team-lifecycle-v4 — workflow orchestration CLI team-lifecycle-v4, maestro-flow, catlog22, community, workflow orchestration CLI, ai agent skill, ide skills, agent automation, MCP endpoint integration, multi-agent dashboard, software development lifecycle, CLI workflow management

v1.0.0
GitHub

About this Skill

Perfect for AI Agents needing streamlined multi-agent workflow orchestration and MCP endpoint integration for software development lifecycle management. team-lifecycle-v4 is a workflow orchestration CLI with MCP endpoint support, enabling multi-agent software development lifecycle management.

Features

Orchestrate multi-agent workflows via CLI
Supports MCP endpoint integration for seamless communication
Multi-agent dashboard for centralized monitoring and control
Coordinates tasks from specification to review
Utilizes Router for efficient workflow management
Enables role-based access with --role flag

# Core Topics

catlog22 catlog22
[5]
[0]
Updated: 3/18/2026

Quality Score

Top 5%
45
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
> npx killer-skills add catlog22/maestro-flow/team-lifecycle-v4
Supports 19+ Platforms
Cursor
Windsurf
VS Code
Trae
Claude
OpenClaw
+12 more

Agent Capability Analysis

The team-lifecycle-v4 skill by catlog22 is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for workflow orchestration CLI, MCP endpoint integration, multi-agent dashboard.

Ideal Agent Persona

Perfect for AI Agents needing streamlined multi-agent workflow orchestration and MCP endpoint integration for software development lifecycle management.

Core Value

Empowers agents to orchestrate software development from specification to review, leveraging multi-agent dashboards and MCP endpoint integration, while supporting CLI-based workflow management and Router-based architecture.

Capabilities Granted for team-lifecycle-v4

Automating software development workflows from specification to review
Integrating multi-agent dashboards for enhanced collaboration and visibility
Streamlining testing and review processes using MCP endpoint integration

! Prerequisites & Limits

  • Requires CLI-based workflow management
  • Limited to software development lifecycle management
  • Dependent on Router-based architecture
Project
SKILL.md
6.2 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

[No tags]
SKILL.md
Readonly

Team Lifecycle v4

Orchestrate multi-agent software development: specification -> planning -> implementation -> testing -> review.

Architecture

Skill(skill="team-lifecycle-v4", args="task description")
                    |
         SKILL.md (this file) = Router
                    |
     +--------------+--------------+
     |                             |
  no --role flag              --role <name>
     |                             |
  Coordinator                  Worker
  roles/coordinator/role.md    roles/<name>/role.md
     |
     +-- analyze -> dispatch -> spawn -> STOP
                                 |
                    +--------+---+--------+
                    v        v            v
             [team-worker]  ...    [team-supervisor]
              per-task               resident agent
              lifecycle              message-driven
                                     (woken via SendMessage)

Role Registry

RolePathPrefixInner Loop
coordinatorroles/coordinator/role.md----
analystroles/analyst/role.mdRESEARCH-*false
writerroles/writer/role.mdDRAFT-*true
plannerroles/planner/role.mdPLAN-*true
executorroles/executor/role.mdIMPL-*true
testerroles/tester/role.mdTEST-*false
reviewerroles/reviewer/role.mdREVIEW-, QUALITY-, IMPROVE-*false
supervisorroles/supervisor/role.mdCHECKPOINT-*false

Role Router

Parse $ARGUMENTS:

  • Has --role <name> -> Read roles/<name>/role.md, execute Phase 2-4
  • No --role -> Read roles/coordinator/role.md, execute entry router

Shared Constants

  • Session prefix: TLV4
  • Session path: .workflow/.team/TLV4-<slug>-<date>/
  • CLI tools: ccw cli --mode analysis (read-only), ccw cli --mode write (modifications)
  • Message bus: mcp__ccw-tools__team_msg(session_id=<session-id>, ...)

