownership only; they should not override matched skills

Context File Hygiene

• Active context files live under <project>/.openclaw/
• Reuse one context file per active code chain when possible
• Naming pattern: context-<task-slug>.md
• Active context file cap per project: 60
• Context-file lifecycle window per project: 100 total files across active + archive
• Completed or stale files should be deleted or moved to .openclaw/archive/

Current Dual-Dev Standard

• fullstack-dev:实现、模块深挖、开发文档、接口文档、Claude coding 执行;默认 coding skill 可采用 coding-lead,其中 simple 任务直做,medium 倾向 Claude ACP run 或 direct acpx,complex 通过现有会话连续协作 + 上下文文件推进,不把 ACP session 持久线程作为正式主路径;context 活跃上限 60、生命周期总窗口 100;并行允许但必须先定义边界,总上限 5 个工作单元
• devops:交付、部署、环境、回归、冒烟、自动QA、发布门禁
• product-lead:澄清、PRD、验收标准,不完整不得派工
• chief-of-staff:路由、裁决、控制 token 浪费

部署流程

完整步骤见 README.md。以下是关键参数选取对照表。

必填参数

参数	默认值	说明
teamName	Alpha Team	团队名
workspaceDir	~/.openclaw/workspace-team	工作区路径
timezone	Asia/Shanghai	cron 时区
morningHour	8	晨报时间
eveningHour	18	晚报时间
thinkingModel	自动检测	策略型角色（chief/product/growth/content）
executionModel	自动检测	执行型角色（devops/fullstack/intel/data）
ceoTitle	Boss	CEO 称呼

可选参数

• roles：自定义角色列表（默认全郥 8 个）
• roleNames：自定义角色名称（如中文起名）
• --team <prefix>：多团队并存时用于隔离角色 ID
• Telegram bot tokens（可选，配置后自动写入 openclaw.json）

核心命令

# 交互式生成
node <skill-dir>/scripts/deploy.js

# 非交互生成
node <skill-dir>/scripts/deploy.js --config team-builder.json

# 验证生成结果
node <skill-dir>/scripts/deploy.js --verify --config team-builder.json

# 应用配置（写入 openclaw.json）
node <workspace-dir>/apply-config.js

# 创建 cron
powershell <workspace-dir>/create-crons.ps1  # Windows
bash <workspace-dir>/create-crons.sh          # macOS/Linux

# 重启
openclaw gateway restart

--verify 检查生成物是否包含双开发模型、角色归属、cron 条目。

完整安装步骤见 README.md。

Cron Schedule

Offset	Agent	Task	Frequency
H-1	Data Analyst	Data + user feedback	Daily
H-1	Intel Analyst	Competitor scan	Mon/Wed/Fri
H	Chief of Staff	Morning brief (announced)	Daily
H+1	Growth Lead	GEO + SEO + community	Daily
H+1	Content Chief	Weekly content plan	Monday
H+2	DevOps	Delivery / environment / Deep Dive / acceptance	Daily
H+10	Chief of Staff	Evening brief (announced)	Daily

(H = morning brief hour)

Generated File Structure

<workspace>/
├── AGENTS.md, SOUL.md, USER.md  (auto-injected)
├── apply-config.js, create-crons.ps1/.sh, README.md
├── agents/<8 agent dirs>/       (SOUL.md + MEMORY.md + memory/)
└── shared/
    ├── briefings/, decisions/, inbox/ (v2: with status tracking)
    ├── status/team-dashboard.md     (chief-of-staff maintains, all agents read first)
    ├── data/                        (public data pool, data-analyst writes, all read)
    ├── kanban/, knowledge/
    └── products/
        ├── _index.md                (product matrix overview)
        ├── _template/               (knowledge directory template)
        └── {product}/               (per-product knowledge, up to 20 files)
            ├── overview.md, architecture.md, database.md, api.md, routes.md
            ├── models.md, services.md, frontend.md, auth.md, integrations.md
            ├── jobs-events.md, config-env.md, dependencies.md, devops.md
            ├── test-coverage.md, tech-debt.md, domain-flows.md, data-flow.md
            ├── i18n.md, changelog.md, notes.md

Knowledge Governance

Each shared knowledge file has a designated owner. Only the owner agent updates it; others read only.

