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team-execute
Execute an epic using agent teams for parallel task execution. Team lead coordinates, coding agents implement, review agent checkpoints.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Execute an epic using agent teams for parallel task execution. Team lead coordinates, coding agents implement, review agent checkpoints.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | team-execute |
| description | Execute an epic using agent teams for parallel task execution. Team lead coordinates, coding agents implement, review agent checkpoints. |
| user-invocable | true |
| argument-hint | <epic-name> |
Execute an epic using parallel agent teams. For epics with independent tasks that can be worked on simultaneously.
/next-level:execute for sequential execution insteadTeamCreate: team_name = "epic-<name>"
Create tasks for each work item, then spawn agents:
Team Lead (you — the orchestrator):
Coding Agents (one per parallelizable task):
Task tool with:
subagent_type: "coding-agent"
team_name: "epic-<name>"
name: "coder-<task-slug>"
model: "sonnet"
mode: "bypassPermissions"
prompt: <task-specific context — see Coding Agent Prompt Template below>
Review Agent (one, shared across all tasks):
Task tool with:
subagent_type: "checkpoint-reviewer"
team_name: "epic-<name>"
name: "reviewer"
model: "sonnet"
mode: "default"
prompt: <checkpoint context — plan, completed task, test results, git diff>
Each coding agent uses the coding-agent subagent type (see agents/coding-agent.md), which has TDD enforcement and quality hooks built into its frontmatter. The spawn prompt provides task-specific context:
You are implementing task #{ISSUE_NUMBER}: {TASK_TITLE}
## Task Description
{ISSUE_BODY}
## Acceptance Criteria
{ACCEPTANCE_CRITERIA}
## Project Context
- Working directory: {PROJECT_ROOT}
- Test command: {TEST_COMMAND}
- Relevant files: {FILE_LIST}
If team orchestration is unreliable (agents failing, coordination issues):
/next-level:execute for sequential task executionPrimary review entrypoint for plans, diffs, commits, subagent results, and pre-submit checks. Selects quick, standard, or deep mode by scale and risk.
Planner-led repo improvement loops with subagents, review gates, Mermaid DAGs, and workflow-aware commits. Use for improve, refactor, audit, or continue-loop requests.
Triage and apply code review feedback with technical verification. Use when a human, reviewer subagent, CI, or adversarial reviewer returns findings or requested changes.
Heavy review mode normally invoked by request-code-review for risky checkpoints, pre-submit stacks, OpenSpec/cflx acceptance, security-sensitive work, or explicit deep review, audit, or red-team requests.
Post-implementation cleanup for recent changes using parallel reuse, quality, and efficiency reviewers. Use after building a feature or fix, before commit, PR, or submit.
End-of-workflow learning loop for skill-guided work. Use at session/checkpoint closeout to classify GitHub/CodeRabbit/human/CI findings, log missed-review patterns, and propose skill improvements only when evidence justifies them.