Worker Spawn Template

Coordinator spawns workers using this template:

Agent({
  subagent_type: "team-worker",
  description: "Spawn <role> worker",
  team_name: <team-name>,
  name: "<role>",
  run_in_background: true,
  prompt: `## Role Assignment
role: <role>
role_spec: <project>/.claude/skills/team-lifecycle-v4/roles/<role>/role.md
session: <session-folder>
session_id: <session-id>
team_name: <team-name>
requirement: <task-description>
inner_loop: <true|false>

Read role_spec file to load Phase 2-4 domain instructions.
Execute built-in Phase 1 (task discovery) -> role Phase 2-4 -> built-in Phase 5 (report).`
})

Supervisor Spawn Template

Supervisor is a resident agent (independent from team-worker). Spawned once during session init, woken via SendMessage for each CHECKPOINT task.

Spawn (Phase 2 -- once per session)

Agent({
  subagent_type: "team-supervisor",
  description: "Spawn resident supervisor",
  team_name: <team-name>,
  name: "supervisor",
  run_in_background: true,
  prompt: `## Role Assignment
role: supervisor
role_spec: <project>/.claude/skills/team-lifecycle-v4/roles/supervisor/role.md
session: <session-folder>
session_id: <session-id>
team_name: <team-name>
requirement: <task-description>

Read role_spec file to load checkpoint definitions.
Init: load baseline context, report ready, go idle.
Wake cycle: coordinator sends checkpoint requests via SendMessage.`
})

Wake (handleSpawnNext -- per CHECKPOINT task)

SendMessage({
  type: "message",
  recipient: "supervisor",
  content: `## Checkpoint Request
task_id: <CHECKPOINT-NNN>
scope: [<upstream-task-ids>]
pipeline_progress: <done>/<total> tasks completed`,
  summary: "Checkpoint request: <CHECKPOINT-NNN>"
})

Shutdown (handleComplete)

SendMessage({
  type: "shutdown_request",
  recipient: "supervisor",
  content: "Pipeline complete, shutting down supervisor"
})

User Commands

CommandAction
check / statusView execution status graph
resume / continueAdvance to next step
revise <TASK-ID> [feedback]Revise specific task
feedback <text>Inject feedback for revision
recheckRe-run quality check
improve [dimension]Auto-improve weakest dimension

Completion Action

When pipeline completes, coordinator presents:

AskUserQuestion({
  questions: [{
    question: "Pipeline complete. What would you like to do?",
    header: "Completion",
    multiSelect: false,
    options: [
      { label: "Archive & Clean (Recommended)", description: "Archive session, clean up team" },
      { label: "Keep Active", description: "Keep session for follow-up work" },
      { label: "Export Results", description: "Export deliverables to target directory" }
    ]
  }]
})

Specs Reference

Session Directory

.workflow/.team/TLV4-<slug>-<date>/
+-- team-session.json           # Session state + role registry
+-- spec/                       # Spec phase outputs
+-- plan/                       # Implementation plan + TASK-*.json
+-- artifacts/                  # All deliverables
+-- wisdom/                     # Cross-task knowledge
+-- explorations/               # Shared explore cache
+-- discussions/                # Discuss round records
+-- .msg/                       # Team message bus

Error Handling

ScenarioResolution
Unknown commandError with available command list
Role not foundError with role registry
CLI tool failsWorker fallback to direct implementation
Fast-advance conflictCoordinator reconciles on next callback
Supervisor crashRespawn with recovery: true, auto-rebuilds from existing reports
Supervisor not ready for CHECKPOINTSpawn/respawn supervisor, wait for ready, then wake
Completion action failsDefault to Keep Active

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is team-lifecycle-v4?

Perfect for AI Agents needing streamlined multi-agent workflow orchestration and MCP endpoint integration for software development lifecycle management. team-lifecycle-v4 is a workflow orchestration CLI with MCP endpoint support, enabling multi-agent software development lifecycle management.

How do I install team-lifecycle-v4?

Run the command: npx killer-skills add catlog22/maestro-flow/team-lifecycle-v4. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for team-lifecycle-v4?

Key use cases include: Automating software development workflows from specification to review, Integrating multi-agent dashboards for enhanced collaboration and visibility, Streamlining testing and review processes using MCP endpoint integration.

Which IDEs are compatible with team-lifecycle-v4?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for team-lifecycle-v4?

Requires CLI-based workflow management. Limited to software development lifecycle management. Dependent on Router-based architecture.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add catlog22/maestro-flow/team-lifecycle-v4. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use team-lifecycle-v4 immediately in the current project.

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