File	Owner	Update Trigger
geo-playbook.md	growth-lead	After GEO experiments/discoveries
seo-playbook.md	growth-lead	After SEO experiments
competitor-map.md	intel-analyst	After each competitor scan
content-guidelines.md	content-chief	After proven writing patterns
user-personas.md	data-analyst	After new user insights
tech-standards.md	product-lead	After architecture decisions

Update Protocol

When updating a knowledge file, the owner must:

1. Add a dated entry at the top: ## [YYYY-MM-DD] <what changed>
2. Include the reason and data evidence
3. Never delete existing entries without CEO approval (append, don't replace)

Chief of Staff Governance

The chief-of-staff monitors knowledge file health during weekly reviews:

• Are files being updated regularly?
• Any conflicting information between files?
• Any stale entries that should be archived?

Self-Evolution Pattern

Agents improve their own strategies over time through a feedback loop:

1. Execute task (cron or inbox triggered)
2. Collect results (data, metrics, outcomes)
3. Analyze: what worked vs what didn't
4. Update knowledge files with proven strategies (with evidence)
5. Next execution reads updated knowledge → better performance

This is NOT the agent randomly changing rules. Updates must be:

• Data-driven: backed by metrics or concrete outcomes
• Incremental: append new findings, don't rewrite everything
• Traceable: dated with evidence so others can verify

What Agents Can Self-Update

• Their own knowledge files (per ownership table above)
• Their own MEMORY.md (lessons learned, decisions)
• shared/data/ outputs (data-analyst only)

What Requires CEO Approval

• shared/decisions/active.md (strategy changes)
• Adding/removing agents or changing team architecture
• External publishing or spending decisions

Public Data Layer

The shared/data/ directory serves as a read-only data pool for all agents:

• data-analyst writes: daily metrics, user feedback summaries, anomaly alerts
• All agents read: to inform their own decisions
• Format: structured markdown or JSON, dated filenames (e.g., metrics-2026-03-01.md)
• Retention: keep 30 days, archive older files

Project Deep Dive - Code Scanning

Agents can deeply understand each SaaS product through automated code scanning. This is critical - without deep project knowledge, all team decisions are surface-level.

How It Works

1. CEO adds a product to shared/products/_index.md (name, URL, code directory, tech stack)
2. Product Lead triggers a delivery-oriented Deep Dive scan by dispatching to DevOps (via sessions_spawn if online, inbox as fallback)
3. DevOps enters the project directory (read-only) and generates shared knowledge / delivery-oriented scan outputs
4. Fullstack Dev picks up module-level deep dive or implementation follow-up when needed
5. Knowledge files are generated in shared/products/{product}/
6. All agents consume these files via manifest-based lazy loading (never read all at once)

Manifest-Based Lazy Loading (MANDATORY)

Each product directory includes a manifest.json (~200 tokens) that lists all files with one-line summaries and a taskFileMap mapping task types to relevant files.

Agent workflow:

1. Read _index.md → identify which product
2. Read {product}/manifest.json → see all files + summaries (~200 tokens)
3. Based on taskFileMap or summaries, read only 1-3 relevant files
4. Never read more than 5 product files per session

Why: With 15+ products × 20 files each, full loading = 40K+ tokens per product. Manifest loading = 200 tokens + only what's needed.

DevOps MUST regenerate manifest.json after every delivery-oriented scan (L0-L4). Fullstack Dev updates it when doing module-level follow-up that changes knowledge scope. Template in _template/manifest.json.

Manifest Quality Standards

摘要不能为了省 token 丢掉关键信息。每条摘要须满足:

• 核心文件(database/models/services/routes/integrations):50-130字,列出关键实体名/数量/域名
• 中等文件(auth/frontend/commands/config):30-80字,点明方案和范围
• 轻量文件(changelog/notes/metrics):可以短(<20字)
• taskFileMap:必须覆盖该产品的所有核心业务场景(不少于8个映射)
• codeStats:必须包含文件数、行数、模型数、表数等量化指标

Product Knowledge Directory

Each product gets a knowledge directory with up to 20 files + manifest:

shared/products/{product}/
├── manifest.json        ← **INDEX** (~200 tokens): file list, summaries, taskFileMap
├── overview.md          ← Product positioning (from _index.md)
├── architecture.md      ← System architecture, tech stack, design patterns, layering
├── database.md          ← Full table schema, relationships, indexes, migrations
├── api.md               ← API endpoints, params, auth, versioning
├── routes.md            ← Complete route table (Web + API + Console)
├── models.md            ← ORM relationships, scopes, accessors, observers
├── services.md          ← Business logic, state machines, workflows, validation
├── frontend.md          ← Component tree, page routing, state management
├── auth.md              ← Auth scheme, roles/permissions matrix, OAuth
├── integrations.md      ← Third-party: payment/email/SMS/storage/CDN/analytics
├── jobs-events.md       ← Queue jobs, event listeners, scheduled tasks, notifications
├── config-env.md        ← Environment variables, feature flags, cache strategy
├── dependencies.md      ← Key dependencies, custom packages, vulnerabilities
├── devops.md            ← Deployment, CI/CD, Docker, monitoring, logging
├── test-coverage.md     ← Test strategy, coverage, weak spots
├── tech-debt.md         ← TODO/FIXME/HACK inventory, dead code, complexity hotspots
├── domain-flows.md      ← Core user journeys, domain boundaries, module coupling
├── data-flow.md         ← Data lifecycle: external → import → process → store → output
├── i18n.md              ← Internationalization, language coverage
├── changelog.md         ← Scan diff log (what changed between scans)
└── notes.md             ← Agent discoveries, gotchas, implicit rules

Scan Levels

Level	Scope	When	Output
L0 Snapshot	Surface: directory tree, packages, env	First onboard	architecture, dependencies, config-env
L1 Skeleton	Structure: DB, routes, models, components	First onboard	database, routes, api, models, frontend
L2 Deep Dive	Logic: services, auth, jobs, integrations	On-demand per module	services, auth, jobs-events, integrations, domain-flows, data-flow
L3 Health Check	Quality: tech debt, tests, security	Periodic / pre-release	tech-debt, test-coverage, devops
L4 Incremental	Delta: git diff → update affected files	After code changes	changelog + targeted updates

Content Standards

Knowledge files capture not just WHAT exists but WHY:

• Design decisions: Why this approach was chosen
• Implicit business rules: Logic buried in code (e.g., "orders auto-cancel after 72h")
• Gotchas: What breaks if you touch this module carelessly
• Cross-module coupling: Where changing A silently breaks B
• Performance hotspots: N+1 queries, missing indexes, bottleneck endpoints

Role Responsibilities

Role	Responsibility
Product Lead	Clarification / PRD / acceptance: complete clarification, PRD, user stories, acceptance criteria, and review knowledge freshness before delegating
DevOps	Delivery / QA gate / Deep Dive: enter code directory for deployment-oriented scans, maintain release checklist, smoke/regression testing, auto-QA access, and generate/update shared product knowledge files
Fullstack Dev	Implementation / docs / Deep Dive follow-up: continue module-level deep dive, code analysis, implementation, dev docs, interface docs, and ACP session work
Chief of Staff	Routing / escalation: split implementation vs delivery tasks, prevent duplicate labor, escalate blockers
All Agents	Consumption: read product knowledge before any product-related decision

Per-Stack Auto-Detection

Fullstack Dev auto-detects tech stack and applies stack-specific scan strategies:

• Laravel/PHP: migrations, route:list, Models, Services, Middleware, Policies, Jobs, Console/Kernel
• React/Vue: components, router, stores, API client, i18n
• Python/Django/FastAPI: models.py, urls.py, views.py, middleware, celery
• General: tree, git log, grep TODO/FIXME, .env.example, Docker, CI, tests

Team Coordination v2

Inbox Protocol v2 (backup channel, status tracking)

Primary dispatch: sessions_spawn (real-time). Inbox is for archival, cross-session handoff, and fallback when spawn is unavailable.

Every inbox message now has a status field:

• pending → received → in-progress → done (or blocked)
• Chief-of-staff monitors timeouts: high>4h, normal>24h pending = intervention
• Blocked >8h = escalation to CEO
• Recipients MUST update status immediately upon reading

Team Dashboard (shared/status/team-dashboard.md)

Chief-of-staff maintains a "live scoreboard" updated every session:

• 🔴 Urgent/Blocked items
• 📊 Per-agent status table (last active, current task, status icon)
• 📬 Unprocessed inbox summary (pending/blocked messages across all inboxes)
• 🔗 Cross-agent task chain tracking (A→B→C with per-step status)
• 📅 Today/Tomorrow focus

Agent 启动顺序（内置于 AGENTS.md）：

1. 确认角色
2. 读 agents/[role]/SOUL.md
3. 读 shared/onboarding.md（项目背景，CEO 填写）
4. 读 shared/status/team-dashboard.md（当前状态）
5. 读 shared/decisions/active.md（仅涉及策略时）
6. 读 shared/inbox/to-[role].md
7. 读 agents/[role]/MEMORY.md（仅需历史上下文时）

Chief-of-Staff as Router

The chief is upgraded from "briefing writer" to "active team router":

• Real-time dispatch: uses sessions_spawn(runtime="subagent") to directly wake agents and assign tasks - this is the primary dispatch method
• Blocker detection: scans all inboxes for overdue messages
• Inbox as backup: writes to inbox only for archival, cross-session handoff, or when agent is unreachable
• Task chain tracking: identifies multi-agent workflows and tracks each step
• Escalation: persistent blockers get flagged to CEO
• Runs 4x/day (morning brief, midday patrol, afternoon patrol, evening brief)

Cron Schedule (10 jobs, up from 7)

Time	Agent	Type	Purpose
07:00	data-analyst	daily	Data pull + feedback scan
08:00	chief-of-staff	announce	Morning: router scan + brief + quality
09:00	growth-lead	daily	GEO/SEO/community
09:00	product-lead	daily (NEW)	Inbox + clarification/PRD + task delegation
10:00	content-chief	daily M-F (was weekly)	Content creation + collaboration
10:00	devops	daily (delivery track)	Inbox + Deep Dive + delivery + QA gate
12:00	chief-of-staff	patrol (NEW)	Router scan only, no brief
15:00	chief-of-staff	patrol (NEW)	Router scan only, no brief
18:00	chief-of-staff	announce	Evening: router scan + summary + next day plan
07:00 M/W/F	intel-analyst	3x/week	Competitor scan

Why These Changes Matter

Before	After	Impact
Inbox = primary dispatch	Inbox = backup + spawn = primary	Real-time dispatch via spawn; inbox for archival only
Chief 2x/day	Chief 4x/day with router role	Blockers caught within hours, not days
Content-chief 1x/week	Daily M-F	Actually produces content
Product-lead no cron	Daily	Knowledge governance happens
No team dashboard	Dashboard every session	All agents know the full picture
No timeout detection	Automatic timeout rules	Nothing falls through cracks

Key Design Decisions

• Shared workspace so qmd indexes everything for all agents
• Real-time spawn dispatch as primary inter-agent communication; inbox as backup for archival and cross-session handoff
• Chief as Router - active coordinator who dispatches via sessions_spawn, detects blockers, and resolves them
• Team Dashboard - single source of truth for team-wide status, maintained by chief every session
• GEO as #1 priority (AI search = blue ocean)
• Fullstack Dev spawns Claude Code via ACP for complex implementation tasks
• DevOps owns delivery and QA gate so implementation and release responsibilities stay separated
• Project Deep Dive gives all agents deep codebase understanding, not just surface-level product overviews

Customization

Edit ROLES array in scripts/deploy.js to add/remove agents. Edit references/soul-templates.md for SOUL.md templates. Edit references/shared-templates.md for shared file templates.